CN110705070A - Multi-stage rolling optimization planning method for distributed energy system - Google Patents
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Abstract
The invention relates to a multi-stage rolling optimization planning method for a distributed energy system, which comprises the following steps: s1, establishing an internal and external thermoelectric decoupling simulation evaluation model, and establishing an iterative optimization operation flow of the simulation evaluation model; s101, establishing an electric and thermal balance equation; s102, calculating output power of each device and transmission power among buses according to an outer-layer bus balance equation and a scheduling strategy, S103, inputting the power/heat power of each bus exchange determined by an outer layer into an inner-layer electric and heat system model S104 in the step S101, and judging whether the change of the internal network loss value of each bus before and after calculation is smaller than an allowable error range; s105, outputting the system state quantity as a simulation result; s2, establishing an intelligent optimization model; and S3, performing rolling optimization solution on the simulation evaluation model and the intelligent optimization model. The invention realizes the optimized configuration of the distributed energy system and improves the precision and the expandability of the site selection and the volume determination of the distributed energy system.
Description
Technical Field
The invention belongs to the technical field of distributed energy planning, and particularly relates to a multi-stage rolling optimization planning method for a distributed energy system.
Background
A large number of distributed power sources, distributed energy storage, electric automobiles, combined cooling heating and power supplies and the like are connected into an active power distribution network, new challenges are provided for the power distribution network, and the energy internet provides an important technical choice for meeting the challenges. The energy internet is used as an energy complex which takes electric power as a center, is coupled by multiple energy sources and is communicated by multiple networks, has the characteristics of transverse coupling of various forms of energy sources such as cold, heat, electricity and gas and longitudinal communication of multiple links such as energy production, conversion, transmission and consumption, breaks through the existing independent energy supply mode of electricity, heat and gas, and becomes a modern energy development and utilization mode for improving the comprehensive energy efficiency.
The energy Internet breaks the asynchronism of energy production and consumption by means of key technologies such as distributed power supplies, energy storage and the like, energy is decoupled in time and space to achieve free sharing of energy, and the complexity of the energy Internet also puts higher requirements on planning and design. On one hand, the diversity of energy types and equipment types enables a system configuration scheme to be more flexible and changeable, and the reasonability of the scheme directly influences the energy efficiency and the economy of the system. On the other hand, the characteristics of the load side have more obvious influence on system planning, the reproducibility of system design is low, and higher requirements are provided for the universality and the effectiveness of the planning method.
Equipment and system modeling are the basis of energy Internet planning research, but the existing method usually does not distinguish the difference of the characteristics of a thermal system and an electric system and adopts a unified modeling method; or the network relationship and the construction time sequence are not considered, simplification processing is carried out according to the energy flow relationship, the difference of the transmission characteristics of electricity and heat and the network loss can not be effectively reflected, and the accuracy requirement of the model is difficult to meet for a distributed energy system comprising a complex network.
The composition structure relationship of a typical distributed energy system is shown in fig. 1, and the system comprises a fan, a photovoltaic unit, an energy storage unit, a cogeneration unit, a gas boiler and other equipment. The wind turbine, the photovoltaic and the storage battery are connected with an alternating current bus through respective one-way/two-way converters, the generator is connected to the alternating current bus, and the alternating current bus is connected to a power distribution network through a transformer. The generator generates electricity and outputs heat to supply heat load, and when the heat generated by the generator is insufficient, the residual heat is supplemented by the gas boiler.
The single-layer bus-type structure mainly describes the connection mode and the coupling relation of main equipment, and clearly describes the transfer relation of energy among different media. The structure can independently model different power supplies, heat sources, energy storage devices and cold/heat/electricity conversion devices, and can simplify a column writing system balance equation and equipment constraint conditions under the condition of not considering the transmission characteristics of a network management. However, only the power grid is simplified into a single power bus, and the heat supply network is simplified into a single heat bus, so that the difference of the transmission characteristics of electricity and heat and the network loss cannot be effectively reflected, and the accuracy requirement of the model is difficult to meet for a distributed energy system comprising a complex network.
Therefore, based on the problems, the distributed energy system multi-stage rolling optimization planning method for realizing the optimal configuration of the distributed energy system and improving the accuracy and expandability of the site selection and volume determination of the distributed energy system is provided, and has important practical significance.
