CN113158547A - Regional comprehensive energy system optimal configuration method considering economy and reliability - Google Patents

Regional comprehensive energy system optimal configuration method considering economy and reliability Download PDF

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CN113158547A
CN113158547A CN202110264372.5A CN202110264372A CN113158547A CN 113158547 A CN113158547 A CN 113158547A CN 202110264372 A CN202110264372 A CN 202110264372A CN 113158547 A CN113158547 A CN 113158547A
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边晓燕
史越奇
徐波
林顺富
赵健
王小宇
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Abstract

The invention relates to an economic and reliable optimization configuration method of a regional comprehensive energy system, which comprises the following steps: 1) establishing a regional comprehensive energy system model; 2) acquiring a device state transition time sequence model; 3) constructing a double-layer optimization configuration model; 4) and performing multi-objective solution on the double-layer optimization configuration model to obtain and execute a regional comprehensive energy system multi-objective optimization configuration scheme considering economic and reliability factors. Compared with the prior art, the method has the advantages of high practicability, high accuracy, high reliability quantification and the like, and economy and reliability are considered.

Description

Regional comprehensive energy system optimal configuration method considering economy and reliability
Technical Field
The invention relates to the technical field of power system planning, in particular to an optimal configuration method of a regional comprehensive energy system considering economy and reliability.
Background
The regional comprehensive energy system is located in a terminal link of energy consumption, a micro-grid is used as a main forming form, multi-energy complementation is facilitated, the energy utilization rate is improved, and optimal configuration of the regional comprehensive energy system is an important link for realizing safe, reliable and economic energy supply. In recent years, researchers at home and abroad have conducted more researches on the optimization configuration of regional integrated energy systems, however, the existing researches mainly aim at the aspects of reducing system cost, reducing carbon emission, improving the utilization rate of renewable energy sources and the like, and the researches on the reliability of the regional integrated energy systems are only in the starting stage.
In the aspect of optimal configuration of a regional integrated energy system related to the reliability problem, most of methods proposed by the existing researches adopt a traditional state enumeration method or a non-sequential Monte Carlo simulation method to quantify an expected value index of insufficient energy supply, and the reliability of a configuration scheme is improved by adding corresponding constraint conditions. The method adds the reliability constraint in the optimized configuration model, and can ensure that the generated configuration scheme meets the corresponding reliability requirement. However, considering the reliability of the system only in the constraint condition easily biases the configuration scheme to be conservative, thereby causing problems of increase of redundant equipment and excessive investment; on the other hand, the output result of the single-target optimization configuration method is single, the relation between economy and reliability cannot be reflected, and meanwhile, the reference is not provided for the decision of a planner.
The difficulty of taking reliability as an optimal configuration objective function is that the reliability quantification is realized by adopting a proper method, the reliability quantification method is mainly divided into a state enumeration method and a simulation method, and the number of evaluation states of the state enumeration method is exponentially increased along with the increase of system elements, so that the method is only suitable for a system with few fault elements; the simulation method is divided into a non-sequential Monte Carlo method and a sequential Monte Carlo method, wherein the latter method can consider the change of the system on the time sequence in the sampling process, thereby realizing the reliability quantification of the system containing the energy storage and renewable energy and other devices with strong correlation of the time sequence.
Disclosure of Invention
The present invention is directed to overcoming the above-mentioned drawbacks of the prior art and providing an optimal configuration method for regional integrated energy systems with consideration of economy and reliability.
The purpose of the invention can be realized by the following technical scheme:
an optimal configuration method of a regional integrated energy system considering economy and reliability comprises the following steps:
1) establishing a regional comprehensive energy system model: describing a regional comprehensive energy system, and respectively establishing a linear mathematical model for energy conversion equipment and energy storage equipment in the regional comprehensive energy system;
2) acquiring a device state transition time sequence model: in a regional comprehensive energy system, respectively establishing an equipment state transition time-sequence model based on a Markov process for the normal-fault-repair cyclic reciprocating process of the state change of each energy conversion equipment, and generating an equipment fault state sequence by adopting sequential Monte Carlo sampling;
3) constructing a double-layer optimization configuration model: the upper-layer planning model carries out optimization configuration according to the economic efficiency and reliability targets of the system, and the determined configuration scheme is used as the input condition of the lower-layer optimization operation model; the lower-layer optimized operation model obtains the optimal reliability and the corresponding operation cost of the system through optimized operation on the basis of the configuration scheme determined by the upper-layer planning model, and provides a basis for iterative improvement of the upper-layer planning model planning scheme;
4) performing multi-objective solution on the double-layer optimization configuration model: the upper layer planning model adopts an NSGA-II algorithm to carry out multi-target solution; the lower-layer optimization operation model takes one operation period every day, the whole simulation period is divided into a plurality of stages, a linear programming algorithm is adopted to solve the stages respectively, and finally the multi-objective optimization configuration scheme of the regional comprehensive energy system considering the economic and reliability factors is obtained and executed.
Step 1), contain energy conversion equipment, power generation facility and energy storage equipment among the regional comprehensive energy system, energy conversion equipment include combined heat and power system, gas boiler, electric boiler, absorption formula refrigerator and compression refrigerator, power generation facility include distributed photovoltaic and fan, energy storage equipment include battery, heat-retaining device and cold storage equipment, combined heat and power system through consuming natural gas and producing electric energy and heat energy, regional comprehensive energy system's heat energy is provided by combined heat and power system, gas boiler and/or electric boiler, cold energy is provided from electric energy and heat energy conversion respectively by absorption formula refrigerator and electric refrigerator, the electric energy is provided by purchasing electricity, combined heat and power system, photovoltaic and/or fan to outside.
