An ies multi-objective dynamic optimization scheduling method considering multi-energy flow constraints

Abstract

The application discloses a kind of IES multi-objective dynamic optimization scheduling method considering multi-energy flow constraint, including the following steps: IES steady-state MEF modeling;Based on the calculation of MEF using Newton-Raphson method, the steady-state energy flow distribution of IES is obtained;IES multi-objective optimization scheduling model considering MEF constraint is constructed;The model constructed in step c is solved using NSGA-II based on normal distribution crossover operator, and the Pareto optimal solution set of IES multi-objective dynamic optimization scheduling is obtained.To ensure the operation safety and reduce the analysis error, the IES multi-objective dynamic optimization scheduling model considering MEF constraint is established, with the minimum operation cost and pollutant emission external cost as the target, and the process of solving the model using NSGA-II based on normal distribution crossover operator is given.The effect of the method is good through test.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of power integrated energy system scheduling, and particularly relates to a multi-objective dynamic optimization scheduling method of IES considering multiple energy flow constraints. BACKGROUND

[0002] With the increasingly prominent energy and environmental problems, how to improve the comprehensive utilization efficiency of various types of energy and reduce pollutant emissions has become a key problem to be solved for China to build a clean, low-carbon, safe and efficient modern energy system.

[0003] As an important part of the social energy system, the traditional power system, heat system and natural gas system are separately planned, designed and operated, which breaks the coupling between different types of energy and greatly limits the flexibility of energy system operation. Integrated energy system (IES) as an important form of new generation energy system covers power supply, heat supply, gas supply and electrified transportation, integrates various forms of energy supply, energy conversion and energy storage equipment, realizes the coupling of different types of energy at different links such as source, network and load, has the characteristics of flexible operation, low carbon and high efficiency, and high renewable energy consumption rate, and has been highly valued by people.

[0004] At present, the research results of IES at home and abroad mainly focus on the analysis of electric-thermal-natural gas multiple energy flow (MEF), system joint planning and coordinated operation. For example, some documents make steady-state modeling for electric-gas and electric-thermal power flow in IES and propose decomposition method and overall method for solving MEF nonlinear equation set; some documents propose a distributed iterative calculation method for electric-gas hybrid MEF based on multi-agent, but the required number of iterations is large and the obtained solution is not proved to be optimal solution; some documents consider the correlation of wind power and load and study the probabilistic optimal power flow problem of electric-gas interconnection system; some documents decompose electric-gas hybrid optimal MEF into electric and gas subsystems based on the structure of energy hub, but the C matrix constructed to reflect the connection relationship of the subsystems is not reversible in some cases; some documents study the joint planning problem of electric-gas interconnection system, consider the safety constraints of the system, decompose the complex nonlinear programming problem into an investment main problem and multiple constraint checking sub-problems based on Benders decomposition, which greatly reduces the computational complexity; some documents study the effect of electric-thermal system interconnection on wind power consumption.

[0005] The energy flow in the IES, especially the heat energy transmission, causes high loss. For the optimal scheduling problem of the IES, most research results only optimize from the perspective of multi-type energy conversion, and some documents research the day-ahead scheduling problem of the IES based on the energy hub, but the energy hub only provides a general model of multi-energy conversion and cannot consider the network structure of the system, so the electric and heat energy losses are ignored, and there is a certain error. SUMMARY

[0006] The purpose of the present application is to provide a multi-objective dynamic optimal scheduling method of an IES considering multi-energy flow constraints, so as to improve the operation safety and reduce the analysis error.

[0007] In order to achieve the purpose of the present application, the technical scheme provided by the present application is as follows:

[0008] A multi-objective dynamic optimal scheduling method of an IES considering multi-energy flow constraints, comprising the following steps:

[0009] Step a: IES steady-state MEF modeling;

[0010] Step b: calculating the MEF based on the Newton-Raphson method to obtain the steady-state energy flow distribution of the IES;

[0011] Step c: constructing a multi-objective optimal scheduling model of the IES considering MEF constraints;

[0012] Step d: using NSGA-II based on normal distribution crossover operator to solve the model constructed in step c to obtain the Pareto optimal solution set of the multi-objective dynamic optimal scheduling of the IES.