Disclosure of Invention
The invention aims to overcome the defects of the prior art and provide a distributed energy system multi-stage rolling optimization planning method which realizes the optimization configuration of a distributed energy system and improves the precision and expandability of the location and capacity of the distributed energy system.
The technical problem to be solved by the invention is realized by adopting the following technical scheme:
a multi-stage rolling optimization planning method for a distributed energy system comprises the following steps:
s1, establishing an internal and external thermoelectric decoupling simulation evaluation model, and establishing an iterative optimization operation flow of the simulation evaluation model;
s101, establishing an electric and thermal balance equation;
establishing an outer layer bus solving model:
the balance equation of the electric bus is formula (1):
Pgrid+PPV+PWT+Ppgu+LE+PES+PEC+Ploss＝0 (1)
wherein, PgridRepresenting the grid exchange power, PPVRepresenting the photovoltaic power generation power, PWTRepresenting the power generated by the fan, PpguRepresents the output power of the cogeneration unit, LERepresenting the electrical load, PESRepresenting stored energy exchange power, PECIndicating the refrigerator power, PlossRepresenting the internal network loss value, P, of the electrical busbarlossIs 0; in the balance equation of the electric bus, the power flow is positive to the electric bus, and the outflow electric bus is negative;
the equilibrium equation for the thermal busbar is formula (2):
QPGUηWH+Qboiler+Lheat+QES，heat+Qloss，heat＝0 (2)
wherein Q isPGUWaste heat of cogeneration unitAmount ηWHFor waste heat recovery efficiency, QboilerFor heat supply to gas-fired boilers, LheatFor thermal load, QES，heatFor the power of the heat storage device, Qloss，heatThe value is the internal network loss value of the thermal bus;
establishing an inner layer bus solving model:
selecting a power flow calculation model of the power system from the calculation model of the inner layer bus, and establishing a node power balance equation (3) and an inequality constraint (4) of three variables of X, mu and P of the power network:
f(X，μ，P)＝0 (3)
hmin≤H(X，μ，P)hmax(4)
wherein: x represents the state variables of the system, such as the voltage amplitude and phase angle of the nodes, μ is the control variable or part of the optimization variable in the optimization problem, and P is the initial power injection by each device (e.g., P in equation 1)grid、PPV、PWT、Ppgu、LE、PES、PEC) Determined node power injection vector, hmin、hmaxRespectively, the system maximum value and the system minimum value of the variables X, mu and P; calculating the power flow distribution and network loss of the inner-layer bus according to the formula (3-4);
s102, calculating output power of each device and transmission power between buses according to an outer bus balance equation and a scheduling strategy, wherein the scheduling strategy can select a fixed strategy or an optimization strategy according to needs;
it should be noted that the scheduling policy may generally select a fixed policy or an optimization policy, where the optimization policy may be further divided into static optimization and dynamic optimization; the fixed strategy makes an operation rule according to the priority of equipment drawn up in advance, and the priority does not change along with the operation environment of the system; the static optimization determines the priority and the operation mode of each device according to the operation cost of each device under the operation environment of the system at the current time or time period; the dynamic optimization considers the operation cost in one scheduling period (comprising a plurality of time intervals), and optimizes the system operation by taking the highest total income or the lowest total cost in the scheduling period as a target. Because the planning model is focused on, the internal operation strategy can be selected according to the requirement of the distributed energy construction time sequence, and the fixed strategy or the optimization strategy can be used for calculating and determining the power of each device, so that the application of the method is not influenced.
