CN110163411B

CN110163411B - Operation optimization method for regional comprehensive energy system

Info

Publication number: CN110163411B
Application number: CN201910289848.3A
Authority: CN
Inventors: 王永利; 王玉东; 李芳�; 高铭晨; 马裕泽; 曾鸣; 韩金山; 祝金荣; 郭红珍; 张福伟
Original assignee: North China Electric Power University
Current assignee: North China Electric Power University
Priority date: 2019-04-11
Filing date: 2019-04-11
Publication date: 2022-02-18
Anticipated expiration: 2039-04-11
Also published as: CN110163411A

Abstract

The invention discloses a regional comprehensive energy system operation optimization method, which comprises the following steps: s1, establishing an energy network transmission model; s11, establishing a natural gas network model; s12, establishing a thermal network model; s13, establishing an electric-heat-air coupling network system model; s2, establishing an economic dispatching model of the regional comprehensive energy system; s3, combining the economic dispatching model of the regional integrated energy system with the natural gas network model, the thermodynamic network model and the electric-heat-gas coupling network system model to establish a mixed integer linear programming model of the regional integrated energy system; and S4, obtaining the result of the mixed integer linear programming model through a drosophila optimization algorithm. The invention comprehensively considers the coupling relation among the power system, the thermodynamic system and the natural gas system, can realize the economic advantage of cogeneration, and improves the utilization efficiency of regional comprehensive energy.

Description

Operation optimization method for regional comprehensive energy system

Technical Field

The invention relates to the field of energy, in particular to a regional comprehensive energy system operation optimization method.

Background

The energy and environmental problems are increasingly prominent, and the transformation of human energy consumption modes is promoted. How to improve energy efficiency, reduce environmental pollution and realize sustainable energy development is a common concern at present. The regional integrated energy system (RIE) takes a micro-grid comprising a combined cooling heating and power supply (CCHP) as a core unit, and can uniformly schedule a power grid, natural gas energy (NG) and Distributed Generation (DGs). The RIES can meet the requirements of various loads and improve the economic and environmental benefits of energy systems, which is an important direction for the development of energy systems in the future [1-2 ].

Currently, the planning and operation of the rees are generally performed by taking the CCHP system of a single region as a research object, and selecting equipment and managing energy according to the load characteristics of the region so as to realize region optimization. However, the load characteristics of a particular area tend to be relatively singular, which limits the optimization results of the rees to some extent. By establishing a coupling relationship among the multiple energy supply systems, the CCHP systems of the multiple regions are connected with each other, forming a coordinated complementary RIES of the multiple energy networks. The complementarity of the load characteristics among the areas is fully utilized, so that the uniform planning, uniform design and coordinated operation of the CCHP in a plurality of areas are possible. The purpose of the integrated energy system is to achieve the best overall efficiency. Therefore, establishing an accurate energy network coupling model and an accurate rees economic dispatch model is the key to achieving this goal.

Disclosure of Invention

The invention aims to solve the problems and provides a method for optimizing the operation of a regional comprehensive energy system comprising different energy networks.

In order to achieve the purpose, the technical scheme of the invention is as follows:

a regional comprehensive energy system operation optimization method comprises the following steps:

s1, establishing an energy network transmission model;

s11, establishing a natural gas network model;

s12, establishing a thermal network model;

s13, establishing an electric-heat-air coupling network system model;

s2, establishing an economic dispatching model of the regional comprehensive energy system;

s3, combining the economic dispatching model of the regional integrated energy system with the natural gas network model, the thermodynamic network model and the electric-heat-gas coupling network system model to establish a mixed integer linear programming model of the regional integrated energy system;

and S4, obtaining the result of the mixed integer linear programming model through a drosophila optimization algorithm.

Further, the natural gas network model in the step S11 includes a node pressure model, a natural gas pipeline gas flow model, a pressurizer model, and a node gas flow balance model;

the formula of the node pressure model is as follows:

wherein the content of the first and second substances,

respectively representing the maximum and minimum pressure values of the node i; SGBs are a collection of natural gas network nodes;

the formula of the gas flow model of the natural gas pipeline is as follows:

wherein, fP_pbtIs the flow p of the natural gas pipeline in t years; II type_ibtAnd pi_jbtIs the air pressure at both ends of the pipeline P; phi_pAnd sgn_pRepresenting the gas flow parameters and flow direction of the pipeline P;

and

representing the upper and lower limits of pipeline transportation capacity, and SP representing the set of natural gas pipelines;

the formula of the pressurizer model is as follows:

wherein fc_cbtRepresenting the airflow through the compressor in the b-load region of the year t;

and

is the air pressure at the inlet and outlet; gamma-shaped_CWhich represents the pressure increase ratio of the compressor,

represents the upper limit of the transmission capacity of the pressurizer;

the formula of the node gas flow balance model is as follows:

wherein A, U, C and D respectively represent correlation matrixes of a natural gas pipeline, a pressurizer, a natural gas source, a natural gas load and a natural gas network node; WL_rbtRepresenting the natural gas load at level t when year b is divided.

Further, the thermodynamic network model in step S12 includes node flow balance, pressure loss balance, heat-flow equation, and node temperature fusion;

the equation of the node flow balance is as follows:

wherein q is_ps,k,t/q_pr,k,tRespectively representing the pipeline set of the flow of the kth section water supply/return pipeline at the time t and taking the node i as the terminal point,

respectively, a set of pipes starting at node i, S_ns/S_nrRespectively a water supply/return pipe node set, S_tIs a set of scheduling time periods;

the pressure loss balance equation is:

wherein, Δ p_ps,k,t/Δp_pr,k,tRespectively represents the pressure loss at the t moment of the kth section water supply/return pipeline_pIs a pressure loss parameter;

according to kirchhoff's law, the pressure drop in the pipe is equal to the pressure provided by the water pump:

wherein S is_pu,s；S_pu,rFor a collection of water pumps in a water supply network, p_pu,i,tThe pressure provided for the ith water pump at the moment t;

the heat-flow equation is:

wherein the content of the first and second substances,

respectively showing the heat energy at the inlet/outlet of the k-th water supply pipeline at the moment t,

respectively representing the temperature at an inlet/outlet of a kth section water supply pipeline at the moment t, and C represents the specific heat capacity of water;

the equation for the node temperature fusion is:

wherein, T_ns,i,t/T_nr,i,tRespectively connecting the node temperature of the ith node t moment of the water supply pipeline/the water return pipeline;

assuming that the flow rates of the pipelines form a stable temperature field after being fused at the node i, the temperature of the hot water flowing out of the node is equal to the temperature of the node, namely the temperature of the inlet of the pipeline with the node as a starting point is equal to the temperature of the node:

further, the electric-thermal-air coupling network system model in the step S13 realizes energy conversion, storage and distribution through an energy hub; the energy hub comprises an NG-T hub, an E-T hub and an E-NG-T hub;

the energy centers of the NG-T concentrator are:

wherein, O_TRepresents a heat demand, and may be supplied by various heat sources; lambda [ alpha ]₁Representing the ratio of gas boiler heating to total heat demand; n is a radical of_NGRepresents a natural gas input; delta₁Is natural gas consumed by a gas boiler; eta_GBRepresents the energy conversion efficiency of a gas boiler;

the energy centers of the E-T hub are:

wherein λ is₂Is the ratio of the electric boiler heat supply to the total heat demand; lambda [ alpha ]₃Is the ratio of heat pump supply to total heat demand; n is a radical of_ERepresents an electrical power input; kW; alpha is alpha_EBIs the power proportion input into the electric boiler; alpha is alpha_HPIs the proportion of power input to the heat pump; eta_EBIs the energy conversion efficiency of the electric boiler system; eta_HPIs the energy conversion efficiency of the heat pump system;

the energy centers of the E-NG-T concentrator are:

wherein, delta₂Representing the NG proportion assigned to the E-NG-T hub; eta_CCHP-TIs the thermal efficiency of the E-NG-T hub; eta_CCHP-EIs the electrical efficiency of the E-NG-T hub; beta is a_CCHPIs the proportion of the total power demand of the system that is provided by the E-NG-T hub.

Further, the regional integrated energy system economic dispatch model in step S2 includes economic cost and constraint condition.