[0013] Compared with the prior art, the present application has the following technical effects:

[0014] The present application establishes the steady-state energy flow models of the electric power subsystem, the heat subsystem and the natural gas subsystem in the IES, and models the coupling devices between the subsystems, and on this basis, establishes the steady-state MEF model of the IES, and proposes a MEF calculation method based on the Newton-Raphson method. In order to ensure the operation safety and reduce the analysis error, a multi-objective dynamic optimal scheduling model of the IES is established, which takes the minimum of the operation cost and the external cost of pollutant emission as the target and considers the MEF constraints, and a process of using NSGA-II based on normal distribution crossover operator to solve the model is given. Through tests, the method of the present application has good effect. BRIEF DESCRIPTION OF DRAWINGS

[0015] Figure 1 The flowchart of the method of the present application is shown in the figure;

[0016] Figure 2A schematic diagram of a pipeline with gas driven compressors;

[0017] Figure 3 A structure diagram of IES example;

[0018] Figure 4 Power curves of PV, WT, electrical load and thermal load;

[0019] Figure 5 A Pareto frontier diagram of IES multi-objective dynamic optimization scheduling;

[0020] Figure 6 An electrical power curve a of power generation equipment;

[0021] Figure 7 A thermal power curve a of heat supply equipment;

[0022] Figure 8 A diagram of electrical power loss, thermal power loss and compressor gas consumption;

[0023] Figure 9 An electrical power curve b of power generation equipment;

[0024] Figure 10 A thermal power curve b of heat supply equipment;

[0025] Figure 11 A Pareto frontier diagram of IES electrical and thermal subsystem decoupled multi-objective dynamic optimization scheduling. DETAILED DESCRIPTION

[0026] The technical solutions in the embodiments of the present application will be described clearly and completely below with reference to the accompanying drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative work fall within the scope of protection of the present application.

[0027] Main idea: Firstly, the steady-state energy flow models of the power subsystem, the heat subsystem and the natural gas subsystem in IES are established, and the coupling devices between the subsystems are modeled. On this basis, the steady-state MEF model of IES is established, and the MEF calculation method based on Newton-Raphson method is proposed. In order to ensure the operation safety and reduce the analysis error, the multi-objective dynamic optimization scheduling model of IES is established, which takes the minimum of the operation cost and the external cost of pollutant emission as the objective and considers the MEF constraint. The process of solving the model by using NSGA-II based on normal distribution crossover operator is given. Finally, the operation mechanism of the equipment in IES under different optimization objectives is analyzed by example, and the important role of the coupling between the subsystems in improving the economic benefit and environmental benefit is illustrated by comparing the Pareto frontiers of the multi-objective optimization scheduling of the coupled operation and the decoupled operation of the subsystems in IES.

[0028] As shown in Figure 1 , the multi-objective dynamic optimization scheduling method of IES considering multi-energy flow constraints provided by the embodiment comprises the following steps:

[0029] Step a: IES steady-state MEF modeling, comprising the following steps:

[0030] Step a.1 steady-state energy flow modeling of power subsystem

[0031] The steady-state energy flow model of the power subsystem in IES is represented by the classical alternating current flow model, and the power flowing through the line ij can be represented as:

[0032]

[0033] The injected active power and reactive power of node i satisfy the node power balance:

[0034]

[0035] In the formula, P Gi , Q Gi and P Li , Q Li represent the active and reactive power related to the power generation equipment and the load, respectively.

[0036] Step a.2 steady-state energy flow modeling of natural gas subsystem

[0037] The nodes in the natural gas subsystem in IES are divided into two categories: one category of natural gas injection amount is known, and the other category of node pressure is known. For the pipeline ij, the relationship between the natural gas flow and the pressure is as follows:

[0038]

[0039] In the formula, f ijK ij is the pipe constant, p i is the pressure of node i, s ij is used to represent the flow direction of natural gas, when p i > p j , +1 is taken, otherwise -1 is taken;

[0040] When natural gas flows in the pipe, pressure drop will be generated along the flow direction. In order to ensure the supply pressure, some high flow rate pipes will be installed with compressors, Figure 2 As shown, nodes m and n represent the beginning and end of the pipe respectively, and m is also the inlet of the compressor, o is the outlet, f com is the gas flow rate of the compressor, f cp is the gas consumption of the compressor, which can be regarded as the load of node m;

[0041] The mathematical model of the compressor is shown in equation (4):

[0042]

[0043] In the equation: k cp is the compression ratio, T gas is the temperature of natural gas, a is the polytropic index of the compressor, q gas is the heat value of natural gas;

[0044] The flow balance equation of the pipe with compressor can be expressed as:

[0045] f com + f cp = f in (5)

[0046] In the equation: f in is the natural gas flow rate at the inlet of the compressor;

[0047] For the natural gas system, the node flow balance equation is as follows:

[0048]

[0049] In the equation: f Si and f Li are the gas injection amount and load gas consumption of node i respectively.