S103, inputting each bus exchange electric/thermal power determined at the outer layer into the electric and thermal system model formula (3-4) at the inner layer in the step S101, respectively carrying out simulation according to the selected electric and thermal system time scale (hourly, weekly, monthly and quarterly), and calculating the energy flow relation and the network loss;
s104, judging whether the change of the internal network loss value of each bus before and after calculation is smaller than an allowable error range, and if so, turning to the step S105; if not, outputting the internal network loss value as the transmission loss of each bus, and turning to the step S102;
s105, stopping iteration, and outputting the system state quantity as a simulation result;
s2, establishing an intelligent optimization model, dividing into three stages of an early stage, a middle stage and a later stage according to the fact that the load increase P of the user side is unequal to the capacity increase W of the distributed energy system, and constructing different multi-objective functions, wherein the early stage mainly takes the minimum net present value M as a target, the middle stage mainly takes the highest cost benefit F as a target, and the later stage mainly takes the minimum net present value M and the highest cost benefit F as targets, as shown in the following formula:
wherein, the calculation formula of the net present value M is shown as formula (6):
wherein, CNPVTo net present value, Cann，tIs the annual average cash flow, KCRF(r，Tpro) Is the capital recovery factor for the project cycle, is used to calculate the present value of the annual average cash flow, r is the interest rate, TproIs a project period, KCRFIs represented by equation (7):
the calculation formula of the cost gain F is shown as the formula (8):
Cann，t＝Cann，cap+Cann，rep+Cann，om+Cann，ele+Cann，bas-Bsel-Bsub(8)
wherein, Cann，capFor annual cost, Cann，repFor annual replacement costs, Cann，omFor annual maintenance cost, Cann，eleAnnual electricity charge cost, Cann，basFor annual basic electricity charge cost, BselFor annual electric sales income, BsubEarning for subsidy; wherein, annual electric charge cost Cann，eleAnnual electricity sales income BselAnd subsidy income BsubRelated to system operation, calculation needs to be carried out by combining a simulation result of 8760 hours;
annual capital cost Cann，capThe calculation formula is shown in formula (9):
Cann，cap＝Ccap.KCRF(r，Tpro) (9)
wherein, CcapInitial capital costs for all equipment;
annual replacement cost Cann，repThe replacement cost of each element of the system in the whole period of the project is subtracted by the residual value at the end of the project, and the calculation formula is shown as the formula (10):
wherein, CrepFor a single replacement cost, TcomThe life cycle of the elements, T, varies with the particular operating conditions of the stored energysurFor the remaining life of the element at the end of the project, KSF(r，Tcom) Paying fund factor, K, for a component cycleSF(r，Tpro) Paying fund factor for project period, frepCorrecting factors for capital recovery coefficients for partitioning across termsDifferent capital recovery stages resulting from component replacement within the target cycle;
annual operation and maintenance cost Cann，omThe statistical data of the operation and maintenance cost of the system in the last year is adopted for the cost required by the system in a good operation state.
Annual electricity charge cost Cann，eleFor representing the electricity consumption cost of the distributed energy system actually purchasing electricity from the power grid, the calculation formula is shown as an equation (11):
wherein, Wpur，iThe amount of electricity purchased to the grid for the ith hour, cpur，iElectricity prices are purchased for the ith hour, different electricity prices exist in different periods of electricity consumption peak, flat section and low valley of provinces in China, and electricity charges of 8760 hours of electricity in the whole year need to be added according to the operation condition of the system;
annual basic electricity charge cost Cann，basThe method is used for representing the basic capacity electric charge paid when a large industrial distributed energy system in China adopts two electricity prices, and is calculated according to the maximum load demand standard in the month, and the formula (12) is shown as follows:
wherein, Pmax，jAverage maximum load capacity of 15 minutes for peak power consumption in the month of jbasBasic electricity fee price collected monthly from the two electricity prices;
annual electricity sales income BselThe calculation formula is shown in equation (13) for representing the profit when the distributed energy system sells the remaining power to the power grid on the internet:
wherein, Wsel，iThe amount of electricity sold to the grid for the ith hour, cselBuybacking the electricity price for the power grid;
subsidy income BsubThe method is characterized in that investment subsidies or electric quantity subsidies which are possibly adopted for energy storage application in the future are temporarily not considered for subsiding the generated energy of the distributed power supply;
s3, performing rolling optimization solution on the simulation evaluation model and the intelligent optimization model, wherein the rolling optimization solution method comprises the following steps:
s301, setting the composition equipment and structure parameters, equipment model parameters and genetic algorithm parameters of the distributed energy system;
s302, an intelligent optimization module generates an initial population, and transmits a numerical value corresponding to an initial population individual as a system variable to a simulation evaluation module;
s303, decoupling and iterating the simulation evaluation module according to the method of the step S1 to obtain a simulation result, simultaneously calculating corresponding evaluation indexes, namely calculating an optimized target value according to different targets set at different stages of a front stage, a middle stage and a later stage by using an equation (5-13), and transmitting the calculated evaluation indexes to the intelligent optimization module;
s304, the intelligent optimization module determines numerical values of individual fitness of the population according to various evaluation indexes, updates individual population according to the individual fitness, and transmits the numerical values corresponding to new individuals to the simulation evaluation module as system variables;
and S305, continuously performing interactive calculation among the simulation evaluation module and the intelligent optimization module until the genetic algorithm in the intelligent optimization module reaches a termination condition, outputting an optimization planning result, and otherwise, repeating the steps S303-S304.