The combined heat and power system comprises a gas turbine and a waste heat boiler, and the expression of a linearized mathematical model of the combined heat and power system is as follows:
Figure BDA0002971523610000031
wherein N isGTNumber of gas turbines installed, gammaGT,iGT(t)、
Figure BDA0002971523610000032
iGT th gas turbine respectively is consumed at time tThe rate of natural gas and the power generated,
Figure BDA0002971523610000033
for the power generation efficiency, λ, of iGT th gas turbinegasIs the heating value of natural gas, NRBIn order to increase the number of waste heat recovery boilers to be installed,
Figure BDA0002971523610000034
for the thermal power produced by the iGT th gas turbine at time t,
Figure BDA0002971523610000035
iRB th waste heat boiler.
When the demand of the heat load is larger than the maximum heat load which can be provided by the combined heat and power system, the gas boiler and the electric boiler participate in providing the heat load, and the expression of the linearized mathematical model of the gas boiler and the electric boiler is as follows:
Figure BDA0002971523610000036
wherein, γGB,iGB(t)、
Figure BDA0002971523610000037
iGB th gas boiler consumed natural gas at time t and output thermal power,
Figure BDA0002971523610000038
for the heat conversion efficiency of iGB th gas boiler,
Figure BDA0002971523610000039
iEB th electric boiler consumes electric power and outputs thermal power at the moment t respectively,
Figure BDA00029715236100000310
the conversion efficiency of iEB th electric boiler.
The expression of the linear mathematical model of the absorption refrigerator and the electric refrigerator is as follows:
Figure BDA00029715236100000311
wherein,
Figure BDA00029715236100000312
respectively the thermal power consumed and the cold power (kW) output by the iAC table absorption refrigerator at the moment t,
Figure BDA00029715236100000313
electric power consumed and cold power (kW) output by the electric refrigerator of the iEC station at time t,
Figure BDA00029715236100000314
the heat-to-cold conversion efficiency and the electricity-to-cold conversion efficiency of the iAC stage absorption chiller and the iEC stage electric chiller, respectively.
In the regional integrated energy system, the photovoltaic and the fan are equivalent to a reducible load with a negative demand, and the expression of the linearized mathematical model is as follows:
Figure BDA0002971523610000041
wherein,
Figure BDA0002971523610000042
normalized output curve values, c, of the photovoltaic and the fan at the time t, respectivelyPV,iPV、cWT,iWTThe installation capacities of the photovoltaic of the iPV station and the blower of the iWT th station,
Figure BDA0002971523610000043
actual output of the photovoltaic power of the iPV platform and the iWT fan at the moment t respectively;
in a regional comprehensive energy system, a generalized energy storage dynamic general model is adopted to model an energy storage device, energy stored in the energy storage device is used as a state variable, the charging and discharging energy power of the energy storage device is used as a control variable, and the state change in the whole operation process is obtained through mutual recursion among all periods.
The step 2) specifically comprises the following steps:
21) determining a device state x of an energy conversion device i at an initial timei(t0)；
22) Device state x according to current time periodi(t) randomly sampling according to the probability of each state transition path to determine the state x of the next time segmenti(t + Δ t), the probability of the device state transition according to different transition paths is specifically:
Figure BDA0002971523610000044
wherein, pii{. denotes the probability of an event {. at the energy conversion device i, λi、μiRespectively representing the fault rate and the repair rate of the energy conversion equipment i, wherein delta t is a time interval;
23) step 22) is performed for the next time period until the states of the energy conversion devices i for all time periods within the cycle are determined, a sequence of states of the energy conversion devices i is generated
Figure BDA0002971523610000045
24) Repeating the steps 21) to 23) for the total N energy conversion devices in the regional integrated energy system, and generating the state sequence set of all the energy conversion devices in the regional integrated energy system
Figure BDA0002971523610000046
In the step 3), the objective function of the upper layer planning model is defined by an economic objective f1And a reliability target f2Two independent parts, economic target f1The expression of (a) is:
min f1＝Cinv+Cop+Cmain
Figure BDA0002971523610000051
Figure BDA0002971523610000052
Cmain＝εCinv
wherein, CinvFor annual investment costs, CopFor the expected value of the operating cost, CmainFor equipment maintenance cost, N is the number of equipment types in the regional integrated energy system, MopOptimizing the number of run cycles, i.e. number of samples, p, of the run model for the lower layersUnit price of class s devices, nsIs the installed quantity of class s equipment, y is the planned age, m is the minimum expected capital recovery, Cj,opCalculating the annual operation cost of the sample j through a lower-layer optimized operation model, wherein epsilon is a maintenance cost coefficient;
reliability target f2The expression of (a) is:
Figure BDA0002971523610000053
wherein E isj,EENSAnd (4) obtaining the annual energy supply shortage expected value of the sample j through calculation of a lower-layer optimization operation model.
In the step 3), the objective function of the lower-layer optimization operation model comprises a system power supply insufficiency index Ej,EENSAnd an operating cost Cj,opThe two parts are specifically as follows:
min f3＝τ1Ej,EENS+τ2Cj,op
Figure BDA0002971523610000054
Figure BDA0002971523610000055
wherein, tau1、τ2The weight coefficient is the main target of minimizing the energy supply shortage of the system for embodying, and the value satisfies tau1＞＞τ2，
Figure BDA0002971523610000056
The load reduction amounts of the electric load, the heat load and the cold load at the time t, respectively, are tauelec、τheat、τcoldRespectively the weight coefficients of the reliability of the electric load, the thermal load and the cold load,
Figure BDA0002971523610000057
γgrid(t) the power and gas purchasing rate from the system to the upper-level power grid and the natural gas network at the moment t, pelec(t)、pgas(t) prices for electricity and gas purchase at time t, respectively, Δ t being a time interval;
the constraint conditions of the lower-layer optimized operation model comprise energy balance constraints of an air supply subsystem, a power supply subsystem, a heat supply subsystem and a cold supply subsystem, upper and lower limit constraints of output of each energy conversion device, power and gas purchasing rate constraints of a regional comprehensive energy system to an external power grid, equality constraints between charging and discharging energy power and state variables of each energy storage device, upper and lower limit constraints of storage capacity and charging and discharging energy power of each energy storage device and upper and lower limit constraints of reduction of user electricity, heat and cold loads.