[0050] Step a.3 steady-state energy flow modeling of thermodynamic subsystem

[0051] For each node of the thermodynamic subsystem, there are three temperatures related to it: the heat supply temperature T s represents the temperature of hot water before it is injected into the load node or when it flows out of the heat source node, the output temperature T o represents the temperature of hot water when it flows out of the load node, and the heat recovery temperature Tr represents the temperature of the hot water flowing out of the load node after mixing with the water of other pipes or the temperature of the low-temperature hot water flowing into the heat source;

[0052] For a heat supply system, it needs to be described from two aspects of hydraulic model and thermodynamic model;

[0053] 1) Hydraulic model

[0054] The hydraulic model is used to describe the relationship satisfied by the hot water flowing in the network. For each node, the flow continuity equation should be satisfied:

[0055] ∑m q,in -∑m q,out = m q (7)

[0056] In the formula, m q,in , m q,out are the flows of the water flowing into the node q and flowing out of the node, respectively, and m q is the flow flowing out of the node q;

[0057] The pressure drop h ij of the hot water flowing in the pipe ij caused by the friction can be represented as:

[0058] h ij = κ ij m ij |m ij | (8)

[0059] In the formula, κ ij is the resistance coefficient of the pipe, and m ij is the flow of the water in the pipe;

[0060] For a closed loop composed of pipes, the pressure drop H of the water in the pipe along the loop direction is 0:

[0061] H = ∑h ij = 0 (9)

[0062] 2) Thermodynamic model

[0063] The thermodynamic model [iii] describes the relationship between the heat power and the temperature and flow;

[0064] For the node i, the heat power can be represented as:

[0065] Φ i = C p m i (T s,i -T o,i ) (10)

[0066] In the formula, C pCw

[0067] The heat power transported by pipe ij is expressed as:

[0068] Φ ij = C p m ij (T s,i -T r,i ) (11)

[0069] The temperature change of hot water flowing from node i to node j through pipe ij is expressed as:

[0070]

[0071] where T a is the ambient temperature, λ is the heat conduction coefficient of the pipe, and L is the length of the pipe.

[0072] For any node in the thermal system, the temperature change of water in the pipe before and after mixing at the node can be expressed as:

[0073] ∑(m in T in ) = (∑m out )T out (13)

[0074] where m in , T in and m out , T out are the flow rate and temperature of water flowing into and out of the node, respectively, T in is the actual T o of the node, and T out is the T r of the node.

[0075] The power balance equation for a node in the thermal system is as follows:

[0076]

[0077] Step a.4 Coupling device modeling;

[0078] Consider micro-turbine CHP system, ground source heat pump (GSHP), gas-fired boiler (GB), and fuel cell (FC) as coupling devices between subsystems. The mathematical models of GB and FC are given in

[14] and [iv], and the mathematical models of micro-turbine CHP system and GSHP are as follows:

[0079] 1) Micro-turbine CHP system

[0080] The micro gas turbine CHP system uses natural gas as fuel to generate electricity, and the waste heat is recovered for heating load, and its mathematical model is as follows:

[0081]

[0082] P CHP , Φ CHP , f CHP respectively represent the electric power, heat power and natural gas consumption of the CHP system, η MT is the micro gas turbine power generation efficiency, Φ CHP is the micro gas turbine exhaust waste heat, η l , η rec are the waste heat loss rate and the waste heat recovery efficiency of the lithium bromide unit respectively, and COP h is the heating coefficient.

[0083] 2) Ground source heat pump

[0084] The ground source heat pump system is a new type of environmentally friendly energy utilization system which uses rock-soil, underground water or surface water as low-temperature heat source, consumes electric energy to transfer heat to the load for heating, and the transferred heat Φ GSHP can be 4-5 times of the consumed electric power P GSHP , which can effectively reduce the heating cost. The simplified mathematical model of the ground source heat pump is a quadratic function which approximates the relationship between input and output, as shown in equation (16):

[0085]

[0086] Step b: calculate the MEF based on the Newton-Raphson method to obtain the steady-state energy flow distribution of the IES;

[0087] The IES steady-state MEF bias expression is as follows:

[0088]

[0089] The state variable of the IES is selected as x = [θ; V; p; T s ; T r ; m], which is the state variable of the system. First-order derivation is performed on equation (17) to obtain the correction equation for solving by the Newton-Raphson method:

[0090] ΔF = JΔx (18)

[0091] In the formula: ΔF is the bias vector composed of the left side of equation (17), and J is the Jacobian matrix obtained by first-order derivation of the right side of equation (17);

[0092] Given the appropriate initial values of state variables, the iterative solution of equation (18) is obtained, and the steady-state energy flow distribution of IES is obtained when the algorithm converges.

[0093] Step c: Constructing the IES multi-objective optimization scheduling model considering MEF constraints

[0094] 1. Objective function

[0095] The devices contained in IES include wind turbines WT, photovoltaic power generation PV, energy storage ES, CHP, FC, GSHP and GB. From the economic and environmental aspects, the IES operation cost and pollutant emissions are selected as optimization objectives, the multi-energy flow constraints and device operation constraints are considered, and the IES multi-objective dynamic optimization scheduling model is constructed.