The invention has the advantages and positive effects that:
the invention provides an expanded double-layer bus type structure as a basis, the structure considers the complex energy flow relation of electricity, heat, cold and the like in the bus, takes the construction time sequence of a distributed power supply and a user side load into consideration, simultaneously reserves the characteristic description of the bus type structure, and adopts a simulation evaluation-intelligent optimization double-module structure to construct a distributed energy system multi-stage rolling optimization planning model; the model not only guarantees the precision requirement of each system, but also simplifies the calculation and analysis processes, considers the construction time sequence of the distributed energy system and the actual energy load, effectively reduces the dimensionality and complexity of system simulation by combining decoupling iteration, can effectively support the joint optimization planning of the energy Internet, and improves the adaptability of the distributed energy construction popularization and application.
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The technical solutions of the present invention will be described in further detail below with reference to the accompanying drawings and examples, but it should be understood that these drawings are designed for illustrative purposes only and thus do not limit the scope of the present invention. Furthermore, unless otherwise indicated, the drawings are intended to be illustrative of the structural configurations described herein and are not necessarily drawn to scale.
Fig. 1 is a schematic structural diagram of a typical distributed energy system provided in the background of the present invention;
Detailed Description
First, it should be noted that the specific structures, features, advantages, etc. of the present invention will be specifically described below by way of example, but all the descriptions are for illustrative purposes only and should not be construed as limiting the present invention in any way. Furthermore, any individual technical features described or implicit in the embodiments mentioned herein may still be continued in any combination or subtraction between these technical features (or their equivalents) to obtain still further embodiments of the invention that may not be mentioned directly herein.
It should be noted that the embodiments and features of the embodiments in the present application may be combined with each other without conflict.
A multi-stage rolling optimization planning method for a distributed energy system comprises the following steps:
s1, establishing an internal and external thermoelectric decoupling simulation evaluation model, and establishing an iterative optimization operation flow of the simulation evaluation model;
s101, establishing an electric and thermal balance equation;
establishing an outer layer bus solving model:
the balance equation of the electric bus is formula (1):
Pgrid+PPV+PWT+Ppgu+LE+PES+PEC+Ploss＝0 (1)
wherein, PgridRepresenting the grid exchange power, PPVRepresenting the photovoltaic power generation power, PWTRepresenting the power generated by the fan, PpguRepresents the output power of the cogeneration unit, LERepresenting the electrical load, PESRepresenting stored energy exchange power, PECIndicating the refrigerator power, PlossRepresenting the internal network loss value, P, of the electrical busbarlossIs 0; in the balance equation of the electric bus, the power flow is positive to the electric bus, and the outflow electric bus is negative;
the equilibrium equation for the thermal busbar is formula (2):
QPGUηWn+Qboiler+Lheat+QES，heat+Qloss，heat＝0 (2)
wherein Q isPGUWaste heat quantity of the cogeneration unit etaWHFor waste heat recovery efficiency, QboilerFor heat supply to gas-fired boilers, LheatFor thermal load, QES，heatFor the power of the heat storage device, Qloss，heatThe value is the internal network loss value of the thermal bus;
establishing an inner layer bus solving model:
selecting a power flow calculation model of the power system from the calculation model of the inner layer bus, and establishing a node power balance equation (3) and an inequality constraint (4) of three variables of X, mu and P of the power network:
f(X，μ，P)＝0 (3)
hmin≤H(X，μ，P)hmax(4)
wherein: x represents the state variables of the system, such as the voltage amplitude and phase angle of the nodes, μ is the control variable or part of the optimization variable in the optimization problem, and P is the initial power injection by each device (e.g., P in equation 1)grid、PPV、PWT、Ppgu、LE、PES、PEC) Determined node power injection vector, hmin、hmaxRespectively, the system maximum value and the system minimum value of the variables X, mu and P; calculating the power flow distribution of the inner layer bus according to the formula (3-4)Network loss;
s102, calculating output power of each device and transmission power between buses according to an outer bus balance equation and a scheduling strategy, wherein the scheduling strategy can select a fixed strategy or an optimization strategy according to needs;
it should be noted that the scheduling policy may generally select a fixed policy or an optimization policy, where the optimization policy may be further divided into static optimization and dynamic optimization; the fixed strategy makes an operation rule according to the priority of equipment drawn up in advance, and the priority does not change along with the operation environment of the system; the static optimization determines the priority and the operation mode of each device according to the operation cost of each device under the operation environment of the system at the current time or time period; the dynamic optimization considers the operation cost in one scheduling period (comprising a plurality of time intervals), and optimizes the system operation by taking the highest total income or the lowest total cost in the scheduling period as a target. Because the planning model is focused on, the internal operation strategy can be selected according to the requirement of the distributed energy construction time sequence, and the fixed strategy or the optimization strategy can be used for calculating and determining the power of each device, so that the application of the method is not influenced.