Further, the economic cost is calculated by the following formula:

F＝MIN(C_transactione+C_operation+C_storage+C_environment)；

wherein F is the economic cost; c_transactionIs the energy trading cost; c_operationIs the cost of operation; c_storageIs the energy storage cost; c_environmentIs the cost of the environment;

wherein M is a demand response coefficient; m is 1 to represent that the system participates in demand response; m is 0, which means that the system does not participate in the demand response; epsilon_drpIs demand response electricity price; epsilon_fpIs a fixed electricity price; p_e-grid(t) is the exchange power between the rees and the grid; pi is the natural gas price; p_cchp(t) and P_GB(t) output power of CCHP and GB, respectively; eta_cchpAnd η_GBOutput efficiencies of CCHP and GB, respectively; LHV_NGIs the low heating value of natural gas;

wherein, P_kIs the output power of device K;

is the unit power cost of the device;

C_storage＝C_EES+C_TES+C_NGES

wherein, C_EESIs the cost of energy storage; c_TESIs the cost of heat storage; c_NGESIs the cost of gas storage; c_EES-purchaseIs the procurement cost of the EES equipment; y is the number of times the energy storage device is used in the full life cycle; c_EES-capIs the capacity of the energy storage device; p_EES(t)、P_TES(t) and P_NGES(t) power for energy storage, heat storage and gas storage, respectively; alpha and beta are the unit power costs of heat storage and gas storage, respectively;

wherein, P_k(t) is the power of the blowdown source k at time t; zeta_k,jIs the unit emission cost of the pollutant j from the pollution discharge source k; gamma ray_jIs the unit emission price of pollutant j; d1 and D2 are government mandated environmental emission limits; delta_jandλ_jIs a step environment transaction price;

further, the constraint conditions comprise power balance constraint of electricity, heat and gas, external network transmission power constraint, cogeneration operation constraint and energy storage equipment constraint;

the electric, thermal and pneumatic power balance constraints are as follows:

P_e-load(t)+P_EES-char(t)＝P_e-grid(t)+P_WT(t)+P_PV(t)+P_cchp(t)η_g-e+P_EES-dis(t)

P_t-load(t)+P_{TES_char}(t)＝P_cchp(t)η_g-t+P_GB(t)η_GB+P_{TES_dis}(t)

P_{ng_load}(t)+P_{NGES_char}(t)+P_GB(t)+P_cchp(t)＝P_ng-grid(t)+P_{NGES_dis}(t)；

wherein, P_e-load(t)、P_t-load(t) and P_{ng_load}(t) electrical load power, thermal load power and natural gas load power, respectively; p_EES-char(t)、P_TES-char(t) and P_NGES-char(t) the charging power of the electricity storage, heat storage and gas storage systems, respectively; p_EES-dis(t)、P_TES-dis(t) and P_NGES-dis(t) the discharge power of the electricity storage, heat storage and gas storage systems, respectively; p_e-grid(t) and P_ng-grid(t) the exchange power of the regional comprehensive energy system with the power grid and the gas grid respectively; p_WT(t) and P_PV(t) power of WT and PV generator, respectively; eta_g-eAnd η_g-tGas-to-electricity and gas-to-heat efficiencies of cogeneration, respectively;

the external network transmission power constraint comprises a power grid constraint and an air grid constraint; the power grid constraint is as follows:

wherein the content of the first and second substances,

and

respectively the minimum exchange power and the maximum exchange power between the power grid and the regional integrated energy system;

the air network constraints are:

wherein the content of the first and second substances,

is natural gas supply of source a;

and

is the minimum and maximum natural gas supply at source a; theta_bIs the pressure at node b;

and

is the minimum and maximum pressure at node b; sigma_cIs the compression ratio, σ, of the booster station c_cIs equal to theta_bDivided by theta_a；

And

is the minimum and maximum compression ratio of compression station c;

the cogeneration operation constraints are:

wherein, P_cchpn(t) is the rated power of the gas turbine;

and

is the upper and lower limit values of the gas turbine climb rate;

the energy storage device constraints are:

wherein X represents an electricity storage system, a heat storage system or a gas storage system; s_x(t) and S_x(t +1) is the state of the energy storage system at time t and t + 1; p_x-char(t)andP_x-dis(t) represents the power of charging and discharging of the energy storage system, respectively; eta_x-charAnd η_x-disRespectively the charge-discharge efficiency of the energy storage system;

and

respectively is the upper limit and the lower limit of the energy storage state of the energy storage system; s_x,24And S_x,1Energy storage states which are respectively the starting time and the ending time of the scheduling period;

and

representing the upper limit and the lower limit of the energy charging power of the energy storage system;

and

the upper limit value and the lower limit value of the discharging power of the energy storage system.

Compared with the prior art, the invention has the advantages and positive effects that:

the invention establishes an energy network transmission model of an electricity-heat-natural gas network and an economic dispatching model of a multi-energy network regional comprehensive energy system. On one hand, according to the characteristics of the RIES energy network, a regional comprehensive energy system accurate speaking scheduling model is established. Firstly, a natural gas network model containing node air pressure, pipeline air flow and a voltage stabilizer is established according to the composition of a natural gas network in a regional integrated energy system and a node air flow balance equation so as to describe the operation condition of the regional natural gas network. Secondly, a general model of thermal network energy transfer is established based on the basic principles of heat transfer and the basic principles of management networks. The model takes heat flow and temperature as optimization variables, and can describe the state of the heat network more accurately. Third, an electro-thermal-air coupling network model of different energy networks based on multiple energy hubs is established. The model created consists of three different energy hubs that are used to integrate the different energy networks and loads of the area, depending on the energy balance of the system.

On the other hand, a mixed integer linear programming model taking the optimization of the energy hub as a core is established in consideration of the complexity and the randomness of the operation optimization of the comprehensive energy system. The goal of this model is to minimize the overall cost of system operation and take into account the constraints of system energy balance, equipment operation and energy network security. Meanwhile, a coordination optimization strategy with an energy hub is established according to the established electric-heat-air coupling network model and the regional comprehensive energy system precise scheduling model. Then, optimization simulation is carried out by taking a commercial building integrated energy system as an example, and the effectiveness and the accuracy of the model established by the method are verified.

The invention comprises the operation optimization of the RIES of different energy networks (electricity-heat-natural gas), and the established model considers the economic operation and the carbon trading mechanism of the system. The RIES combines natural gas, electric energy, heat energy and other energy sources together through an advanced physical information technology and an innovative management mode, and realizes coordinated planning, optimized operation, cooperative management and complementation among different energy subsystems. In addition, the comprehensive energy technology can effectively improve the energy efficiency and promote the sustainable development of energy while meeting the diversified energy requirements of the system.

Drawings

In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to these drawings without creative efforts.

FIG. 1 is a schematic illustration of a typical RIES;

FIG. 2 is a schematic diagram of a model compressor consuming natural gas;

FIG. 3 is a diagram of a RIES zone thermal network architecture;

FIG. 4 is a diagram of the coupling pattern of an energy hub based "electro-thermal-air grid";

FIG. 5 is a schematic diagram of an optimization strategy based on interrelationships between models;

FIG. 6 is a diagram showing the process of fruit fly searching for food;

FIG. 7 is a flow chart of the FOA-based RIES economic operation optimization;

FIG. 8 is a RIES network topology;

FIG. 9 is a typical winter daily thermal load and electrical load curve;

FIG. 10 is a typical winter daily electricity price and natural gas price prediction curve;

FIG. 11 is a graph of efficiency of a gas turbine in a regional integrated energy system;

FIG. 12 is a graph of output power prediction for typical daily DGs in winter;

FIG. 13 is a carbon trading curve for a regional energy complex in simulation;

fig. 14 is a schematic diagram of an optimal scheduling result of power in mode 1;

FIG. 15 is a schematic illustration of the gas turbine operating efficiency in mode 1;

fig. 16 is a schematic diagram of an optimal scheduling result of power in mode 2;

FIG. 17 is a schematic illustration of the gas turbine operating efficiency in mode 2;

fig. 18 is a schematic diagram of an optimal scheduling result of power in mode 3;

FIG. 19 is a schematic illustration of the gas turbine operating efficiency in mode 3;

FIG. 20 is a graph showing the thermal power optimization results for the system in mode 1;

FIG. 21 is a schematic diagram showing the optimization results of the heat medium flow and the heat transfer power of the regional integrated energy system;

FIG. 22 is a graph showing the thermal power optimization results of the system in mode 2;

FIG. 23 is a schematic diagram showing the optimization results of the flow rate of the system heating medium and the heat transfer power of the pipe section;

FIG. 24 is a diagram illustrating the thermal power optimization results of the system in mode 3;

FIG. 25 is a graph showing the optimization results of the flow rate of the heat medium and the heat transfer power in mode 3;

FIG. 26 is a diagram illustrating the optimal scheduling results for NG power in mode 1;

FIG. 27 is a diagram illustrating the result of the best scheduling of NG power in mode 2;

FIG. 28 is a diagram illustrating the optimal scheduling results for NG power in mode 3;

FIG. 29 is a comparison of power exchange in different modes;

FIG. 30 pollutant emissions per hour in mode 1;

FIG. 31 pollutant emissions per hour in mode 2;

FIG. 32 pollutant emissions per hour in mode 3;

FIG. 33 is a graphical representation of carbon emission costs at different emission limit adjustment ratios.