[0096] 1) Minimization of IES operation cost

[0097] The IES operation cost mainly includes the start-up and shutdown cost of power generation devices, the electric energy interaction cost with the external grid, and the cost of purchasing natural gas from the outside of IES, which can be expressed by the following formula:

[0098]

[0099] In the formula: SU i is the start-up and shutdown cost of the i-th device, u i (t) represents the operating state of the device within the t time period, and takes 1 when operating, otherwise takes 0; and P are the purchase and sale electricity prices of IES from and to the external grid at t time, respectively, P gas is the natural gas price, f gas (t) is the amount of natural gas purchased by IES from the outside; T is the total scheduling period, which is 24h, and Δt is the time interval, which is 1h;

[0100] 2) Minimization of IES pollutant emission cost

[0101] Except for PV and WT, other energy supply devices in IES will consume natural gas or electric energy when operating, thus inevitably producing pollutants such as NO x , SO2 and CO2. The external cost of pollutant emission during device operation is selected as the measurement index, and the objective function representing the minimization of pollutant emission is constructed, as shown in equation (20):

[0102]

[0103] In the formula: α k is the external discount cost of the k-th pollutant, k=1, 2, 3 representing NO x , SO2 and CO2, respectively, and λk,i Pikis the emission factor of the kth pollutant for the ith device i (t) is the power of the device at time t, λ grid,k Piesis the emission factor of the kth pollutant for the ith device

[0104] 2. Constraints

[0105] To ensure the safe and stable operation of the IES, the relevant constraints need to be considered, such as power balance constraints, unit output constraints, ES energy constraints, system state quantity constraints, and MEF constraints.

[0106] 1) Power balance constraints

[0107] The power subsystem and the thermal subsystem in the IES need to satisfy the balance of electric power and thermal power, respectively. In addition, the supply and demand balance of natural gas in the natural gas subsystem should be satisfied, as shown in equations (21)-(23):

[0108]

[0109] Φ GB (t) + Φ CHP (t) + Φ GSHP = Φ D (t) + Φ loss (t) (22)

[0110] f gas (t) = f D (t) + f CHP (t) + f GB (t) + f FC (t) + f cp (t) (23)

[0111] In the equations: Pch, Pdis the charging and discharging power of the ES, respectively PV (t), P WT (t) are the PV and WT power, respectively FC (t) is the electric power of the FC loss (t) is the electric power loss of the IES D (t) is the electric load power; Φ GB (t), Φ D (t), and Φ loss (t) are the thermal power of the GB, the load thermal power, and the IES thermal power loss, respectively D (t), f GB (t), and f FC (t) are the natural gas demand of the gas load, the GB gas consumption, and the FC gas consumption, respectively. P loss (t), Φ loss(t) and f cp MEF can be calculated by MEF;

[0112] 2) Equipment operation constraints:

[0113] The power of each type of equipment must be within the specified range:

[0114]

[0115] P = (t) * (t) i (t), Φ j (t) represent the output power of the power supply equipment and heating equipment respectively. In addition, each device also needs to meet the ramp rate constraint and minimum start-stop time constraint;

[0116] The ES also needs to meet the following constraints:

[0117]

[0118] E = (t) * (t) ES (t) is the electrical energy stored by the ES at time t, η ch , η dis are the charging and discharging efficiencies respectively;

[0119] 3) MEF constraints

[0120] The system state variable x needs to be limited within a certain range:

[0121] x min ≤ x ≤ x max (26)

[0122] Step d: use NSGA-II based on normal distribution crossover operator to solve the model constructed in step c to obtain the Pareto optimal solution set of IES multi-objective dynamic optimization scheduling.

[0123] NSGA-II algorithm is used to solve the model. Since the crossover process in NSGA-II uses a simulated binary crossover operator, as shown in equation (27), the search range of the algorithm is limited, and problems such as local optimum, poor convergence, etc. are prone to occur. Therefore, normal distribution is introduced into the binary crossover of the crossover operation, that is, 1.481|N(0,1)| is used instead of parameter β to expand the search space, as shown in equation (28). To further enhance the spatial search capability, the discrete recombination operation of the evolution strategy is introduced into equation (28), thereby obtaining the normal distribution crossover operator (29).

[0124]

[0125]

[0126]

[0127] where a 1 / 2,i is the corresponding ith variable on the offspring chromosome, x 1,i , x 2,i is the corresponding ith variable on the two parent chromosomes, N(0, 1) is a normally distributed random variable, and u is a random number uniformly distributed in the interval (0, 1).

[0128] The basic flow of the NSGA-II algorithm based on the normal distribution crossover operator is as follows:

[0129] (1) Data initialization. Input the network parameters, device parameters, and NSGA-II parameters of the IES.

[0130] (2) Population initialization. Perform one-time initialization of the 24-hour scheduling scheme.