In the step 4), solving the double-layer optimization configuration model specifically comprises the following steps:
41) inputting the cost performance parameter, NSGA-II algorithm parameter, population quantity R and operation cycle quantity M of each equipment model in the regional comprehensive energy systemopNumber of samplings MsAnd full scene data of the comprehensive energy load and renewable energy output curve;
42) generating 2R initial individuals, and correcting the individuals which do not meet the constraint condition;
43) performing M for each individualsThe subsystem state sequence is sampled and decomposed intoMopIn each operation period, based on multi-scene data, a linear programming algorithm is adopted to carry out comparison on the data MopSolving the multi-stage optimization operation problem formed by each operation period one by one, and calculating the economic and reliability indexes of each individual;
44) performing non-dominated sorting on the population by adopting a tournament method, and eliminating R non-superior individuals by an elite retention strategy;
45) carrying out genetic operation on the rest individuals, and combining the offspring and the parent into a new population with the number of 2R;
46) and (5) repeating the steps 43) -45), if the convergence condition is reached, selecting all individuals in the non-dominant set with the highest ranking as a Pareto optimal solution set, and outputting an optimal configuration result.
Compared with the prior art, the invention has the following advantages:
the invention considers the economy and the reliability of the regional comprehensive energy system simultaneously in the process of optimizing the configuration, so that the optimizing configuration result has the requirements of the economy and the reliability, and the invention has more practicability compared with the traditional method.
In the aspect of a reliability evaluation link, the reliability of the regional comprehensive energy system is quantitatively evaluated by adopting a sequential Monte Carlo method, so that the reliability of the system containing the energy storage and renewable energy and other high-sequence correlation devices can be more accurately quantified.
In addition, the economic efficiency and the reliability are taken into consideration as the objective function in the optimization configuration and are solved by adopting a multi-objective optimization algorithm, so that more diversified and refined choices can be provided for planners.
Drawings
Fig. 1 is a schematic structural diagram of a regional integrated energy system.
FIG. 2 is a diagram of a Markov process for a change in the state of a device;
FIG. 3 is a schematic flow chart of the overall method of the two-layer model.
Detailed Description
The invention is described in detail below with reference to the figures and specific embodiments.
Examples
The invention provides a regional comprehensive energy system double-layer optimization configuration method considering economy and reliability, which establishes a double-layer optimization configuration model: the upper layer model is a multi-objective optimization configuration model, the minimum net cost per year and comprehensive energy deficiency rate index of the system are taken as objective functions, and a corresponding non-dominated optimal gene sequence set is solved through an NSGA-II multi-objective genetic algorithm, so that a Pareto optimal solution set of the configuration scheme is obtained; the lower model is an optimized operation model, the configuration scheme determined by the upper model is converted into linear constraint conditions, the optimized operation of the system is realized by taking the lowest system load shedding amount and the optimal operation economy as targets, the operation reliability is quantized by adopting a sequential Monte Carlo method, and the operation cost and the reliability quantized value are fed back to the upper model. And realizing multi-objective optimization configuration of the regional comprehensive energy system through recursive iteration between the upper layer model and the lower layer model.
The method comprises the following steps:
step 1, establishing a regional comprehensive energy system model: describing a regional comprehensive energy system based on an energy hub model, and respectively establishing a linear mathematical model for energy conversion equipment and energy storage equipment in the regional comprehensive energy system;
step 2, establishing an equipment state transition time sequence model: aiming at the 'normal-fault-repair' cyclic reciprocating process of the state change of the energy conversion equipment in the regional comprehensive energy system, respectively establishing an equipment state transition time sequence model based on a Markov process; establishing a sampling generation method of an equipment fault state sequence based on the basic principle of sequential Monte Carlo sampling;
step 3, establishing a double-layer optimization configuration model: the upper-layer planning model carries out optimal configuration on equipment according to the economic efficiency and reliability targets of the system, and the determined configuration scheme is used as the input condition of the lower-layer model; the lower-layer optimized operation model obtains the optimal reliability and the corresponding operation cost of the system through optimized operation on the basis of the configuration scheme determined by the upper-layer model, and provides a basis for iterative improvement of the upper-layer model planning scheme;
step 4, establishing a multi-target solving process: the upper layer model adopts NSGA-II algorithm to carry out multi-target solution; the lower layer model divides the whole simulation period into a plurality of stages in a mode of taking one operation period every day, and solves the stages respectively by adopting a linear programming algorithm;
and 5, executing multi-objective optimization configuration of the regional comprehensive energy system with consideration of economy and reliability: carrying out computer programming on the model and the solving process established in the step; inputting basic data such as load capacity, load curve, photovoltaic, wind power output curve, cost of each device and the like of a regional comprehensive energy system; and running the program to obtain a regional comprehensive energy system multi-objective optimization configuration scheme set, and a planner can select a proper scheme from the optimization configuration scheme set according to actual requirements.
The establishing of the regional comprehensive energy system model in the step 1 specifically comprises the following steps:
the invention models a regional comprehensive energy system based on an energy hub model, and the considered energy conversion equipment comprises: the combined heat and power system comprises a combined heat and power system CHP, a gas boiler GB, an electric boiler EB, an absorption refrigerator AC and a compression refrigerator EC, and meanwhile, in order to realize the consumption of renewable energy, a certain number of distributed photovoltaic PV and fans WT are arranged in a power supply subsystem; in order to improve the economy and flexibility of the system operation, certain energy storage devices including storage batteries, heat storage devices and cold storage devices are considered to be configured in the system, and the structure of the regional comprehensive energy system considered by the invention is shown in fig. 1.