Detailed Description

The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived from the embodiments of the present invention by a person skilled in the art without any creative effort, should be included in the protection scope of the present invention.

As shown in fig. 1 to 33, a Regional Integrated Energy System (RIES) can provide users with energy of cold, heat, electricity, gas, etc., and thus the RIES has various devices and complicated operation characteristics [26,27 ]. Depending on the function of the device, the devices in the system can be divided into four types: CCHP equipment, energy storage equipment, refrigeration equipment and auxiliary equipment. In addition to traditional power generation equipment, renewable energy power generation is also a feature of the rees. At present, distributed Photovoltaic (PV) and wind generating sets (WT) have become the key of rees renewable energy power generation due to their characteristics of green, clean, convenient installation and mature technology. The energy supply network of the rees mainly includes an electric grid (E-grid), a heat grid (T-grid) and a natural gas grid (NG-grid). The three energy networks are connected by an energy conversion device. The energy conversion apparatus includes an electric heating apparatus, a gas turbine, and a gas boiler. Meanwhile, the user side of the rees is equipped with Electrical Energy Storage (EES), Thermal Energy Storage (TES) and gas storage. FIG. 1 is a schematic representation of a typical RIES.

As shown in fig. 1, the interconnection of the power grid (E-grid), the heat grid (T-grid) and the natural gas grid (NG-grid) in the system is realized by an energy hub. In RIES, the energy hub is primarily an energy conversion device. Energy conversion devices can be divided into three categories depending on the function of the energy hub.

(1) Electrothermal conversion device (E-T): electric Boiler (EB), Air Conditioning (AC).

(2) Natural gas-heat conversion device (NG-T): gas fired boiler (GB).

(3) Electric heating natural gas conversion equipment (E-T-NG): a micro Gas Turbine (GT), a Waste Heat Boiler (WHB), and a Refrigerator (REF).

Natural gas network modeling

Typical natural gas networks in rees include gas sources, pipelines, compressors, gas storage nodes, natural gas loads, and the like. The gas network is provided by one or more gas sources and is transmitted to gas storage nodes, load nodes or is connected with other energy systems through coupling points through a natural gas pipeline through a high-medium low-pressure network. The natural gas is mainly delivered to users through natural gas pipelines after being mined and processed. However, due to the friction of the natural gas stream with the duct walls during flow, the pressure will gradually decrease after a certain transport distance. To ensure that the natural gas can be normally transported to the load side, a pressurizer should be installed to increase the pressure of the natural gas system. Fig. 2 is a model of a compressor consuming natural gas. Components in the natural gas network are modeled as follows.

(1) Nodal pressure model

The air pressure of each node in the NG network must be within a safe and reasonable range, and the mathematical expression of the air pressure is as shown in the formula (1).

In the formula (I), the compound is shown in the specification,

respectively representing the maximum and minimum pressure values of the node i; the SGB is a collection of natural gas network nodes.

(2) Airflow model of natural gas pipeline

A Weymouth steady-state energy flow model is used for describing the relation between gas flow and pressure at two ends, and the specific expression is as follows [29 ]:

in the formula, fP_pbtThe flow rate p, m3/s of the natural gas pipeline in t years; II type_ibtAnd pi_jbtIs the air pressure, kPa, at both ends of the pipeline P; phi_pandsgn_pRepresenting the gas flow parameters and flow direction of the pipeline P;

and

representing the upper and lower limits of the pipeline transport capacity, and SP represents the collection of natural gas pipelines.

(3) Pressurizer model

The original pressurizer model is a non-convex non-linear expression describing the relationship between the pressurization ratio and the energy consumption of the pressurizer. Since the focus here is on studying the expansion of natural gas pipelines and the pressurizer (electricity or natural gas) consumes very little energy, the model of the pressurizer is simplified, i.e. the energy consumed by the pressurizer. Neglecting during operation and maintaining the pressurization relationship between the inlet and outlet of the pressurizer, as well as the delivery capacity limitations of the pressurizer [30 ].

In the formula, fc_cbtRepresents the gas flow through compressor C in the b-load region of t years;

is the air pressure at the inlet and outlet; gamma-shaped_CRepresents the pressure increase ratio of the compressor C,

representing the upper limit of the transfer capacity of the pressurizer.

(4) Nodal gas flow balance equation

In the formula, A, U and CandD respectively represent correlation matrixes of a natural gas pipeline, a pressurizer, a natural gas source, a natural gas load and a natural gas network node; WL_rbtRepresenting the natural gas load at level t when year b is divided.

Heat supply network modeling

As shown in fig. 3, the district heating network is generally divided into a main heating network and a secondary heating network, and the two parts are connected by a secondary heat exchanger, and include a water supply pipe and a water return pipe. The heat energy generated by the heating unit is transferred to the main heat network through the main heat exchanger, and hot water is distributed to each secondary heat exchanger and then distributed to the users through each secondary network. The cooled working fluid enters a return pipe and returns to form a circulation. Although there is thermal interaction between the main network and the auxiliary network, the working fluids in the main network and the auxiliary network are independent of each other.

There are two modes of operation for a district heating network: quality supervision and quantity supervision. The operation mode of the heat-saving network is a quality adjusting mode. The quality adjusting mode can not only ensure the stability of the hydraulic working condition of the system, but also facilitate the actual operation.

(1) Node flow balancing

According to kirchhoff's law, the flow of hot water flowing into a certain node is equal to the flow of hot water flowing out of the node:

equations (1) and (2) respectively represent the node flow balance in the water supply pipeline and the water return pipeline. In the formula q_ps,k,t/q_pr,k,tAnd respectively shows the flow rate (Kg/h) of the water supply/return pipeline at the kth stage at the time t. Respectively, a pipeline set with the node i as a terminal point,

respectively, a set of pipes starting at node i, S_ns/S_nrRespectively a water supply/return pipe node set, S_tIs a set of scheduling time periods.

(2) Balance of pressure loss

The pipe pressure loss is proportional to the square of the flow into the pipe:

in the formula,. DELTA.p_ps,k,t/Δp_pr,k,tRespectively represents the pressure loss (m), mu at the time t of the kth section water supply/return pipeline_pIs the pressure loss parameter (m/(kg)²/h²))。

in the formula, S_pu,s；S_pu,rFor a collection of water pumps in a water supply network, p_pu,i,tThe pressure (m) provided for the ith water pump at time t.

(3) Heat-flow equation

The heat energy carried by the hot water medium is proportional to the hot water flow and the hot water temperature:

the relations among the hot water flow, the temperature and the heat energy are described by the formulas (6) and (7). Because there is a delay and heat loss in the hot water delivery, the outlet temperature of the pipe is slightly lower than the inlet temperature of the pipe, and the temperature change is later than the inlet of the pipe, so that each pipe has two temperature variables and two heat variables respectively representing the inlet and the outlet. The heating system adopts a quality regulation mode, namely the flow of hot water is kept constant, so that one pipeline only has one flow variable.

In the formula (I), the compound is shown in the specification,

respectively represents the heat energy (kW) at the inlet/outlet of the k-th water supply pipeline at the moment t,

respectively representing the temperature (DEG C) at an inlet/outlet of a k-th section water supply pipeline at the time t, C represents the specific heat capacity of water, and 4.6185KJ/(kg DEG C) is taken; λ represents a unit transformation parameter and is 3600.

(4) Nodal temperature fusion

According to the first law of thermodynamics, the heat energy flowing into a certain node is equal to the heat energy flowing out of a certain node. Therefore, the nodal temperature fusion can be described as:

in the formula, T_ns,i,t/T_nr,i,tRespectively connected with the node temperature (DEG C) of the ith node t moment of the water supply pipeline/the water return pipeline. Assuming that the flow rates of the pipelines form a stable temperature field after being fused at the node i, the temperature of the hot water flowing out of the node is equal to the temperature of the node, namely the temperature of the inlet of the pipeline with the node as a starting point is equal to the temperature of the node:

modeling of electro-thermal-gas coupled systems

In the rees of the "electric-thermal-air grid", an Energy Hub (EH) is the key to connecting three energy networks. The energy coupling link is described by adopting an energy concentration model, three energy networks are coupled through a system heating module, and an electric network, a heat network and a gas network are coupled together through different devices [35,36 ]. Fig. 4 is a diagram of the coupling pattern of an "electro-thermal-air grid" based on an energy hub.