[0131] (3) Calculate the operating cost and pollutant external emission cost corresponding to the individual, and calculate the MEF of the 24 time sections. Add the out-of-limit values of the system state variables and other constraints to the two objective functions as penalty terms to obtain the individual fitness value considering the penalty terms.

[0132] (4) Perform tournament selection, normal distribution crossover, and mutation on the individual to obtain the offspring population.

[0133] (5) Repeat the operation of (3).

[0134] (6) Perform Pareto hierarchical sorting on the parent and offspring populations as a whole to obtain new population individuals.

[0135] Through the above steps, the Pareto optimal solution set of the IES multi-objective dynamic optimization scheduling is finally obtained.

[0136] Application Example

[0137] The IES multi-objective dynamic optimization scheduling model considering the MEF constraint proposed in the present application is used for day-ahead optimization scheduling. Figure 3 In the example, EBi, GBi, and HBi represent the nodes of the electric, heat, and gas subsystems, respectively. The electric power subsystem is connected to the external power grid through node EB12, and a set of 200 kWp PVs are installed at EB3 and EB8, and a 1 MW WT is installed at EB4. The CHP operates in the electrically-heating mode. The maximum and minimum outputs of the devices in the IES are shown in Table 1, in which the ES capacity is 800 kWh. It is assumed that 80% of the power of the external power grid is generated by traditional generators, and 20% is generated by new energy power generation. The time-of-use electricity price for the exchange of power between the IES and the external power grid is shown in Table 2, and the natural gas price is 2.51 yuan / m 3 The PV, WT, and total electric load, heat load, and natural gas load curves are shown in FIG. 1.Figure 4 As shown, it is assumed that the power ratio of each electrical load node is 2:4:3:4:3, the power factor is 0.9, the power ratio of each heat load node is 3:4:8:8, the load ratio of each gas load node is 3:2, and the power line type is LJ95.

[0138] Table 1

[0139]

[0140] Table 2

[0141]

[0142] Comparison of optimization results under different objectives

[0143] The multi-objective optimization scheduling model proposed in this application was used to simulate the example IES. The NSGA-II algorithm based on the normal distribution crossover operator was employed, with a population size of 50 and a maximum generation count of 1000. The obtained Pareto front is as follows: Figure 5 As shown.

[0144] Simulation results show that the relationship between operating costs and external costs of pollutant emissions is non-linear, starting from the point where operating costs are lowest ( Figure 5 From midpoint A to the point where the external cost of pollutants is lowest ( Figure 6 At point B, the external cost of pollutants decreases by 301 yuan, but the operating cost increases by 1247 yuan. If economic and environmental goals are treated equally, operators will tend to favor economic goals. Only when operators attach sufficient importance to environmental goals will they tend to choose the option with lower external costs of pollutant emissions.

[0145] To analyze the differences in the operation of internal devices of IES under different objectives, we selected... Figure 5 For midpoints A and B, dynamic economic scheduling and dynamic environmental scheduling are performed on the IES, and the corresponding scheduling results are as follows: Figure 6-8 As shown.

[0146] 1) Dynamic economic scheduling

[0147] With the goal of minimizing operating costs, the total daily operating cost of the IES is 28,290 yuan, and the external cost of pollutant emissions is 921 yuan. Since the total output of PV and WT in the IES example is relatively high, and the emissions of CHP and FC are also relatively low, the external cost of pollutant emissions is at a low level. The dynamic economic scheduling results of the IES are as follows: Figure 6 and Figure 7 As shown. By Figure 4 It can be seen that around 6:00, the total power of the PV and WT inside the IES is greater than the load power. Therefore, in Figure 6The IES outputs a small amount of power to the external grid in the corresponding period, and the ES is charged in the low-price period before 7:00; in the noon peak period, the ES discharges, and at the same time, the PV and WT have large outputs, so the total power of the power supply is greater than the load power, and therefore the IES also outputs the remaining power to the outside; the power supply cost of the MT is about 0.79 yuan / kWh, and the power supply cost of the FC is about 0.468 yuan / kWh, so the average cost of power supply and heat supply in the CHP mode is about 0.344 yuan / kWh, which is lower than the power supply cost of the external grid in the flat and peak periods, and therefore the CHP and FC operate at the maximum power in this period; the heat supply cost of the GSHP in the peak period is only about 0.83 / 4.5=0.184 yuan / kWh, which is lower than the heat supply cost of the GB and the CHP, and therefore the GSHP operates at the maximum power in each period of the whole day, and the GB is used to maintain the heat power balance in the IES.

[0148] Since the operation and maintenance cost of the ES is ignored in the ES model established in the present application, the optimal charging and discharging process obtained by simulation is not unique, and there is a discontinuous discharging process in the noon peak period.

[0149] The changes of the electric power loss, the heat power loss and the compressor gas consumption in the IES under the condition of the minimum operation cost are shown in Figure 8 .