S103, inputting each bus exchange electric/thermal power determined at the outer layer into the electric and thermal system model formula (3-4) at the inner layer in the step S101, respectively carrying out simulation according to the selected electric and thermal system time scale (hourly, weekly, monthly and quarterly), and calculating the energy flow relation and the network loss;
s104, judging whether the change of the internal network loss value of each bus before and after calculation is smaller than an allowable error range, and if so, turning to the step S105; if not, outputting the internal network loss value as the transmission loss of each bus, and turning to the step S102;
s105, stopping iteration, and outputting the system state quantity as a simulation result;
s2, establishing an intelligent optimization model, dividing into three stages of an early stage, a middle stage and a later stage according to the fact that the load increase P of the user side is unequal to the capacity increase W of the distributed energy system, and constructing different multi-objective functions, wherein the early stage mainly takes the minimum net present value M as a target, the middle stage mainly takes the highest cost benefit F as a target, and the later stage mainly takes the minimum net present value M and the highest cost benefit F as targets, as shown in the following formula:
wherein, the calculation formula of the net present value M is shown as formula (6):
wherein, CNPVTo net present value, Cann，tIs the annual average cash flow, KCRF(r，Tpro) Is the capital recovery factor for the project cycle, is used to calculate the present value of the annual average cash flow, r is the interest rate, TproIs a project period, KCRFIs represented by equation (7):
the calculation formula of the cost gain F is shown as the formula (8):
Cann，t＝Cann，cap+Cann，rep+Cann，om+Cann，ele+Cann，bas-Bsel-Bsub(8)
wherein, Cann，capFor annual cost, Cann，repFor annual replacement costs, Cann，omFor annual maintenance cost, Cann，eleAnnual electricity charge cost, Cann，basFor annual basic electricity charge cost, BselFor annual electric sales income, BsubEarning for subsidy; wherein, annual electric charge cost Cann，eleAnnual electricity sales income BselAnd subsidy income BsubRelated to system operation, calculation needs to be carried out by combining a simulation result of 8760 hours;
annual capital cost Cann，capThe calculation formula is shown in formula (9):
Cann，cap＝Ccap.KCRF(r，Tpro) (9)
wherein，CcapInitial capital costs for all equipment;
annual replacement cost Cann，repThe replacement cost of each element of the system in the whole period of the project is subtracted by the residual value at the end of the project, and the calculation formula is shown as the formula (10):
wherein, CrepFor a single replacement cost, TcomThe life cycle of the elements, T, varies with the particular operating conditions of the stored energysurFor the remaining life of the element at the end of the project, KSF(r，Tcom) Paying fund factor, K, for a component cycleSF(r，Tpro) Paying fund factor for project period, frepCorrecting factors for capital recovery coefficients, which are used for dividing different capital recovery stages generated by element replacement in the whole project period;
annual operation and maintenance cost Cann，omThe statistical data of the operation and maintenance cost of the system in the last year is adopted for the cost required by the system in a good operation state.