The energy hub may implement energy conversion, storage and distribution, and the model may describe the coupling relationships between electricity, gas, cold and heat. It connects energy input and output through a coupling matrix, the relationship between the two meeting the following requirements:

O＝MN (18)

(1) NG-T concentrator

The NG-T energy concentrator is mainly a gas-heat conversion device and mainly comprises a Gas Boiler (GB). According to the relationship expressed by equation (18), the energy center can be written in the form:

in the formula, O_TRepresenting heat demand, kW; o is_TCan be supplied by various heat sources; lambda [ alpha ]₁Representing the ratio of gas boiler heating to total heat demand; n is a radical of_NGRepresenting natural gas input, m 3; delta₁Is natural gas consumed by a gas boiler; eta_GBRepresenting the energy conversion efficiency of the gas boiler.

(2) E-T concentrator

An E-T energy hub is an "electric heat" conversion device, mainly comprising an Electric Boiler (EB) and a Heat Pump (HP). The EB system converts electric energy into heat energy, and the heat pump system extracts heat energy from air, soil or a water source by using the electric energy for heating.

According to the relationship represented by equation (18), the E-T energy hub can be written as follows:

in the formula, λ₂Is the ratio of the electric boiler heat supply to the total heat demand; lambda [ alpha ]₃Is the ratio of heat pump supply to total heat demand; n is a radical of_ERepresenting power input, kW; alpha is alpha_EBIs to be transportedThe power proportion of the electric boiler; alpha is alpha_HPIs the proportion of power input to the heat pump; eta_EBIs the energy conversion efficiency of the electric boiler system; eta_HPIs the energy conversion efficiency of the heat pump system.

(3) E-NG-T concentrator

The E-NG-T energy hub is mainly an 'electricity-gas-heat' conversion device named as CCHP. The natural gas system provides energy to the power system through the CCHP, which plays a supporting role in its voltage level. Meanwhile, the power supply device can generate certain heat when power is supplied.

According to the relationship expressed by equation (18), the energy center can be written as follows:

in the formula, delta₂Represents the NG proportion allocated to CCHP; eta_CCHP-TIs the thermal efficiency of CCHP; eta_CCHP-EIs the electrical efficiency of CCHP; beta is a_CCHPIs the proportion of the power provided by the CCHP to the total power demand of the system.

Optimized scheduling model based on EH energy hub

Model building

The equipment in the RIES system is various and complex, and the characteristics of the equipment can change along with the change of operating conditions, thereby increasing the difficulty of establishing a system model. This section establishes an rines economic dispatch model. The model aims at the lowest economic cost, comprehensively considers constraint conditions such as power balance and the like, reasonably arranges the output of each device and realizes the economic operation of the system. A mathematical model of the RIES operation optimization problem based on the network coupling system is described in detail, including economic cost and constraints.

Optimizing an objective

Economic efficiency is a primary goal for system operation. Operation of the rees should reduce the economic cost as much as possible to make the system operate economically better. In order to furthest mine the economic advantages of the system, a RIES economic dispatching model is established in the section, and on the premise of meeting the load requirements of users, the output of each device is effectively arranged, so that the system can run economically. The specific mathematical model is as follows.

F＝MIN(C_transactione+C_operation+C_storage+C_environment) (22)

In the formula, C_transactionIs the energy trading cost; c_operationIs the cost of operation; c_storageIs the energy storage cost; c_environmentIs an environmental cost.

Wherein M is a demand response coefficient; m is 1 to represent that the system participates in demand response; m is 0, which means that the system does not participate in the demand response; epsilon_drpIs demand response electricity price; epsilon_fpIs a fixed electricity price; p_e-grid(t) is the exchange power between the rees and the grid, kW; pi is the natural gas price; p_cchp(t) and P_GB(t) output power, kW, for CCHP and GB, respectively; eta_cchpAnd η_GBOutput efficiency,%, of CCHP and GB, respectively; LHV_NGIs the lower heating value of natural gas, kWh/m3.

In the formula, P_kIs the output power of the equipment K, kW;

is the unit power cost, yuan/kW, of the equipment.

C_storage＝C_EES+C_TES+C_NGES (25)

In the formula, C_EESIs the cost of energy storage; c_TESIs the cost of heat storage; c_NGESIs the cost of gas storage; c_EES-purchaseIs the procurement cost of the EES equipment; y is the number of times the energy storage device is used in the full life cycle; c_EES-capIs the capacity of the energy storage device; p_EES(t),P_TES(t) and P_NGES(t) power for energy storage, heat storage and gas storage, kW, respectively; alpha and beta are the unit power costs of heat storage and gas storage, yuan/kW, respectively.

The staging environmental trade refers to the environmental cost equal to the emission source of each emitted pollutant multiplied by its environmental price when the total amount of pollutant emissions does not exceed the basic emission limit stipulated by the government, and the environmental cost also includes the staging environmental trade cost when the total amount of pollutant emissions exceeds the basic emission limit. It is assumed herein that the power purchased from the grid is entirely coal-fired to generate electricity. Thus, the RIES carbon trading benchmark emission limits are determined by the electrical power purchase, CCHP contribution, and GB contribution:

in the formula, psi is the unit power discharge capacity, kg/kW; p_e-purchase(t) is the amount of electricity purchased from the grid, kW.

In the formula, P_k(t) is the power of the blowdown source k at time t, kW; zeta_k,jIs the unit emission cost, kg/kWh, of pollutant j from the pollution discharge source k; gamma ray_jIs the unit discharge price of pollutant j, yuan/kg; d1 and D2 are government mandated environmental emission limits; delta_jandλ_jIs the trading price in the step environment, yuan/kg.

Constraint conditions

The RIES improves the energy utilization efficiency through the coordinated use of various energy sources and the coupled operation of different devices. Thus, the rees operation optimization is necessarily a complex problem of multivariable, multi-constraint. The RIES operation optimization can be performed only on the premise of meeting set constraints, including electricity, heat and gas power balance constraints, external grid transmission power constraints, cogeneration operation constraints and energy storage equipment constraints.

(1) Electric, thermal and pneumatic power balance constraint

The balance of electricity, heat and gas power is the first prerequisite for optimizing operation. The system operation can be optimized only when the energy supply of the system meets the energy demand.

P_e-load(t)+P_EES-char(t)＝P_e-grid(t)+P_WT(t)+P_PV(t)+P_cchp(t)η_g-e+P_EES-dis(t) (31)

P_t-load(t)+P_{TES_char}(t)＝P_cchp(t)η_g-t+P_GB(t)η_GB+P_{TES_dis}(t) (32)

P_{ng_load}(t)+P_{NGES_char}(t)+P_GB(t)+P_cchp(t)＝P_ng-grid(t)+P_{NGES_dis}(t) (33)

In the formula, P_e-load(t)，P_t-load(t) and P_{ng_load}(t) electrical load power, thermal load power and natural gas load power, respectively; p_EES-char(t)，P_TES-char(t) and P_NGES-char(t) the charging power of the electricity storage, heat storage and gas storage systems, respectively; p_EES-dis(t)，P_TES-dis(t) and P_NGES-dis(t) the discharge power of the electricity storage, heat storage and gas storage systems, respectively; p_e-grid(t) and P_ng-grid(t) the exchange power of the RIES and the power grid and the gas grid respectively; p_WT(t) and P_PV(t) power of WT and PV generator, respectively; eta_g-eAnd η_g-tThe gas-to-electricity and gas-to-heat efficiencies of cogeneration, respectively.

(2) Outer network transmission power constraints

In the rees, the exchange power of the system with the external network must be controlled within a certain range in consideration of the safety of the energy transmission pipeline and the economy of the system cost [40,41].

Grid constraints

In the rees, the transmission power of the system and the grid cannot exceed the maximum power that the pipeline can withstand, taking into account safety considerations.

In the formula (I), the compound is shown in the specification,

and

respectively the minimum and maximum exchange power between the grid and the rees, kW.

2) Air network restraint

The gas network constraints comprise gas source point natural gas supply constraints, node pressure constraints and booster station compression ratio constraints [42 ].

In the formula (I), the compound is shown in the specification,

is natural gas supply of source a;

and

and

and

Are the minimum and maximum compression ratios of compression station c.

(3) Cogeneration operation constraints

CCHP mainly meets rated power and climb rate constraints. Its output power must not exceed the rated power. The power rating and ramp rate of CCHP are constrained as follows.

0≤P_cchp(t)≤P_cchpn(t) (36)

In the formula, P_cchpn(t) is the rated power of the gas turbine;

and

are the upper and lower limits of the gas turbine climb rate. .

(4) Energy storage device restraint

In order to reduce the wear and prolong the life of the energy storage device, it is necessary to limit the depth of discharge of the energy storage device. Therefore, the change in the state of charge of the stored energy and the charge-discharge power are constrained as follows.