[0150] 2) Dynamic environmental scheduling

[0151] When the external cost of pollutant emission is the minimum, the total operation cost of the IES is 29537 yuan, and the external cost of pollutant emission is 620 yuan. The dynamic environmental scheduling result of the IES is shown in Figure 9 , Figure 10 .

[0152] The pollutant emission of the GSHP for heat supply is related to the proportion of the electric energy from different sources in the IES, and the higher the proportion of the external grid electric energy, the lower the cleanliness of the electric energy in the IES, and the higher the external cost of the pollutant emission of the GSHP for heat supply. As shown in Figure 9 , Figure 10 , when the environmental cost is taken as the target, the GSHP is in the shutdown state most of the time, and only operates in some periods when the IES purchases a small amount of power from the external grid or sells power to the external grid, i.e. when the electric energy is clean. The external cost of the pollutant emission of the MT for energy supply by consuming natural gas is fixed, and the MT is basically in the shutdown state in some periods of 0:00-8:00 and 23:00-24:00 when the proportion of the power of the PV and WT is high. The pollutant emission of the GB for heat supply using natural gas as fuel is obviously lower than that of other devices, and the GB maintains the heat power balance in the IES while supplying more heat energy in most periods.

[0153] The gas purchase amount of IES from outside in two cases is shown in Table 3. It can be seen from Table 3 that, due to the clean energy of natural gas, the gas purchase amount in the dynamic environmental scheduling case is higher than that in the dynamic economic scheduling case.

[0154] Table 3

[0155]

[0156]

[0157] Analysis of the effect of subsystem coupling on the optimal operation of IES

[0158] In order to illustrate the economic and environmental benefits brought by the coupling of the internal electric and thermal subsystems of IES, the CHP and GSHP in the example IES are removed to decouple the electric and thermal subsystems, and the Pareto front obtained by the method of the present application is shown in Figure 11

[0159] When the electric and thermal subsystems are decoupled, the flexibility of IES operation is greatly reduced, and the optimization space is reduced. It can be seen from the simulation results of Figure 11 that the operation cost and the external cost of pollutant emission are higher than those of Figure 5 , and the difference between the maximum and minimum values of the operation cost and the external cost of pollutant emission is only about 100 yuan, and the two are basically in a linear relationship. Comparing Figure 9 and Figure 10 , Figure 11 it can be seen that the coupling between the internal subsystems of IES plays an important role in the optimal operation. Therefore, in order to improve the economic and environmental benefits of IES operation, multi-energy conversion devices should be added in IES to strengthen the interaction between different energies and expand the flexible operation range.

[0160] Finally, it should be noted that: the above examples are only used to illustrate and describe the present application, and are not intended to limit the present application to the scope of the described embodiments. In addition, those skilled in the art can understand that the present application is not limited to the above examples, and more variants and modifications can be made according to the teaching of the present application, and these variants and modifications all fall within the scope of the present application.​

Claims

1. A method for IES multi-objective dynamic optimization scheduling considering multi-energy flow constraints, characterized in that, Comprising the following steps: Step a: IES steady-state MEF modeling; Step b: calculating the MEF based on the Newton-Raphson method to obtain the steady-state energy flow distribution of IES; Step c: constructing an IES multi-objective optimization scheduling model considering MEF constraints; The construction of the IES multi-objective optimization scheduling model considering MEF constraints specifically includes:

1. Objective function The devices contained in the IES include wind turbines WT, photovoltaic power generation PV, energy storage ES, micro combined heat and power CHP, fuel cell FC, ground source heat pump GSHP and gas boiler GB, from the aspects of economy and environment, the IES operation cost and pollutant emission are selected as optimization objectives, considering multi-energy flow constraints and device operation constraints, an IES multi-objective dynamic optimization scheduling model is constructed; 1) IES operation cost minimization The IES operation cost includes the start-stop cost of the power generation device, the electric energy interaction cost with the external power grid and the cost of purchasing natural gas from the outside of the IES, which can be expressed by the following formula: where: SU i is the start-up cost of the ith equipment, u i (t) represents the running state of the equipment in the t period, taking 1 for running and 0 otherwise; respectively represent the purchase price and the sale price of electricity from / to the external power grid at time t, respectively represent the purchase power and the sale power of electricity from / to the external power grid; p gas is the natural gas price, f gas (t) is the amount of natural gas purchased by the IES from the outside; T is the total scheduling period, taking 24h, and Δt is the time interval, 1h. 2) IES pollutant emission cost minimization The other energy supply devices in IES, except PV and WT, consume natural gas or electricity when they operate, thus inevitably produce NO x , SO2 and CO2 pollutants. The external cost of pollutant emission when the selected device operates is chosen as the measurement index, and the objective function representing the minimum pollutant emission is constructed, as shown in equation (20): wherein: a k is the external discount cost of the kth pollutant, k = 1, 2, 3 for NO x , SO2and CO2, respectively, and λ k,i is the emission factor of the kth pollutant for the ith device, P i (t) is the power of the device at time t, λ grid,k is the emission factor of the pollutant corresponding to the electricity purchased from the external grid by the IES; 2. Constraint conditions In order to ensure the safe and stable operation of the IES, the relevant constraint conditions need to be considered; 1) Power balance constraint The electric power and thermal power of the electric power subsystem and the thermal power subsystem of the IES need to be balanced respectively, in addition, the supply and demand balance of natural gas in the natural gas subsystem should be met, as shown in formula (21)-(23): Φ GB (t) + Φ CHP (t) + Φ GSHP = Φ D (t) = Φ loss (t) (22) f gas (t) = f D (t) + f CHP (t) + f GB (t) + f FC (t) + f cp (t) (23) where: P (t) and P (t) are the charging and discharging power of ES, P PV P (t) and P (t) are the PV and WT power, P WT P (t) and P (t) are the FC electric power, P FC P (t) and P (t) are the IES electric power loss, P loss P (t) and P (t) are the electric load power; Φ D P (t) and P (t) are the GB thermal power, load thermal power and IES thermal power loss; f GB P (t) and P (t) are the natural gas demand of gas load, GB gas consumption and FC gas consumption; P D P (t) and P (t) are the natural gas demand of gas load, GB gas consumption and FC gas consumption; P loss P (t) and P (t) are the natural gas demand of gas load, GB gas consumption and FC gas consumption; P D P (t) and P (t) are the natural gas demand of gas load, GB gas consumption and FC gas consumption; P GB P (t) and P (t) are the natural gas demand of gas load, GB gas consumption and FC gas consumption; P FC P (t) and P (t) are the natural gas demand of gas load, GB gas consumption and FC gas consumption; P loss P (t) and P (t) are the natural gas demand of gas load, GB gas consumption and FC gas consumption; P loss P (t) and P (t) are the natural gas demand of gas load, GB gas consumption and FC gas consumption; P cp P (t) and P (t) are the natural gas demand of gas load, GB gas consumption and FC gas consumption; P 2) Device operation constraint: The power of each type of device must be within the specified range when operating: where P i (t), Φ j (t) represent the output power of the power supply and the heating device, respectively; in addition, each device has to satisfy ramp rate constraints and minimum start-up time constraints; ES also needs to satisfy the following constraints: wherein: E ES (t) is the electrical energy stored by ES during the time period t, η ch , η dis are the charge and discharge efficiencies, respectively. 3) MEF constraint The system state variable x needs to be limited within a certain range: x min ≤x≤x max (26) Step d: using NSGA-II based on normal distribution crossover operator to solve the model constructed in step c to obtain the Pareto optimal solution set of IES multi-objective dynamic optimization scheduling.

2. The IES multi-objective dynamic optimization scheduling method considering multi-energy flow constraints according to claim 1, wherein, The step a includes the following steps: Step a.1 steady-state energy flow modeling of electric power subsystem; Step a.2 steady-state energy flow modeling of natural gas subsystem; Step a.3 steady-state energy flow modeling of thermal power subsystem; Step a.4 coupling device modeling.

3. The IES multi-objective dynamic optimization scheduling method considering multi-energy flow constraints according to claim 2, characterized in that, The steady-state energy flow modeling of the electric power subsystem is specifically as follows: The steady-state energy flow model of the electric power subsystem in the IES is represented by the classical alternating current flow model, and the power flowing through the pipe ij can be expressed as: The active power and reactive power injected at node i satisfy the node power balance: where: P Gi , Q Gi and P Li , Q Li represent active and reactive power related to the power generation facility and the load, respectively.

4. The IES multi-objective dynamic optimization scheduling method considering multi-energy flow constraints according to claim 3, characterized in that, The steady-state energy flow modeling of the natural gas subsystem is specifically as follows: The nodes in the natural gas subsystem in the IES are divided into two categories: one category of natural gas injection amount is known, and the other category of node pressure is known, for the pipe ij, the relationship between the natural gas flow and the pressure is as follows: where: f ij is the flow in pipe ij, K ij is the pipe constant, p i is the pressure at node i, s ij is used to characterize the direction of flow of natural gas, when p i > p j then +1, otherwise -1; When natural gas flows in a pipeline, it will produce pressure drop along the flow direction. In order to ensure the supply pressure, a compressor will be installed on the pipeline with certain flow rate. Nodes m and n represent the beginning and end of the pipeline respectively, and m is also the inlet of the compressor, o is the outlet, f com is the gas delivery rate of the compressor, cp is the gas consumption rate of the compressor, which can be regarded as the load of node m; The mathematical model of the compressor is shown in formula (4): where: k cp is the compression ratio, T gas is the natural gas temperature, a is the polytropic index of the compressor, q gas is the heating value of the natural gas; The flow balance formula of the pipe containing the compressor can be expressed as: f com +f cp = in (5) where: f in is the natural gas flow at the compressor inlet; For the natural gas system, the node flow balance formula is as follows: where: f Si , f Li are the gas injection amount and the gas consumption amount of the load at node i, respectively.