Annual electricity charge cost Cann，eleFor representing the electricity consumption cost of the distributed energy system actually purchasing electricity from the power grid, the calculation formula is shown as an equation (11):
wherein, Wpur，iThe amount of electricity purchased to the grid for the ith hour, cpur，iElectricity prices are purchased for the ith hour, different electricity prices exist in different periods of electricity consumption peak, flat section and low valley of provinces in China, and electricity charges of 8760 hours of electricity in the whole year need to be added according to the operation condition of the system;
annual basic electricity charge cost Cann，basThe method is used for representing the basic capacity electric charge paid when a large industrial distributed energy system in China adopts two power rates, and is calculated according to the standard of the maximum load demand in the month, as shown in a formula (12)：
Wherein, Pmax，jAverage maximum load capacity of 15 minutes for peak power consumption in the month of jbasBasic electricity fee price collected monthly from the two electricity prices;
annual electricity sales income BselThe calculation formula is shown in equation (13) for representing the profit when the distributed energy system sells the remaining power to the power grid on the internet:
wherein, Wsel，iThe amount of electricity sold to the grid for the ith hour, cselBuybacking the electricity price for the power grid;
subsidy income BsubThe method is characterized in that investment subsidies or electric quantity subsidies which are possibly adopted for energy storage application in the future are temporarily not considered for subsiding the generated energy of the distributed power supply;
s3, performing rolling optimization solution on the simulation evaluation model and the intelligent optimization model, wherein the rolling optimization solution method comprises the following steps:
s301, setting the composition equipment and structure parameters, equipment model parameters and genetic algorithm parameters of the distributed energy system;
s302, an intelligent optimization module generates an initial population, and transmits a numerical value corresponding to an initial population individual as a system variable to a simulation evaluation module;
s303, decoupling and iterating the simulation evaluation module according to the method of the step S1 to obtain a simulation result, simultaneously calculating corresponding evaluation indexes, namely calculating an optimized target value according to different targets set at different stages of a front stage, a middle stage and a later stage by using an equation (5-13), and transmitting the calculated evaluation indexes to the intelligent optimization module;
s304, the intelligent optimization module determines numerical values of individual fitness of the population according to various evaluation indexes, updates individual population according to the individual fitness, and transmits the numerical values corresponding to new individuals to the simulation evaluation module as system variables;
and S305, continuously performing interactive calculation among the simulation evaluation module and the intelligent optimization module until the genetic algorithm in the intelligent optimization module reaches a termination condition, outputting an optimization planning result, and otherwise, repeating the steps S303-S304.
Example 1
(1) Basic data
The invention adopts the typical engineering practice case of the golden wind industrial park to verify the advancement of the proposed model. The golden wind second-phase park is a national-level advanced clean energy demonstration district, mainly takes wind power and photovoltaic as main energy sources, the load increase condition of the park 2017 and 2020, and main technical parameters of wind power, photovoltaic, micro-combustion engine and the like are shown in the following table 1-2.
TABLE 12017-year 2020 load growth
TABLE 2 Main parameters of distributed energy
(2) Optimized planning scheme
According to the load increase characteristics and the power consumption situation of the golden wind park, the newly added load characteristics in the near term and the future are considered, the capacities, the investment costs and the like of different distributed power supplies are considered, the multi-stage rolling optimization planning model of the golden wind park, such as the front stage rolling optimization planning model, the middle stage rolling optimization planning model and the rear stage rolling optimization planning model, is applied to solve the golden wind park, and the power supply scheme of the park is formed, and the result is shown in the following table 3.
TABLE 3 staged roll plan for park
From table 3 it is found that:
1) compared with the scheme 1 and the scheme 2, the clean energy power generation proportion of the first-stage scheme II reaches 103.9%, namely the clean energy can meet all energy consumption of a park, the situation that the energy is directly sent to a power grid can occur, the economy is optimal theoretically, the premise is that all clean energy output is completely 100%, once wind and light are abandoned, the economy is deteriorated, and the actual risk is large.
2) In the second stage of the project in 2020, the power generation proportion of the clean energy in the scheme I and the scheme II is very close to each other, and is about 60%, on one hand, the optimal results of the schemes are basically the same under the condition of multiple targets in the later stage; on the other hand, the proportion of clean energy in the first stage of the scheme 1 is also about 60%, and further proves that the scheme 1 is better than the scheme 2 in the first stage.