In the formula, X represents an electricity storage system, a heat storage system or a gas storage system; s_x(t) and S_x(t +1) is the energy storage system at times t and t +1The state of (1); p_x-char(t)andP_x-dis(t) represents the power of charging and discharging of the energy storage system, respectively; eta_x-charAnd η_x-disRespectively the charge-discharge efficiency of the energy storage system;

and

and

and

Optimization strategy

The energy network is the connection line between the energy device and the user. Meanwhile, the energy hub is the key for coupling between different energy networks. The optimal scheduling based on the EH is the optimization of a multi-network cooperative system, and the core of the optimization is an energy hub connected with each energy network. Therefore, energy hub optimization is the core of the optimization strategy. Firstly, the energy concentrator is optimized, so that the total operation cost of the system is minimized, and the economic operation of each energy module of the system is realized. And secondly, optimizing the states of all energy networks of the system according to the characteristics of energy balance and energy flow transmission of the system. The optimization result mainly comprises three parts, namely the running state of each energy hub, the running state of a natural gas network and the running state of a thermal network. FIG. 5 shows an optimization strategy based on the interrelationships between models.

The RIES optimization strategy containing different energy networks comprises the following steps:

and 1, optimizing the energy hub. And on the basis of system operation, the concentrator is optimally designed. And establishing an optimization model aiming at minimizing the total operation cost of the system according to the function of the energy concentrator in the system. From the viewpoint of energy balance, the optimization can realize the coordination optimization among all energy hubs of the system, and the economic operation of the system is realized under the condition of meeting the load requirement of the system.

And 2, optimizing a heat supply network. The heat supply network is connected with the output end of the energy concentrator and is a carrier of the heat energy conversion system. The heat energy generated by the energy concentrator is sent into the heat network, so that the heat medium in the heat network has certain energy. The heat medium is conveyed to the user through the pipeline to provide the user with required heat load. Therefore, the optimization of the heat network is closely related to the optimization result of the energy hub. Through a heat power balance system and a heat supply network energy transmission principle, the speed and the transmission power of the heat supply network can be optimized and utilized on the basis of heat supply network model building, and the energy center of the result is optimized to reflect the operation state of the heat supply network.

Section 3: and (5) optimizing the air network. The natural gas network is connected with the input end of the energy hub and is a carrier of the natural gas power conversion system. The energy hub receives natural gas from the natural gas network to fuel equipment in the energy hub. The natural gas network accomplishes this transmission process through pipelines and pressure equipment. The operating conditions of the natural gas network are therefore reflected here primarily by the plant power and the loading of the natural gas in the natural gas network. By optimizing the power of the gas equipment used by the energy concentrator, the gas demand of the natural gas network can be obtained. On the basis, the power of related equipment and the natural gas conveying capacity can be calculated through the established gas network model and the equilibrium equation.

And 4, optimizing the result. And (4) calculating the 1 st to 4 th parts to obtain the running state of each energy concentrator, the running state of the natural gas network and the running state of the heating power network.

Algorithm

The rees economic dispatch model based on different energy networks involves not only a large number of variables and constraints, but also coupling between the networks. Therefore, the RIES operation optimization must be a complex mixed integer nonlinear programming (MINLP) problem. Based on the above analysis, the rees operation in the power-thermal network mode is a typical mixed integer programming problem involving many non-linear, discrete, random, and uncertain factors, and therefore a mixed integer linear programming model of the regional integrated energy system is established, and in order to obtain an optimal solution and an optimal output of the model, a drosophila optimization algorithm (FOA) is selected herein to solve the mixed integer linear programming model. FIG. 6 shows the process of fruit fly searching for food.

FIG. 7 shows a FOA-based RIES economic operations optimization procedure.

According to the characteristics of the FOA algorithm, parameters required to be input in the simulation process mainly comprise system operation parameters and algorithm calculation parameters. Firstly, the operation parameters, different loads, different energy prices, the maximum utilization hours of equipment, pollutant discharge absorption rate and various cost coefficients of each energy unit in the system are input as basic data of simulation. Secondly, algorithm parameters are set, the population size is set to be 500, and the maximum iteration number is set to be 100. After all the parameters are set, the simulation process is calculated according to the flow chart shown in fig. 5, and the result output mainly comprises a target function value, equipment and a pipe network optimization result.

Analysis of simulation results

Data of

A typical RIES from Tianjin is used as the subject of the study. The rated voltage of the power distribution network is 10 kV. The simulation was run for 24 hours, with an optimal adjustment time of 1 hour. The topology of the system is shown in fig. 8, where GP, TP and EP represent nodes of the natural gas, thermal and power networks, respectively. Fig. 9 shows typical thermal and electrical load curves for a winter one-day system. The electricity price and natural gas price prediction curves are shown in fig. 10.

The system capacity is shown in table 2. The parameters of the thermal network are shown in table 3. In the simulation, the operating cost of the heat grid was calculated using the thermal energy transferred by each pipe segment. Fig. 12 shows an output prediction curve of distributed power generation.

Tab.2 System Capacity

Shorthand writing

CCHP

TES

EES

EB

WT

PV

Of significance

Combined cooling heating and power supply

Thermal energy storage

Electrical energy storage

Electric boiler

Fan blower

Photovoltaic system

Capacity/kW

3500

500

3000

2000

1000

Tab.3 Heat supply network parameters

Pipe section	Length (Km)	Radius (m)	Maximum flow velocity (m/s)	TR	Rate of heat loss
						TP9-TP5	0.8	0.4	3.7	20	0.0057
TP8-TP1	1.5	0.4	3.7	20	0.0062
						TP6-TP5	1.0	0.3	3.5	20	0.0056
TP4-TP1	1.1	0.3	3.5	20	0.0064

In the simulation, time of use electricity prices (TOU) were used to purchase electricity from the grid at 0.805 yuan/kWh and natural gas prices at a fixed price (3.25 yuan/m 3)

FIG. 11 shows an efficiency curve for a gas turbine in the system. The operating efficiency of a gas turbine is directly related to the load factor of the system. The load factor depends on the type of turbine. The efficiency of gas turbines is typically in the range of 30-80%. However, when the load in the system is small, the efficiency of the gas turbine will quickly drop, directly affecting the economics of the system operation.

Fig. 13 shows a carbon transaction curve for the system in the simulation. The initial emission limit for carbon emissions herein is related to the power generation of the system, and excess or deficiency in carbon emissions can be traded. Herein, the pollutant emission cost includes a basic emission cost and a carbon trading cost. Pollutants emitted within carbon emission limits require only an emission fee, and pollutants exceeding the specified carbon emission require carbon emission credits to be purchased from the market.

To verify the effectiveness of the method, the following three operating modes were constructed to analyze the total operating cost and operating state of the rees. Due to the difference of the combined cooling heating and power supply operation modes, the three operation modes of the heating equipment and the heating network pipe section have difference. Table 4 shows the system scheduling units in different modes.

Tab.4 system scheduling unit under different modes

In these three modes, the units participating in power scheduling are the same, including CCHP, PV, WT, Grid, and EES. Wherein, PV and WT keep the rated operation state all the time, do not participate in the optimization of the system operation. In addition, there are differences in the operating states of the CCHP in the three modes, which results in changes in the operating states of other scheduling units in the system.

(1) Operating state of the grid in mode 1

In the "FPL" mode (M1), the result of power optimization is shown in fig. 14. The power dispatching unit of the system comprises cogeneration, EES and a power grid.

In fig. 14, a positive power output of the grid indicates that the system is purchasing power from the grid, a negative power indicates that the system is selling power to the grid, an EES positive indicates that the battery is discharging, and a negative indicates that the battery is charging. CCHP, EES and DG participate in system scheduling in FPL mode. The system generates power to meet the demand of the customer during each period of time and sells the remaining power to the grid. In fig. 11, the output of CCHP is stable and at a low level. The average output of CCHP is about 500 kW.

According to the EES power curve in fig. 14, the EES performs charging and discharging under the power rate guidance on the premise of participating in the system economic dispatch, that is, charging and discharging in the low valley period and discharging in the high peak period of the power rate, and determines the charging and discharging state at the current moment according to the power rate and the load condition in the normal period, thereby effectively reducing the peak-valley difference between the power and the thermal load.

In the "FPL" mode (M1), the optimization of the gas turbine operating efficiency results are shown in FIG. 15. As can be seen from fig. 15, the output curve of CCHP in mode 1 substantially coincides with the efficiency curve of the gas turbine and the load factor curve of the gas turbine. As shown in fig. 11, the efficiency of the gas engine typically exceeds 0.8 when the load factor of the system exceeds 0.4. However, the gas turbine participates in grid scheduling, and its output is constrained in mode 1 by its own unit constraints and system scheduling constraints. In this case, the efficiency values of the gas engine are relatively stable, with the load rate of the system exceeding 0.4 83% of the day and the efficiency exceeding 0 only 8.3% of the day. This indicates that the efficiency of the gas engine is relatively good in this mode, but not yet fully exploited.