The heat source of the regional comprehensive energy system is provided by a combined heat and power unit, a gas boiler and an electric boiler together, the combined heat and power unit generates electric energy and heat energy by consuming natural gas, and the combined heat and power unit comprises a gas turbine and a waste heat boiler, and the following parts are as follows:
Figure BDA0002971523610000081
wherein N isGTThe number of gas turbines installed; gamma rayGT,i(t)、
Figure BDA00029715236100000810
Respectively, the rate at which the ith gas turbine consumes natural gas at time t (m)3H) and power generation power (kW);
Figure BDA0002971523610000082
the power generation efficiency of the ith gas turbine; lambda [ alpha ]gasIs the heating value (kWh/m) of natural gas3)；NRBIn order to increase the number of waste heat recovery boilers to be installed,
Figure BDA0002971523610000083
represents the thermal power (kW) generated by the ith gas turbine at the time t;
Figure BDA0002971523610000084
the recovery efficiency of the ith waste heat boiler is shown.
When the demand of the heat load is larger than the maximum heat load which can be provided by the combined heat and power supply, the gas boiler and the electric boiler are required to participate in providing the heat load, and the mathematical models of the two are as follows:
Figure BDA0002971523610000085
wherein, γGB,i(t)、
Figure BDA0002971523610000086
Respectively the rate of consumption of natural gas (m) at time t for the ith gas boiler3H) and thermal power output (kW);
Figure BDA0002971523610000087
the heat conversion efficiency of the ith gas boiler is obtained;
Figure BDA0002971523610000088
electric power (kW) consumed and thermal power (kW) output by the ith electric boiler at the moment t respectively;
Figure BDA0002971523610000089
the conversion efficiency of the ith electric boiler.
The cold source of the regional comprehensive energy system is converted from electric energy and heat energy by the absorption refrigerator and the electric refrigerator respectively. The mathematical models of both are as follows:
Figure BDA0002971523610000091
wherein,
Figure BDA0002971523610000092
respectively the thermal power consumed and the cold power (kW) output by the ith absorption refrigerator at the moment t;
Figure BDA0002971523610000093
electric power consumed and cold power (kW) output by the ith electric refrigerator at the moment t respectively;
Figure BDA0002971523610000094
the heat-cold conversion efficiency and the electricity-cold conversion efficiency of the ith absorption refrigerator and the electric refrigerator respectively.
In addition to purchasing electricity to the external grid and being provided by the cogeneration system, electrical energy can also be provided by configuring two distributed renewable energy sources, photovoltaic and wind turbine. The specific principles of the two will not be described in detail herein for reasons of space. The invention enables the photovoltaic and the fan to be equivalent to a load which can be reduced and has a negative demand, and the specific mathematical model is as follows:
Figure BDA0002971523610000095
wherein,
Figure BDA0002971523610000096
normalized output curve values (p.u.) of the photovoltaic and the fan at the time t respectively; c. CPV,i、cWT,iThe installation capacities (kW) of the ith photovoltaic and the fan in the system are respectively set;
Figure BDA0002971523610000097
the actual power output (kW) of the ith photovoltaic and the fan at the moment t are respectively.
In this example, a generalized energy storage dynamic general model is used to model the three energy storage devices. The model takes the energy stored in the energy storage device as a state variable, takes the charging and discharging energy power of the energy storage device as a control variable, and obtains the state change in the whole operation process through mutual recursion among all the time periods. The recurrence relation is as follows:
Figure BDA0002971523610000098
wherein Q isi(t) is the energy (kWh) stored by the energy storage device i at time t; pi,in(t) and Pi,out(t) the charging and discharging power of the energy storage device i at the moment t (kW) respectively; etai,inAnd ηi,outRespectively charging and discharging efficiency of the energy storage device i; alpha is alphaiThe self-discharging rate of the energy storage device i is obtained; Δ t is the length of time (hours) of a single session; t is the total time period number in the operation period.
The step 2 of establishing the equipment state transition time sequence model specifically comprises the following steps:
the present invention assumes that each energy conversion device in the system has two states, normal operation and fault-free operation, and each time is in one of the states, the transition change of the device state at each time can be described by a two-state markov process, as shown in fig. 2. In the figure, 0 indicates a normal operation state, and 1 indicates a fault shutdown state; λ and μ are failure rate and repair rate, respectively.
The present invention assumes that each energy conversion device has at most one state transition within a time period Δ t, and the probability of the device state transition according to different transition paths is as follows:
Figure BDA0002971523610000101
wherein, pii{. represents the probability of an event {. at device i; x is the number ofi(t) is the state variable of the device i at the time t; lambda [ alpha ]i、μiRespectively, the failure rate and the repair rate of the device i.
The sequential Monte Carlo simulation method realizes the simulation of the system time sequence change by extracting the random sequence of the system state, and the sampling method of the system state sequence adopted by the invention comprises the following steps:
21) determining the state x of a device i at an initial instanti(t0) In this example, it is assumed that all devices are in normal state at the initial time, i.e. xi(t0)＝0；
22) Device state x according to current time periodi(t) randomly sampling the probability of occurrence of each state transition path in equation (6) to determine the state x for the next time periodi(t+Δt)；
23) Step 22) is performed for the next time period until the state of the device i for all time periods in the simulation cycle is determined, thereby generating a sequence of states for the device
Figure BDA0002971523610000102
24) Performing steps 21) -23) on a total of N energy conversion devices within the regional integrated energy system to generate a state sequence set of energy conversion devices in the system
Figure BDA0002971523610000103
The establishment of the double-layer optimization configuration model in the step 3 specifically comprises the following steps:
the invention divides the optimal configuration problem of the regional comprehensive energy system into two levels: the upper layer model carries out the optimal configuration of the equipment according to the economic efficiency and reliability targets of the system, and the determined configuration scheme is used as the input condition of the lower layer model; and the lower layer model obtains the optimal reliability and the corresponding operation cost of the system through optimized operation on the basis of the configuration scheme determined by the upper layer model, and provides a basis for iterative improvement of the upper layer model planning scheme. The general structure of the model is shown in fig. 3.