5. The IES multi-objective dynamic optimization scheduling method considering multi-energy flow constraints according to claim 4, wherein, The steady-state energy flow modeling of the thermal power subsystem is specifically as follows: For each node of the thermal subsystem, there are three temperatures associated with it: the supply temperature T s denotes the temperature at which hot water is injected into the load node or at which hot water flows out of the source node, the output temperature T o denotes the temperature at which hot water flows out of the load node, the recuperation temperature T r denotes the temperature of the hot water flowing out of the load node after mixing with the water of other pipes or the temperature at which low-temperature hot water flows into the source; For the thermal system, it needs to be described from two aspects of hydraulic model and thermal model; 1) Hydraulic model The hydraulic model is used to describe the relationship satisfied by the flowing hot water in the network, for each node, the flow continuity equation should be satisfied: where: m q,in , m q,out are the flow rates of water in the pipes flowing into node q and out of node q, respectively, m q is the flow rate out of node q; The flow of hot water in the pipe ij is subject to a pressure drop h due to friction ij may be represented as: h ij = κ ij m ij | m ij | (8) wherein, k ij is the resistance coefficient of the pipe, m ij is the flow rate of water in the pipe; For a closed loop composed of pipes, the pressure drop H of water in the pipe along the loop direction is 0: H = ∑h ij = 0 (9) 2) Thermal model The thermal model describes the relationship between thermal power and temperature, flow rate; For node i, its thermal power can be expressed as: Φ i = C p m i (T s,i -T o,i ) (10) wherein: C p Cp is the specific heat capacity of water, The thermal power delivered by pipe ij is expressed as: Φ ij = C p m ij (T s,i -T r,i ) (11) The temperature change of hot water flowing from node i to node j through pipe ij is expressed by equation (12): where: T a is the ambient temperature, λ is the thermal conductivity of the pipe, and L is the length of the pipe. For any node in the thermal system, the temperature change of water in the pipe before and after mixing at the node can be expressed as: ∑(m in T in )=(Σm out )T out (13) where m in , T in and m out , T out are the flow rate and temperature of water flowing into and out of the node, respectively, T in is the actual T o of the node, T out is the temperature of the water flowing into the node, and T r is the temperature of the water flowing out of the node; The power balance equation of the node in the thermal system is as follows:

6. The IES multi-objective dynamic optimization scheduling method considering multi-energy flow constraints according to claim 5, wherein, The coupling device modeling is as follows: Consider micro-CHP system, GSHP, GB and FC as coupling devices between subsystems; The mathematical models of micro-CHP system and GSHP are as follows: 1) Micro-CHP system The micro-CHP system generates electricity with natural gas as fuel, and recovers waste heat as load heating, and its mathematical model is as follows: wherein: P CHP , Φ CHP , f CHP are the electric power, the thermal power and the natural gas consumption of the CHP system, respectively, η MT is the microturbine efficiency, η l , η rec are the residual heat loss rate and the residual heat recovery efficiency of the lithium bromide unit, respectively, and COP h is the heating coefficient. 2) Ground source heat pump The ground source heat pump system is a new type of environment-friendly energy utilization system which uses rock-soil body, underground water or surface water as low-temperature heat source, consumes electric energy to transfer heat to the load for heating, and the transferred heat Φ GSHP is 4-5 times of the consumed electric power P GSHP , which can effectively reduce the heating cost. A simplified mathematical model of the ground source heat pump is adopted, i.e. a quadratic function is used to approximate the relationship between input and output, as shown in equation (16).

7. The IES multi-objective dynamic optimization scheduling method considering multi-energy flow constraints according to claim 6, wherein, The calculation of MEF based on Newton-Raphson method to obtain the steady-state energy flow distribution of IES includes: The expression of IES steady-state MEF deviation is as follows: Choose the state variables of IES as x = [θ; V; p; T] s ;T r [m] represents the state variables of the system. Taking the first derivative of equation (17), we obtain the corrected equation for solving using the Newton-Raphson method: ΔF = JΔx (18) In the formula: ΔF is the deviation vector composed of the left side of equation (17), J is the Jacobian matrix obtained by taking the first-order derivative of the right side of equation (17); Given the appropriate initial value of the state quantity, equation (18) is solved by iteration, and when the algorithm converges, the steady-state energy flow distribution of IES is obtained.

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