(2) Operating state of the grid in mode 2

In the "FHL" mode (M2), the result of the power optimization is shown in FIG. 16. The system power scheduling unit includes CCHP, EES and Grid. In mode 2, the gas turbine is operated according to the "FHP" strategy. The waste heat boiler recovers the waste heat of the gas turbine after power generation, and the system operates in a mode that the waste heat recovered by the waste heat boiler meets the heat load of each time period. In this case, the operation of the gas turbine is not constrained by grid scheduling, but by the balancing of thermal loads in the system. That is, the output curve of the gas turbine should follow the thermal load curve of the system and remain unchanged at any time.

According to the heat load curve in fig. 9, the peak period of the heat load is concentrated at 11:00-22: 00. Thus, the average output of CCHP during this period is up to 2300kW, while at 23:00-24:00 and 1:00-10:00, the heat load demand is lower. Therefore, the average output of CCHP is only 500Kw, which makes the output of CCHP in mode 2 greatly fluctuate. In mode 2, the gas turbine is not directly involved in grid scheduling because its main task is to satisfy the thermal power balance of the thermal network, and the power generation of the gas turbine is a fixed value. Therefore, in this case, DG, EES, and GRID are the main power scheduling modes. The DG generates power that first meets the load requirements of the system. Only when the power balance of the system is met can the remaining power be sold to the grid. Thus, in this case, the system is engaged in demand response to a low degree.

In the "FHL" mode (M2), the optimization results of the gas turbine operation efficiency are shown in FIG. 17. As can be seen from fig. 17, the output curve of CCHP in mode 1 substantially coincides with the efficiency curve of the gas turbine and the load factor curve of the gas turbine. The efficiency of the gas engine fluctuates significantly in mode 2 as compared with fig. 15. The efficiency of the gas turbine is over 0.8 at 11:00-22:00, and the operation is good. However, the efficiency of gas turbines is below 0.4 at 23:00-24:00 and 1:00-10:00, which is very low. The main reason for this phenomenon is that the operation mode of the gas engine has changed significantly. In the "FHL" mode, the output time of the gas turbine is consistent with the heat demand of the system. Due to the nature of the system's heat demand, the system's heat demand is lower at 23:00-24:00 and 1:00-10:00, which results in the gas turbine's output being maintained at a lower level and the gas turbine's utilization being lower during this period.

(3) Operating state of the grid in mode 3

In the "optimal scheduling" mode (M3), the result of power optimization is shown in fig. 18. The system power scheduling unit includes CCHP, EES, and GRID. In mode 3, since TES participates in thermal scheduling, the FHL mode of the system is decoupled and the power output limit of CCHP is removed. Meanwhile, the power supply and heat supply cost is optimized, and the optimal power output of each unit under the operation constraint of the RIES can be obtained, namely the optimal scheduling of electric power and heat is realized.

The best scheduling mode for mode 3 is to use a "FPL" mode based on fixed power of heat during the night and increase the output of the gas turbine during the night, thereby increasing the economic efficiency of the system. Because of the connection between the system and the external power grid, the surplus power generated by increasing the output of the gas turbine during the night can be sold to the external power grid for profit. When the output of the gas turbine increases at night, the output of CCHP at 23:00 to 10:00 in fig. 15 is 50kW higher than that in mode 2. Since the TES participates in the system heat distribution, it can store excess heat generated by increasing the output of the gas turbine during the night and release heat during the day, thereby reducing the output of the gas turbine during the day. Therefore, the output of CCHP at 11:00 ~ 22 points is 350kW lower than that of CCHP in mode 2.

The power scheduling unit in mode 3 includes CCHP, Grid, and EES, and the CCHP system operates by a joint scheduling method. In mode 2, the gas turbine is operated less efficiently because the thermal load requirements of the nighttime system are less. Mode 3 is an improvement over mode 2. Decoupling and optimizing the running mode of the gas turbine and the coordination of TES at 23:00-24:00 and 1:00-10: 00. The TES participates in the optimization of the operation of the gas turbine, the output level of the gas turbine in the period is improved, and the operation efficiency of the gas turbine in the period with low heat load requirement is improved. Compared with the operation effect of the gas turbines in the

modes

2 and 3, the efficiency of the gas turbine is improved by about 50% from 23:00 to 10: 00. Meanwhile, at 11:00-22:00, due to the participation of TES, the output level of the gas turbine is lower than that of the mode 2 on the premise of meeting the heat requirement of the system.

Because the running modes of the combined supply of cold, heat and electricity are different, the units participating in heat dispatching in the three modes are also different. All three modes contain a CCHP train, and in addition, mode 1 is equipped with EB and mode 3 with TES. Figures 20, 22 and 24 show the optimal scheduling results of the electrical module in different modes. Fig. 21, 23 and 25 show the flow rate and the heat quantity of the medium in the heat network in different modes.

(1) Operating state of the heat supply network in mode 1.

In the "FLP" mode (M1), the thermal power optimization results of the system are shown in fig. 20. The hot dispatch unit of the system includes CCHP and EB. In the hot-water mode, if the system cannot meet the heat load demand, the EB will supplement the insufficient heat. As can be seen from the thermal load curve of fig. 9, the thermal load demand of 23: 00-10:00 is very small, and the fan does not participate in system scheduling, so that the fan is fully output at night, and the output of EB in this section is very small. In the peak period of heat consumption (11: 00-22: 00), the yield of EB is increased to nearly 3000 kW.

In the "FPL" mode, the optimization results of the system heat medium flow rate and heat transfer power are shown in fig. 21. The flow rate of the heat medium and the heat transfer power in the pipe section are positive clockwise and negative conversely.

As can be seen from fig. 21, the trend of the heat medium flow rate curve is substantially the same as the heat transfer power curve of the tube section. The TP5-TP9 and TP1-TP8 are combined cooling, heating and power supply pipe sections. Since the output power of the gas engine is the same in the same mode, the influence of the length of the pipe on the heat medium flow rate and the thermal power is not great, so that the difference between the heat medium flow rate and the heat transfer power of the pipe sections TP5-TP9 and TP1-TP8 is not great. Because the CCHP output is stable in mode 1, the thermal flow rate and thermal power curves for TP5-TP9 and TP1-TP8 are also stable. And in the TP1-TP4 pipe section, when the heat demand is larger than 23: 00-10:00, electric energy participates in the distribution of heat energy, namely under the guidance of the current electricity price, the electric heat conversion of the electric boiler is mainly output.

(2) Operating state of heat supply network in mode 2

The system thermal power optimization results in mode 2 are shown in fig. 22. The hot dispatch unit of the system includes CCHP. The operating mode of the gas turbine in mode 2 is "FHL".

As shown in FIG. 22, the power output curve of CCHP-T corresponds to the power output curve of CCHP in FIG. 13. The output of CCHP-T fluctuates greatly. According to the heat load curve in FIG. 6, the heat load is concentrated between 11:00 and 22:00, so that the average output level of CCHP-T is up to 3000kW during this period, while the average output level of CCHP-T is only around 600kW when the heat load demand is low (23:00 to 10: 00).

In the "FHL" mode, the optimization results of the system heating medium flow rate and the pipe segment heat transfer power are shown in fig. 23. Since the only source of heat for the system is the gas turbine, the heat distribution calls for only the pipe segments TP5-TP9 and TP1-TP 8. According to the characteristics of the heat load requirement of the system, the output of the gas turbine is concentrated at 11:00-22: 00. Therefore, the heat medium flow in the pipeline TP5-TP9 and the pipeline part TP1-TP8 reaches 3m/s at 11:00-22: 00. However, during lower thermal loads, the flow velocity and flow velocity of the thermal medium in the pipe section are lower.

(3) Mode 3 operating State of the Heat network

In the "joint scheduling" mode, the system thermal power optimization results are shown in fig. 24. The system thermal scheduling unit includes CCHP and TES. In mode 3, the operating mode of the gas turbine is decoupled from the "FHL" mode, and the changed operating mode of the gas turbine is coordinated with each other in a joint optimized operating mode, i.e., the "FPL" mode and the "FHL" mode.

During the low heat demand period (23: 00-24: 00; 1:00-10: 00), the output power of the gas turbine is not limited by the heat load. At this time, the gas turbine directly participates in the power grid dispatching, and the waste heat is stored in the TES. During periods of high heat demand (11: 00-22: 00), the gas turbine is operated in an "FHL" mode, with output levels limited by heat load and TES output. The optimal scheduling mode in mode 3 may increase the output of the gas turbine at night and the overall output level of the gas turbine.