31) Objective function of upper model:
the objective function of the upper layer planning model consists of an economic objective f1And a reliability target f2Two independent parts, wherein the economic target comprises the annual investment cost C of the systeminvExpected value of running cost CopAnd equipment maintenance cost CmainThree parts, specifically as follows:
minf1＝Cinv+Cop+Cmain (7)
Figure BDA0002971523610000111
Figure BDA0002971523610000112
Cmain＝εCinv (10)
wherein N is the number of equipment types in the regional integrated energy system; mopThe number of the operation cycles of the lower model, namely the number of samples; p is a radical ofiUnit price (ten thousand yuan) for the i-th equipment; n isiThe installation number of the ith type equipment; y is the planning year; m is the lowest expected capital recovery; cj,opCalculating the annual operation cost (ten thousand yuan) of the sample j through a lower-layer optimization operation model, specifically formula (15); ε is the maintenance cost factor.
Considering that the energy supply of the regional comprehensive energy system relates to various energy forms of electricity, heat and cold, the invention expands the basic principle of the expected energy shortage index EENS in the reliability evaluation of the power system to various energy forms, and the reliability targets of the invention are as follows:
Figure BDA0002971523610000113
wherein E isj,EENSThe annual under-supply expectation (kWh) for sample j is calculated from the underlying model, see equation (14).
32) Constraint conditions of the upper layer model:
because the regional comprehensive energy system needs to occupy a certain space and is limited by actual construction conditions, the number of installed equipment is limited, and therefore, the number n of different types of equipment needs to be installedkAnd (6) carrying out constraint. The constraint on the number of installations of the equipment can be expressed by the following inequality:
Figure BDA0002971523610000121
wherein,
Figure BDA0002971523610000122
representing the maximum allowed number of installations for class k devices.
33) Objective function of the underlying model:
the optimized operation of the regional integrated energy system is required to ensure that the system energy supply shortage is minimum under the condition of equipment failure, and the system energy supply shortage is better economical efficiency under the normal statej,EENSAnd an operating cost Cj,opTwo parts, and the weight value of the former should be far more than the latter, specifically as follows:
min f3＝τ1Ej,EENS+τ2Cj,op (13)
wherein, tau1、τ2The weight coefficient is taken as the main target of minimizing the energy supply shortage of the system, and the value of the weight coefficient is taken to satisfy tau1＞＞τ2。
When the regional comprehensive energy system cannot supply all the loads due to faults, the energy management system reduces the corresponding load P in a direct load control modeirThus, the system is poweredThe deficiency index may be calculated as follows:
Figure BDA0002971523610000123
wherein,
Figure BDA0002971523610000124
load reduction (kW) of the electric load, the thermal load, and the cooling load at time t, respectively; tau iselec、τheat、τcoldThe weight coefficients of the reliability of the electric load, the thermal load and the cold load respectively reflect the influence degree of different energy supply shortages on users.
Operation cost C of regional integrated energy systemj,opIncluding the electricity and gas purchase costs of the system to the external grid, as follows:
Figure BDA0002971523610000125
wherein,
Figure BDA0002971523610000126
γgrid(t) the power (kW) and gas purchase rate (m) of the system to the upper-level power grid and the natural gas network at the time t3/h)；pelec(t)、pgas(t) the prices of electricity and gas purchase at the moment t are respectively as follows: yuan/kWh, yuan/m3。
34) Constraint conditions of the lower layer model:
the various energy sources in the regional integrated energy system are collected and distributed through respective energy supply subsystems, and the energy balance constraint of the regional integrated energy system needs to be met in the operation process. According to fig. 1, the energy balance constraints of the air supply subsystem, the power supply subsystem, the heat supply subsystem and the cold supply subsystem at each time are respectively shown as formulas (16) to (19).
Figure BDA0002971523610000127
Figure BDA0002971523610000131
Figure BDA0002971523610000132
Figure BDA0002971523610000133
Wherein D isGT、DGB、DEB、DRB、DEC、DAC、DWT、DPV、DBT、DHSE、DISERespectively is a set of a gas turbine, a gas boiler, an electric boiler, a waste heat recovery boiler, an electric refrigerator, an absorption refrigerator, a fan, a photovoltaic, a storage battery, a heat storage device and a cold storage device.
The constraint of the conversion relationship between different energy sources can be represented by the following equations (16) to (19), and meanwhile, each energy conversion device needs to meet the constraint of upper and lower output limits:
0≤Pi(t)≤ci(1-xi(t)) (20)
wherein, ciRated capacity (kW) for the energy conversion device i; x is the number ofi(t) is the state of the energy conversion device i at time t.
The power and gas purchasing rate of the comprehensive energy system in each period of time to the external power grid are limited by the transmission capacity of the external power grid tie line and the natural gas tie pipeline, and the following constraints are also met:
Figure BDA0002971523610000134
Figure BDA0002971523610000135
wherein,
Figure BDA0002971523610000136
maximum transmission capacity (kVA) for regional integrated energy systems and external grid links;
Figure BDA0002971523610000137
maximum transmission rate (m) for connecting pipeline of regional integrated energy system and external natural gas system3/h)；
Figure BDA0002971523610000138
To power the power factor of the subsystem, it is assumed in this invention to be constant at 0.92 at all times.
The equation constraint between the charging and discharging energy power and the state variable of each energy storage device is shown as a formula (5). Meanwhile, the storage capacity and the charge-discharge energy power of the energy storage device need to meet the upper and lower limit constraints as follows:
Figure BDA0002971523610000141
wherein Q isi(t) represents the energy (kWh) stored in the energy storage device i at time t;
Figure BDA0002971523610000144
respectively an upper limit and a lower limit (kWh) of the capacity of the energy storage device i;
Figure BDA0002971523610000142
respectively is the upper limit (kW) of the charging and discharging energy power of the energy storage device i.