3) The scheme is that according to the increase of actual load, the increase construction of new energy power supply is gradually carried out, the phenomenon that the power grid plans power supply and load in the past and a big horse pulls a trolley is effectively avoided, the utilization rate of energy is improved, and the investment return rate of projects is greatly increased.
The conclusion can be drawn that the model framework provided by the invention fully considers the coupling relation among different devices, maintains the independence of the devices, can independently model different power supplies, heat sources, energy storage and heat/electricity conversion devices, maintains the flexibility of the system composition and the accuracy of simulation calculation, and verifies the effectiveness of the model in processing the planning problem of the complex distributed energy system by combining with the actual system case of the golden wind technology park.
The present invention has been described in detail with reference to the above examples, but the description is only for the preferred examples of the present invention and should not be construed as limiting the scope of the present invention. All equivalent changes and modifications made within the scope of the present invention shall fall within the scope of the present invention.
Claims (1)
1. A multi-stage rolling optimization planning method for a distributed energy system is characterized by comprising the following steps: the method comprises the following steps:
s1, establishing an internal and external thermoelectric decoupling simulation evaluation model, and establishing an iterative optimization operation flow of the simulation evaluation model;
s101, establishing an electric and thermal balance equation;
establishing an outer layer bus solving model:
the balance equation of the electric bus is formula (1):
Pgrid+PPV+PWT+Ppgu+LE+PES+PEC+Ploss＝0 (1)
wherein, PgridRepresenting the grid exchange power, PPVRepresenting the photovoltaic power generation power, PWTRepresenting the power generated by the fan, PpguRepresents the output power of the cogeneration unit, LERepresenting the electrical load, PESRepresenting stored energy exchange power, PECIndicating the refrigerator power, PlossRepresenting the internal network loss value, P, of the electrical busbarlossIs 0; in the balance equation of the electric bus, the power flow is positive to the electric bus, and the outflow electric bus is negative;
the equilibrium equation for the thermal busbar is formula (2):
QPGUηWH+Qboiler+Lheat+QES，heat+Qloss，heat＝0 (2)
wherein Q isPGUWaste heat quantity of the cogeneration unit etaWHFor waste heat recovery efficiency, QboilerFor heat supply to gas-fired boilers, LheatFor thermal load, QES，heatFor the power of the heat storage device, Qloss，heatThe value is the internal network loss value of the thermal bus;
establishing an inner layer bus solving model:
selecting a power flow calculation model of the power system from the calculation model of the inner layer bus, and establishing a node power balance equation (3) and an inequality constraint (4) of three variables of X, mu and P of the power network:
f(X，μ，P)＝0 (3)
hmin≤H(X，μ，P)≤hmax(4)
wherein: x represents a state variable of a system, mu is a control variable or a part of optimization variables in an optimization problem, and P is a node power injection vector determined by initial power injection of each device; h ismin、hmaxRespectively, the system maximum value and the system minimum value of the variables X, mu and P;
s102, calculating output power of each device and transmission power between buses according to an outer bus balance equation and a scheduling strategy, wherein the scheduling strategy can select a fixed strategy or an optimization strategy according to needs;
s103, inputting each bus exchange electric/thermal power determined by the outer layer into the electric and thermal system model formula (3-4) of the inner layer in the step S101, respectively carrying out simulation according to the selected time scale of the electric and thermal system, and calculating the energy flow relation and the network loss;
s104, judging whether the change of the internal network loss value of each bus before and after calculation is smaller than an allowable error range, and if so, turning to the step S105; if not, outputting the internal network loss value as the transmission loss of each bus, and turning to the step S102;
s105, stopping iteration, and outputting the system state quantity as a simulation result;
s2, establishing an intelligent optimization model, dividing into three stages of an early stage, a middle stage and a later stage according to the fact that the load increase P of the user side is unequal to the capacity increase W of the distributed energy system, and constructing different multi-objective functions, wherein the early stage mainly takes the minimum net present value M as a target, the middle stage mainly takes the highest cost benefit F as a target, and the later stage mainly takes the minimum net present value M and the highest cost benefit F as targets, as shown in the following formula:
wherein, the calculation formula of the net present value M is shown as formula (6):
wherein, CNPVTo net present value, Cann，tIs the annual average cash flow, KCRF(r，Tpro) Is the capital recovery factor for the project cycle, is used to calculate the present value of the annual average cash flow, r is the interest rate, TproIs a project period, KCRFIs represented by equation (7):
the calculation formula of the cost gain F is shown as the formula (8):
Cann，t＝Cann，cap+Cann，rep+Cann，om+Cann，ele+Cann，bas-Bsel-Bsub(8)
wherein, Cann，capFor annual cost, Cann，repFor annual replacement costs, Cann，omFor annual maintenance cost, Cann，eleAnnual electricity charge cost, Cann，basFor annual basic electricity charge cost, BselFor annual electric sales income, BsubEarning for subsidy; wherein, annual electric charge cost Cann，eleAnnual electricity sales income BselAnd subsidy income BsubRelated to system operation, calculation needs to be carried out by combining a simulation result of 8760 hours;
annual capital cost Cann，capThe calculation formula is shown in formula (9):
Cann，cap＝Ccap.KCRF(r，Tpro) (9)
wherein, CcapInitial capital costs for all equipment;
annual replacement cost Cann，repThe replacement cost of each element of the system in the whole period of the project is subtracted by the residual value at the end of the project, and the calculation formula is shown as the formula (10):
wherein, CrepFor a single replacement cost, TcomThe life cycle of the elements, T, varies with the particular operating conditions of the stored energysurFor the remaining life of the element at the end of the project, KSF(r，Tcom) Paying fund factor, K, for a component cycleSF(r，Tpro) Paying fund factor for project period, frepCorrecting factors for capital recovery coefficients, which are used for dividing different capital recovery stages generated by element replacement in the whole project period;
annual operation and maintenance cost Cann，omIn order to ensure that the system is in a good running state, statistical data of the operation and maintenance cost of the system in the last year is adopted;
annual electricity charge cost Cann，eleFor representing the electricity consumption cost of the distributed energy system actually purchasing electricity from the power grid, the calculation formula is shown as an equation (11):
wherein, Wpur，iThe amount of electricity purchased to the grid for the ith hour, cpur，iThe electricity price is purchased for the ith hour;
annual basic electricity charge cost Cann，basThe method is used for representing the basic capacity electric charge paid when a large industrial distributed energy system in China adopts two electricity prices, and is calculated according to the maximum load demand standard in the month, and the formula (12) is shown as follows:
wherein, Pmax，jAverage maximum load capacity of 15 minutes for peak power consumption in the month of jbasBasic electricity fee price collected monthly from the two electricity prices;
annual electricity sales income BselThe calculation formula is shown as formula (13) for representing the income of the distributed energy system for selling the residual electric quantity to the power grid on the internetThe following steps:
wherein, Wsel，iThe amount of electricity sold to the grid for the ith hour, cselBuybacking the electricity price for the power grid;
subsidy income BsubSupplementing and pasting the generated energy of the distributed power supply;
s3, performing rolling optimization solution on the simulation evaluation model and the intelligent optimization model, wherein the rolling optimization solution method comprises the following steps:
s301, setting the composition equipment and structure parameters, equipment model parameters and genetic algorithm parameters of the distributed energy system;
s302, an intelligent optimization module generates an initial population, and transmits a numerical value corresponding to an initial population individual as a system variable to a simulation evaluation module;
s303, decoupling and iterating the simulation evaluation module according to the method of the step S1 to obtain a simulation result, simultaneously calculating corresponding evaluation indexes, namely calculating an optimized target value according to different targets set at different stages of a front stage, a middle stage and a later stage by using an equation (5-13), and transmitting the calculated evaluation indexes to the intelligent optimization module;
s304, the intelligent optimization module determines numerical values of individual fitness of the population according to various evaluation indexes, updates individual population according to the individual fitness, and transmits the numerical values corresponding to new individuals to the simulation evaluation module as system variables;
and S305, continuously performing interactive calculation among the simulation evaluation module and the intelligent optimization module until the genetic algorithm in the intelligent optimization module reaches a termination condition, outputting an optimization planning result, and otherwise, repeating the steps S303-S304.
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