The optimization results of the heat medium flow rate and the heat transfer power in mode 3 are shown in fig. 25. As can be seen from FIG. 25, TES stores a large amount of heat energy between 23:00 and 24:00 and between 1:00 and 10:00, and releases heat energy between 11:00 and 22: 00.

The total natural gas consumption of the system consists of the natural gas consumption of the gas turbine and the natural gas load of the residents. Since the natural gas load of residents is the same in the three modes, the difference between the natural gas consumption and the natural gas grid condition in the three modes is mainly affected by the efficiency of the gas turbine. In the system, the natural gas consumption of residents is mainly used for the resident gas stove, so that the natural gas consumption curve of the residents has three peak values of 6: 00-9: 00, 11: 00-2: 00 and 17: 00-21: 00. Table 5 shows the natural gas consumption and transmission power of the natural gas network in different modes.

Tab.5 Natural gas consumption and Transmission Power of the Natural gas network in different modes

The total consumption curve of the natural gas load is consistent with the power curve of the natural gas pipeline network. Since the cumulative output of the CCHP system in mode 1 is the smallest of the three modes, the consumption of the CCHP system in mode 1 is also the smallest.

(1) Operating conditions of the Natural gas network in mode 1

In mode 1, the gas turbine is operated according to the "FPL" mode, and the natural gas consumption of the gas turbine is closely related to the outcome of the gas turbine's participation in the grid scheduling. The peak period of the air load in the system is also the period of high power output in the CCHP system. FIG. 26 shows the best scheduling result of NG power in mode 1

As shown in fig. 26, the peak period of the natural gas grid is greatly affected by the load of the resident gas and the load of the system electric power, but is not affected by the heat load of the system. The main reason is that the gas turbine mainly participates in the power scheduling of the system to meet the power demand of the system.

(2) Operating conditions of the Natural gas network in mode 2

In mode 2, the gas turbine is operating in "FHL" mode, and the natural gas consumption of the gas turbine is closely related to the outcome of the gas turbine's participation in the thermal network scheduling. The peak time period of the air load in the system coincides with the high demand time period of the thermal load in the system.

As shown in fig. 27, the peak load period of the natural gas grid is greatly affected by the resident gas load and the system heat load, but is not affected by the system electric load. The main reason is that in mode 2, the gas turbine primarily participates in the thermal scheduling of the system to meet the thermal demand of the system, and the output curve of the gas turbine always follows the thermal load curve.

(3) Operating conditions of the Natural gas network in mode 3

In mode 3, the operating mode of the Gas Turbine (GT) is changed. During periods of high heat demand and high efficiency (11: 00-22: 00), the gas turbine is operated in an "FHL" mode. In the time period (23: 00-24: 00; 1:00-10: 00) of low heat demand and low efficiency, the gas engine operates according to an 'FLP' operation mode. Fig. 25 shows the optimal scheduling result of NG power in mode 3.

As shown in FIG. 28, the peak power period of the natural gas grid is greatly affected by the resident gas loads and the gas turbine operating efficiency. The main reason is the introduction of TES in mode 3, which increases the nighttime output of the gas engine and stores a lot of heat during the nighttime. When daytime demand for heat load is great, TES is released to reduce the output of the gas turbine during daytime. At 23:00-24:00 and 1:00-10:00, the operation mode of the gas turbine is 'FLP' mode, the operation efficiency of the gas turbine is higher than that of the mode 2, and the gas consumption is increased. And when the speed is 11:00-22:00, the operation mode of the gas turbine is an FHL mode. However, TES participates in the heating system, the output of the gas turbine is less than mode 3, and the gas consumption is less.

In order to compare the energy cost, the operation cost and the environmental cost of the RIES in different operation modes conveniently, the optimal operation results in the three modes are calculated by adopting a Genetic Algorithm (GA). Table 6 shows the total cost comparison between the different modes. Table 7 shows the running cost comparison between the different modes. Fig. 29 shows a comparison of power exchange between different modes.

Total cost comparison between Tab.6 different modes

As shown in Table 6, the total cost of schema 1 and schema 2 is 84899.08-ary and 84988.86-ary, respectively, while schema 3 is relatively small and 77816.701-ary. In terms of energy costs, the natural gas purchase cost of mode 1 is the greatest of the three modes because the operating mode in mode 2 is the "FHL" mode, the gas turbine operates inefficiently during low thermal loads, and NG consumption is relatively large.

Tab.7 running cost comparison of different modes (Yuan)

As shown in Table 7, the operating cost of mode 1 is relatively large, about 1 ten thousand dollars higher than that of the other two modes. In mode 1, both GT and EB participate in system demand response scheduling, which increases the operating cost of the system.

Of the three modes, in mode 1, power is exchanged frequently between the system and the grid. In mode 1, the GT operates in "FPL" mode, with all devices in the system responding to grid schedules. The production of GT is limited due to environmental factors. To meet the demand of the thermal load, the system needs to purchase a large amount of electricity from the grid for heating. In

modes

2 and 3, the GT mode of operation is "FHL" during periods of high thermal load demand. The GT can generate a large amount of electricity while meeting the thermal load requirements of the system. Thus, the system delivers a large amount of power to the grid.

When the carbon emissions of the system are greater than the emission limits, excess carbon emission credits may need to be purchased. To penalize excessive carbon emissions, the additional purchase carbon emission allowance employs a stepped carbon emission price as shown in fig. 13 herein. In mode 1, the output of the GT is always within the carbon emission quota, due to participation in grid scheduling and carbon emission restrictions. Thus, the system in mode 1 does not require carbon trading. Fig. 30 shows the hourly emissions of pollutants in mode 1. The emission of various pollutants in mode 1 is greatly affected by the system scheduling.

In mode 2, the operation mode of the GT is "FHL" mode, and the output of the GT is limited by thermal load. During periods of high thermal load (11: 00-22: 00), to meet the user's thermal load demand, the system maintains the output level of the GT at a higher level, with pollutant emission levels exceeding emission limits. Second, due to the inefficiency of GT during low heat load demand phases, the pollutant emissions of the system are large. Fig. 31 shows the hourly emissions of pollutants in mode 2. The emissions of various pollutants in mode 2 are greatly affected by the thermal load requirements.

In mode 3, the operational mode of GT is decoupled based on the operational mode of mode 2. The GT improves the operation efficiency in the low heat load demand stage (23: 00-24:00, 1:00-10: 00) and reduces the output level in the high heat load stage (11: 00-22: 00). Resulting in a reduction in the overall carbon emission level of the system compared to mode 2. Fig. 32 shows the amount of pollutant emissions per hour in mode 3.

According to fig. 13, the emission limit for the rines region is 100 tons. When the carbon emissions of the system exceed this limit, the system needs to purchase the carbon emissions in a carbon trading market. Thus, the size of the emission limits directly affects the carbon emission cost of the system and the plant operation. This section appropriately adjusts the rate of change of emission limits to study the impact of carbon trading quotas on environmental costs. FIG. 33 shows carbon emission costs at different emission limit adjustment ratios.

According to fig. 33, the environmental cost changes in the three modes exhibit completely different trends when the emission limit is changed. The environmental cost trends in mode 2 and mode 3 are similar and inversely related to the emission limits. In contrast, the environmental cost of mode 1 shows a completely opposite trend. The main reason for this is the different modes of operation of the system in the three modes.

In the "FPL" mode (M1), the output of the GT is affected not only by system scheduling, but also by carbon emission factors. According to the objective function herein, the output of the GT will be severely limited when the environmental cost of the system reaches a certain level. When the carbon trading quota changes, the operational state of the GT will change dramatically to ensure global optimization of the system optimization objective function. The GT scheduling strategies in mode 2 and mode 3 are relatively less affected by the amount of carbon transactions than in mode 1. The GT mode of operation is primarily "FHL" mode. In this mode, the output of the gas turbine must first meet the thermal load requirements of the system before it can participate in grid dispatch. In this case, the output of the GT is rigid. Thus, when the emission limits change, the GT will react less to the change.

A method for optimizing the operation of an RIES system is provided. Based on the gas-heat energy concentrator, the electric-heat energy concentrator and the electric-heat-gas energy concentrator, the coupling of an electric-heat-gas network is realized, and an optimal scheduling mode of the RIES is provided by combining the economic operation mode of the RIES. In order to verify the effectiveness of the proposed optimal scheduling mode, a certain RIES community in china was selected for simulation. Through the comparative analysis of the three modes, namely a power-on-heating (FPL) mode, a power-on-heating (FHL) mode and a combined scheduling mode, the result shows that the proposed combined scheduling mode can fully exert the economic operation of the RIES, and the pollutant emission is strictly controlled. The coupling relation among the power system, the thermodynamic system and the natural gas system is comprehensively considered, the combined dispatching operation of the cogeneration system can realize the economic advantage of cogeneration, and the comprehensive energy utilization efficiency is improved.