When the energy conversion device in the regional integrated energy system fails to meet the integrated energy demand of the user, a certain amount of load needs to be interrupted or reduced by the direct load control. The reduction amount of the electricity, heat and cold loads of the user should meet the upper and lower limit constraint conditions:
Figure BDA0002971523610000143
the establishment of the multi-target solving process in the step 4 specifically comprises the following steps:
the upper layer planning model is a nonlinear integer multi-objective optimization problem, an effective mathematical optimization method is not available for the problem at present, the upper layer planning model is solved by adopting an NSGA-II algorithm, the algorithm is a non-dominated sorting genetic algorithm with an elite strategy, and a group of Pareto optimal solution sets meeting conditions can be found out through limited iteration times; the lower-layer optimization operation model needs to optimize the operation mode in the whole simulation period, is a high-dimensional linear programming problem, and in order to reduce the solving difficulty, the invention divides the whole simulation period into a plurality of stages in a mode of taking one operation period every day according to the thought of optimization scheduling in the day, and adopts a linear programming algorithm to respectively solve the stages, thereby forming the multi-stage programming model comprising a plurality of sub-problems.
The solving algorithm of the model comprises the following steps:
41) inputting cost performance parameters, NSGA-II algorithm parameters, population quantity R and operation cycle number M of different equipment models in regional comprehensive energy systemopNumber of samplings MsAnd full scene data of the comprehensive energy load and renewable energy output curve;
42) generating 2R initial individuals, and correcting the individuals which do not meet the constraint condition;
43) performing M for each individualsThe subsystem state sequence is sampled and decomposed into MopOne operation period; based on multi-scene data, linear programming algorithm is adopted to pair MopSolving the multi-stage optimization operation problem formed by each operation period one by one, and calculating the economic and reliability indexes of each individual;
44) performing non-dominated sorting on the population by adopting a tournament method, and eliminating R non-superior individuals by an elite retention strategy;
45) carrying out genetic operation on the rest individuals, and combining the offspring and the parent into a new population with the number of 2R;
46) and (5) repeatedly executing the steps 43) -45), if the algorithm reaches a convergence condition, selecting all individuals in the non-dominant set with the highest ranking as a Pareto optimal solution set, and outputting an optimal configuration result.
In summary, the invention considers the economy and reliability of the regional comprehensive energy system in the process of optimizing configuration, and is beneficial to ensuring that the optimizing configuration result has the requirements of the two aspects, so that the invention has more practicability compared with the traditional method. In the aspect of a reliability evaluation link, the reliability of the regional comprehensive energy system is quantitatively evaluated by adopting a sequential Monte Carlo method, so that the reliability of the system containing the energy storage and renewable energy and other high-sequence correlation devices can be more accurately quantified. In addition, the economic efficiency and the reliability are taken into consideration as the objective function in the optimization configuration and the solution is carried out by adopting a multi-objective optimization algorithm, so that more diversified and refined choices can be provided for planners.

Claims (10)

1. An optimal configuration method of a regional integrated energy system considering economy and reliability is characterized by comprising the following steps:
1) establishing a regional comprehensive energy system model: describing a regional comprehensive energy system, and respectively establishing a linear mathematical model for energy conversion equipment and energy storage equipment in the regional comprehensive energy system;
2) acquiring a device state transition time sequence model: in a regional comprehensive energy system, respectively establishing an equipment state transition time-sequence model based on a Markov process for the normal-fault-repair cyclic reciprocating process of the state change of each energy conversion equipment, and generating an equipment fault state sequence by adopting sequential Monte Carlo sampling;
3) constructing a double-layer optimization configuration model: the upper-layer planning model carries out optimization configuration according to the economic efficiency and reliability targets of the system, and the determined configuration scheme is used as the input condition of the lower-layer optimization operation model; the lower-layer optimized operation model obtains the optimal reliability and the corresponding operation cost of the system through optimized operation on the basis of the configuration scheme determined by the upper-layer planning model, and provides a basis for iterative improvement of the upper-layer planning model planning scheme;
4) performing multi-objective solution on the double-layer optimization configuration model: the upper layer planning model adopts an NSGA-II algorithm to carry out multi-target solution; the lower-layer optimization operation model takes one operation period every day, the whole simulation period is divided into a plurality of stages, a linear programming algorithm is adopted to solve the stages respectively, and finally the multi-objective optimization configuration scheme of the regional comprehensive energy system considering the economic and reliability factors is obtained and executed.
2. The optimal configuration method for regional integrated energy system with economic and reliability taken into consideration according to claim 1, wherein in the step 1), the regional integrated energy system comprises energy conversion equipment, power generation equipment and energy storage equipment, the energy conversion equipment comprises a cogeneration system, a gas boiler, an electric boiler, an absorption refrigerator and a compression refrigerator, the power generation equipment comprises distributed photovoltaic and fans, the energy storage equipment comprises a storage battery, a heat storage equipment and a cold storage equipment, the cogeneration system generates electric energy and heat energy by consuming natural gas, the heat energy of the regional integrated energy system is provided by the cogeneration system, the gas boiler and/or the electric boiler, the cold energy is provided by the absorption refrigerator and the electric refrigerator respectively through conversion of the electric energy and the heat energy, and the electric energy is provided by purchasing electricity from an external power grid, Cogeneration systems, photovoltaics and/or wind turbines.
3. The optimal configuration method for the regional integrated energy system considering the economy and the reliability as claimed in claim 2, wherein the cogeneration system comprises a gas turbine and a waste heat boiler, and the expression of the linearized mathematical model is as follows:
Figure FDA0002971523600000021
wherein N isGTNumber of gas turbines installed, gammaGT,iGT(t)、
Figure FDA0002971523600000022
The rate at which the iGT th gas turbine consumes natural gas at time t and the power generated,
Figure FDA0002971523600000023
for the power generation efficiency, λ, of iGT th gas turbinegasIs the heating value of natural gas, NRBIn order to increase the number of waste heat recovery boilers to be installed,
Figure FDA0002971523600000024
for the thermal power produced by the iGT th gas turbine at time t,
Figure FDA0002971523600000025
iRB th waste heat boiler.