Claims

1. A regional comprehensive energy system operation optimization method is characterized by comprising the following steps: the method comprises the following steps:

s1, establishing an energy network transmission model;

s11, establishing a natural gas network model;

s12, establishing a thermal network model;

s13, establishing an electric-heat-air coupling network system model;

s2, establishing an economic dispatching model of the regional comprehensive energy system, wherein the economic dispatching model comprises economic cost and constraint conditions;

s4, obtaining a result of the mixed integer linear programming model through a drosophila optimization algorithm;

the economic cost calculation formula in step S2 is:

F＝MIN(C_transaction+C_operation+C_storage+C_environment) (22)

wherein M is a demand response coefficient; m is 1 to represent that the system participates in demand response; m is 0, which means that the system does not participate in the demand response; epsilon_drpIs demand response electricity price; epsilon_fpIs a fixed electricity price; p_e-grid(t) is the exchange power between the regional integrated energy system and the grid; pi is the natural gas price; p_cchp(t) and P_GB(t) the output power of the combined cooling, heating and power supply and the gas boiler respectively; eta_cchpAnd η_GBThe output efficiency of the combined supply of cold, heat and electricity and the gas boiler is respectively; LHV_NGIs the low heating value of natural gas;

wherein, P_kIs the output power of device K;

is the unit power cost of the device;

C_storage＝C_EES+C_TES+C_NGES (25)

wherein, C_EESIs the cost of energy storage; c_TESIs the cost of heat storage; c_NGESIs the cost of gas storage; c_ESS-purchaseIs the cost of purchase of the electrical energy storage device; y is the number of times the energy storage device is used in the full life cycle; c_ESS-capIs the capacity of the energy storage device; p_ESS(t)、P_TES(t) and P_NGES(t) power for energy storage, heat storage and gas storage, respectively; alpha and beta are the unit power costs of heat storage and gas storage, respectively;

wherein, P_k(t) is the power of the blowdown source k; zeta_k,jIs the unit emission cost of the pollutant j from the pollution discharge source k; gamma ray_jIs the unit emission price of pollutant j; d₁And D₂Is a government regulated environmental emission allowance; delta_iAnd λ_jIs a step environment transaction price;

the constraint conditions comprise power balance constraint of electricity, heat and gas, external network transmission power constraint, cogeneration operation constraint and energy storage equipment constraint;

the electric, thermal and pneumatic power balance constraints are as follows:

P_e-load(t)+P_ESS-char(t)＝P_e-grid(t)+P_WT(t)+P_PV(t)+P_cchp(t)η_g-e+P_EES-dis(t) (31)

P_t-load(t)+P_{TES_char}(t)＝P_cchp(t)η_g-t+P_GB(t)η_GB+P_{TES_dis}(t) (32)

P_{ng_load}(t)+P_{NGES_char}(t)+P_GB(t)+P_cchp(t)＝P_{ng_grid}(t)+P_{NGES_dis}(t) (33)

wherein, P_e-load(t)、P_t-load(t) and P_{ng_load}(t) electrical load power, thermal load power and natural gas load power, respectively; p_ESS-char(t)、P_{TES_char}(t) and P_{NGES_char}(t) the charging power of the electricity storage, heat storage and gas storage systems, respectively; p_EES-dis(t)、P_{TES_dis}(t) and P_{NGES_dis}(t) the discharge power of the electricity storage, heat storage and gas storage systems, respectively; p_e-grid(t) and P_{ng_grid}(t) the exchange power of the regional comprehensive energy system with the power grid and the gas grid respectively; p_WT(t) and P_PV(t) power of WT and PV generator, respectively; eta_g-eAnd η_g-tGas-to-electricity and gas-to-heat efficiency of combined cooling, heating and power generation;

wherein the content of the first and second substances,

and

respectively electric network and districtMinimum exchange power and maximum exchange power between the domain integrated energy systems;

the air network constraints are:

wherein the content of the first and second substances,

is a natural gas supply from source a;

and

and

is the minimum and maximum pressure at node b; sigma_cIs the compression ratio of the compression station c, σ_cIs equal to

And

is the minimum and maximum compression ratio of compression station c;

the cogeneration operation constraints are:

wherein, P_cchpn(t) is the rated power of the gas turbine at time t;

and

is the upper and lower limit values of the gas turbine climb rate;

the energy storage device constraints are:

wherein X represents an electricity storage system, a heat storage system or a gas storage system; s_x(t) and S_x(t +1) is the state of the energy storage system at time t and t + 1; p_x-char(t) and P_x-dis(t) represents the power of charging and discharging of the energy storage system, respectively; eta_x-charAnd η_x-disRespectively the charge-discharge efficiency of the energy storage system;

and

and

and

is the energy release function of the energy storage systemUpper and lower limits for the rate.

2. The regional integrated energy system operation optimization method of claim 1, wherein: the natural gas network model in the step S11 includes a node pressure model, a natural gas pipeline gas flow model, a pressurizer model, and a node gas flow balance model;

the formula of the node pressure model is as follows:

wherein the content of the first and second substances,

respectively representing the maximum and minimum pressure values of the node i; SGB represents a collection of natural gas network nodes;

the formula of the gas flow model of the natural gas pipeline is as follows:

wherein, fP_pbtThe flow of the natural gas pipeline P in the load b area at the time t;

and

is at two ends of the pipeline PThe air pressure of (a); phi_pAnd sgn_pRepresenting the gas flow parameters and flow direction of the pipeline P;

and

the formula of the pressurizer model is as follows:

wherein fc_cbtRepresenting the flow through compressor c in the b load region at time t;

and

respectively representing the air pressure at the inlet i and the outlet j at the time t; gamma-shaped_CRepresents the pressure increase ratio of the compressor c;

represents the upper limit of the transmission capacity of the pressurizer;

the formula of the node gas flow balance model is as follows:

wherein A is_jp、U_jc、C_jhAnd D_jrRespectively representing correlation matrixes of a natural gas pipeline p, a pressurizer c, a natural gas source h, a natural gas load r and a natural gas network node j; WL_rbtRepresenting the natural gas load in the b load region of t years.

3. The regional integrated energy system operation optimization method of claim 1, wherein: the thermodynamic network model in the step S12 includes node flow balance, pressure loss balance, heat-flow equation, and node temperature fusion;

the equation of the node flow balance is as follows:

wherein q is_ps,k,t，q_pr,k,tRespectively representing the flow of the kth section water supply/return pipeline at the time t;

respectively a water supply/return pipe set starting from the node i, S_ns/S_nrRespectively a water supply/return pipe node set, S_tIs a set of scheduling time periods;

the pressure loss balance equation is:

wherein S is_pu,s、S_pu,rFor a collection of water pumps in a water supply network, p_pu,i,tThe pressure provided for the ith water pump at the moment t;

the heat-flow equation is:

wherein the content of the first and second substances,

the equation for the node temperature fusion is:

wherein, T_ns,i,t/T_nr,i,tThe node temperatures at the ith node t moment of the water supply pipeline/the water return pipeline respectively;

4. the regional integrated energy system operation optimization method of claim 1, wherein: the electric-thermal-air coupling network system model in the step S13 realizes energy conversion, storage and distribution through an energy hub; the energy hub comprises an NG-T hub, an E-T hub and an E-NG-T hub;

the energy centers of the NG-T concentrator are:

wherein, O_TRepresenting heat demand, supplied by various heat sources, kW; lambda [ alpha ]₁Representing the ratio of gas boiler heating to total heat demand; n is a radical of_NGRepresents a natural gas input; δ 1 is the natural gas consumed by the gas boiler; eta_GBRepresents the energy conversion efficiency of a gas boiler;

the energy centers of the E-T hub are:

wherein λ is₂Is the ratio of the electric boiler heat supply to the total heat demand; lambda [ alpha ]₃Is the ratio of heat pump supply to total heat demand; n is a radical of_ERepresenting power input in kW; alpha is alpha_EBIs the power proportion input into the electric boiler; alpha is alpha_HPIs the proportion of power input to the heat pump; eta_EBIs the energy conversion efficiency of the electric boiler system; eta_HPIs the energy conversion efficiency of the heat pump system;

the energy centers of the E-NG-T concentrator are:

where δ 2 represents the NG proportion assigned to the E-NG-T hub; eta_CCHP-TIs the thermal efficiency of the E-NG-T hub; eta_CCHP-EIs the electrical efficiency of the E-NG-T hub; beta is a_CCHPIs the proportion of the total power demand of the system that is provided by the E-NG-T hub.