4. The optimal configuration method for the regional integrated energy system considering the economy and the reliability as claimed in claim 2, wherein when the demand of the heat load is greater than the maximum heat load which can be provided by the cogeneration system, the gas boiler and the electric boiler participate in providing the heat load, and the expression of the linearized mathematical model of the gas boiler and the electric boiler is as follows:
Figure FDA0002971523600000026
wherein, γGB,iGB(t)、
Figure FDA0002971523600000027
iGB th gas boiler consumed natural gas at time t and output thermal power,
Figure FDA0002971523600000028
for the heat conversion efficiency of iGB th gas boiler,
Figure FDA0002971523600000029
iEB th electric boiler consumes electric power and outputs thermal power at the moment t respectively,
Figure FDA00029715236000000210
the conversion efficiency of iEB th electric boiler.
5. The optimal configuration method of the regional integrated energy system considering the economy and the reliability as claimed in claim 2, wherein the expression of the linearized mathematical models of the absorption chiller and the electric chiller is as follows:
Figure FDA00029715236000000211
wherein,
Figure FDA00029715236000000212
respectively the thermal power consumed and the cold power (kW) output by the iAC table absorption refrigerator at the moment t,
Figure FDA0002971523600000031
electric power consumed and cold power (kW) output by the electric refrigerator of the iEC station at time t,
Figure FDA0002971523600000032
the heat-to-cold conversion efficiency and the electricity-to-cold conversion efficiency of the iAC stage absorption chiller and the iEC stage electric chiller, respectively.
6. The method as claimed in claim 2, wherein the regional integrated energy system is configured to optimize the regional integrated energy system with consideration of economy and reliability, wherein the regional integrated energy system is configured to have a reduced load with a negative demand for photovoltaic and wind turbine equivalent, and the expression of the linearized mathematical model is:
Figure FDA0002971523600000033
wherein,
Figure FDA0002971523600000034
normalized output curve values, c, of the photovoltaic and the fan at the time t, respectivelyPV,iPV、cWT,iWTThe installation capacities of the photovoltaic of the iPV station and the blower of the iWT th station,
Figure FDA0002971523600000035
actual output of the photovoltaic power of the iPV platform and the iWT fan at the moment t respectively;
in a regional comprehensive energy system, a generalized energy storage dynamic general model is adopted to model an energy storage device, energy stored in the energy storage device is used as a state variable, the charging and discharging energy power of the energy storage device is used as a control variable, and the state change in the whole operation process is obtained through mutual recursion among all periods.
7. The optimal configuration method for the regional integrated energy system with consideration of economy and reliability as claimed in claim 1, wherein the step 2) specifically comprises the following steps:
21) determining a device state x of an energy conversion device i at an initial timei(t0)；
22) Device state x according to current time periodi(t) randomly sampling according to the probability of each state transition path to determine the state x of the next time segmenti(t + Δ t), the probability of the device state transition according to different transition paths is specifically:
Figure FDA0002971523600000036
wherein, pii{. denotes the probability of an event {. at the energy conversion device i, λi、μiRespectively the failure rate and the repair rate of the energy conversion device i,Δ t is the time interval;
23) step 22) is performed for the next time period until the states of the energy conversion devices i for all time periods within the cycle are determined, a sequence of states of the energy conversion devices i is generated
Figure FDA0002971523600000037
24) Repeating the steps 21) to 23) for the total N energy conversion devices in the regional integrated energy system, and generating the state sequence set of all the energy conversion devices in the regional integrated energy system
Figure FDA0002971523600000041
8. The method as claimed in claim 1, wherein the objective function of the upper layer planning model in step 3) is derived from an economic objective f1And a reliability target f2Two independent parts, economic target f1The expression of (a) is:
min f1＝Cinv+Cop+Cmain
Figure FDA0002971523600000042
Figure FDA0002971523600000043
Cmain＝εCinv
wherein, CinvFor annual investment costs, CopFor the expected value of the operating cost, CmainFor equipment maintenance cost, N is the number of equipment types in the regional integrated energy system, MopOptimizing the number of run cycles, i.e. number of samples, p, of the run model for the lower layersUnit price of class s devices, nsIs the installed quantity of class s equipment, y is the planned age, m is the minimum expected capital recovery, Cj,opCalculating the annual operation cost of the sample j through a lower-layer optimized operation model, wherein epsilon is a maintenance cost coefficient;
reliability target f2The expression of (a) is:
Figure FDA0002971523600000044
wherein E isj,EENSAnd (4) obtaining the annual energy supply shortage expected value of the sample j through calculation of a lower-layer optimization operation model.
9. The method as claimed in claim 8, wherein the objective function of the lower-layer optimal operation model in step 3) includes a system under-power indicator Ej,EENSAnd an operating cost Cj,opThe two parts are specifically as follows:
min f3＝τ1Ej,EENS+τ2Cj,op
Figure FDA0002971523600000045
Figure FDA0002971523600000046
wherein, tau1、τ2The weight coefficient is the main target of minimizing the energy supply shortage of the system for embodying, and the value satisfies tau1＞＞τ2，
Figure FDA0002971523600000051
The load reduction amounts of the electric load, the heat load and the cold load at the time t, respectively, are tauelec、τheat、τcoldRespectively electric, heat and cold loadsThe weight coefficient of the reliability is determined,
Figure FDA0002971523600000052
γgrid(t) the power and gas purchasing rate from the system to the upper-level power grid and the natural gas network at the moment t, pelec(t)、pgas(t) prices for electricity and gas purchase at time t, respectively, Δ t being a time interval;
the constraint conditions of the lower-layer optimized operation model comprise energy balance constraints of an air supply subsystem, a power supply subsystem, a heat supply subsystem and a cold supply subsystem, upper and lower limit constraints of output of each energy conversion device, power and gas purchasing rate constraints of a regional comprehensive energy system to an external power grid, equality constraints between charging and discharging energy power and state variables of each energy storage device, upper and lower limit constraints of storage capacity and charging and discharging energy power of each energy storage device and upper and lower limit constraints of reduction of user electricity, heat and cold loads.
10. The method as claimed in claim 8, wherein the step 4) of solving the two-layer optimal configuration model specifically includes the following steps:
41) inputting the cost performance parameter, NSGA-II algorithm parameter, population quantity R and operation cycle quantity M of each equipment model in the regional comprehensive energy systemopNumber of samplings MsAnd full scene data of the comprehensive energy load and renewable energy output curve;
42) generating 2R initial individuals, and correcting the individuals which do not meet the constraint condition;
43) performing M for each individualsThe subsystem state sequence is sampled and decomposed into MopIn each operation period, based on multi-scene data, a linear programming algorithm is adopted to carry out comparison on the data MopSolving the multi-stage optimization operation problem formed by each operation period one by one, and calculating the economic and reliability indexes of each individual;
44) performing non-dominated sorting on the population by adopting a tournament method, and eliminating R non-superior individuals by an elite retention strategy;
45) carrying out genetic operation on the rest individuals, and combining the offspring and the parent into a new population with the number of 2R;
46) and (5) repeating the steps 43) -45), if the convergence condition is reached, selecting all individuals in the non-dominant set with the highest ranking as a Pareto optimal solution set, and outputting an optimal configuration result.
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Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN115063255A (en) * 2022-05-31 2022-09-16 华南理工大学 Operation and maintenance resource optimal allocation method, device and system for offshore wind farm
CN115358469A (en) * 2022-08-23 2022-11-18 北京化工大学 Method for obtaining optimal laying scheme of urban traffic integrated sensing facility

Citations (10)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN104218620A (en) * 2014-09-26 2014-12-17 国家电网公司 Active power distribution network reliability analysis method based on pseudo sequential Monte Carlo simulation
CN109740827A (en) * 2019-02-14 2019-05-10 华北电力大学 A kind of regional complex energy system planning optimization method based on dual-layer optimization
CN110350527A (en) * 2019-07-15 2019-10-18 国网冀北电力有限公司唐山供电公司 A kind of increment power distribution network dual-layer optimization configuration method containing distributed generation resource
CN110417006A (en) * 2019-07-24 2019-11-05 三峡大学 Consider the integrated energy system Multiple Time Scales energy dispatching method of multipotency collaboration optimization
CN110544017A (en) * 2019-08-12 2019-12-06 上海交通大学 Energy system reliability assessment method considering thermal inertia and energy network constraint
CN111291963A (en) * 2019-12-30 2020-06-16 天津大学 Park comprehensive energy system planning method for coordinating economy and reliability
CN111445090A (en) * 2020-04-21 2020-07-24 清华大学 Double-layer planning method for off-grid type comprehensive energy system
CN111969592A (en) * 2020-07-24 2020-11-20 南昌大学 Comprehensive energy system double-layer planning method based on user satisfaction and demand response
CN112421642A (en) * 2020-10-28 2021-02-26 国家电网有限公司 IES (integrated energy System) reliability assessment method and system
CN112446546A (en) * 2020-12-02 2021-03-05 国网辽宁省电力有限公司技能培训中心 Comprehensive energy system two-stage optimal configuration method considering energy reliability

Patent Citations (10)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN104218620A (en) * 2014-09-26 2014-12-17 国家电网公司 Active power distribution network reliability analysis method based on pseudo sequential Monte Carlo simulation
CN109740827A (en) * 2019-02-14 2019-05-10 华北电力大学 A kind of regional complex energy system planning optimization method based on dual-layer optimization
CN110350527A (en) * 2019-07-15 2019-10-18 国网冀北电力有限公司唐山供电公司 A kind of increment power distribution network dual-layer optimization configuration method containing distributed generation resource
CN110417006A (en) * 2019-07-24 2019-11-05 三峡大学 Consider the integrated energy system Multiple Time Scales energy dispatching method of multipotency collaboration optimization
CN110544017A (en) * 2019-08-12 2019-12-06 上海交通大学 Energy system reliability assessment method considering thermal inertia and energy network constraint
CN111291963A (en) * 2019-12-30 2020-06-16 天津大学 Park comprehensive energy system planning method for coordinating economy and reliability
CN111445090A (en) * 2020-04-21 2020-07-24 清华大学 Double-layer planning method for off-grid type comprehensive energy system
CN111969592A (en) * 2020-07-24 2020-11-20 南昌大学 Comprehensive energy system double-layer planning method based on user satisfaction and demand response
CN112421642A (en) * 2020-10-28 2021-02-26 国家电网有限公司 IES (integrated energy System) reliability assessment method and system
CN112446546A (en) * 2020-12-02 2021-03-05 国网辽宁省电力有限公司技能培训中心 Comprehensive energy system two-stage optimal configuration method considering energy reliability

Non-Patent Citations (2)

* Cited by examiner, † Cited by third party
Title
R. LI, B. CHEN ，X. BAI: "A Bi-Level Model for DG and ESS Integrated Planning in Distribution System", 《2020 IEEE 4TH CONFERENCE ON ENERGY INTERNET AND ENERGY SYSTEM INTEGRATION (EI2), 2020》 *
边晓燕，丁炀，买坤，朱兰，贾庆宇，李东东: "低风速分散式风电并入配电网的双层电压优化模型", 《太阳能学报》 *

Cited By (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN115063255A (en) * 2022-05-31 2022-09-16 华南理工大学 Operation and maintenance resource optimal allocation method, device and system for offshore wind farm
CN115358469A (en) * 2022-08-23 2022-11-18 北京化工大学 Method for obtaining optimal laying scheme of urban traffic integrated sensing facility
CN115358469B (en) * 2022-08-23 2023-04-07 北京化工大学 Method for obtaining optimal laying scheme of urban traffic integrated sensing facility

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