CN113343478A - Independent microgrid capacity optimal configuration method considering uncertainty and demand response - Google Patents
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Abstract
The invention relates to an independent microgrid capacity optimal configuration method considering uncertainty and demand response, which is characterized by comprising the following steps of: firstly, giving a basic structure diagram of an independent micro-grid containing heat pump electricity storage, and explaining the working principle of heat pump electricity storage; secondly, establishing an uncertain scene set with adjustable conservative degree based on a robust optimization idea for representing the influence of source load uncertainty on the operation cost of the independent microgrid; then, an independent micro-grid double-layer robust optimization configuration model is constructed, an outer layer optimization model is used for configuring and optimizing the capacity of the wind turbine generator, the photovoltaic array, the diesel generator and the heat pump for power storage by taking the annual value of the total cost and the like as a target, an inner layer optimization model is used for considering the demand response on the basis of the established uncertain scene set and is used for optimizing the operation by taking the annual operation cost of the system as the lowest target; and finally, solving the model by using a genetic algorithm of the embedded mixed integer linear programming. The method has the advantages of comprehensive consideration of elements, clear model hierarchy and high calculation efficiency.
Description
Technical Field
The invention relates to the field of planning and design of independent microgrids, in particular to an independent microgrid capacity optimal configuration method considering uncertainty and demand response.
Background
The independent microgrid is a small-sized power system which comprises a distributed power supply, a load and an energy storage and is not connected with a main network, and has complete power generation and distribution functions, so that the independent microgrid gradually becomes a key technology for solving the power supply problem in remote areas. However, wind power and photovoltaic output are obviously intermittent and fluctuating under the influence of weather, and load cannot be accurately predicted under the influence of various uncertain factors, so that the influence of source load uncertainty (wind power output uncertainty and load uncertainty) is considered during the optimal configuration of the microgrid, and an obtained configuration result can better adapt to the actual operation scene in the future. Secondly, when the optimal configuration is carried out, Demand Response (DR) is considered in the microgrid optimal configuration model, the timeshiftable potential of timeshiftable load is exerted, the energy storage capacity required by building an independent microgrid can be reduced, and therefore the investment cost of the microgrid can be further reduced. Moreover, due to high investment and construction costs of the microgrid, particularly, an energy storage device which is one of important components of the independent microgrid is very expensive, a new energy storage technology, namely heat pump electricity storage, is worthy of paying attention to provide a new idea for solving the problem of high energy storage cost, compared with pumped storage and compressed air energy storage, the heat pump electricity storage has higher energy density, so that the unit capacity cost is reduced, and the heat pump electricity storage is basically not limited by geographical conditions.
In summary, the invention provides an independent microgrid capacity optimization configuration method considering uncertainty and demand response. Firstly, an independent micro-grid basic structure containing heat pump electricity storage is established, the operation principle of the heat pump electricity storage is emphatically explained, and a simplified mathematical model of the heat pump electricity storage is provided. And secondly, constructing a microgrid robust operation scene considering source load uncertainty, and laying a foundation for establishing an optimized configuration model in the next step. And then, establishing a microgrid double-layer optimization configuration model, optimizing the quantity or capacity of each device by taking the lowest annual value of the total cost as a target through an outer layer model, considering source load uncertainty and excitation type demand response through an inner layer model, and optimizing each operation variable by taking the lowest annual operation cost of the system as a target. Finally, the reasonability and effectiveness of the method provided by the invention are illustrated by examples.
Disclosure of Invention
The invention aims to overcome the defects of the prior art and establish a scientific and reasonable independent micro-grid capacity optimal configuration method which is strong in applicability and good in effect and can optimize annual values of investment cost and the like on the premise of meeting the reliability requirement, the non-renewable energy power generation ratio requirement and the renewable energy permeability requirement.
The technical scheme adopted for realizing the purpose of the invention is that the independent microgrid capacity optimal configuration method considering uncertainty and demand response is characterized by comprising the following steps of:
1) independent micro-grid basic structure for providing heat pump-containing electricity storage
The independent micro-grid comprises a Wind Turbine (WT), a photovoltaic array (PV), a diesel generator (DEG), a load and heat pump electric storage (PHES) system, wherein the wind turbine and the photovoltaic array are connected to an alternating current bus through different types of converters; the load considers two types of loads, namely rigid load and time-shifting load;
the PHES system comprises a heat storage device, an electric heat conversion device and Working Fluid (WF), wherein the heat storage device comprises a heat storage tank and a cold storage tank, and heat storage media are respectively filled in the heat storage tank and the cold storage tank; the electric-heat conversion device comprises a group of coaxial compressors and expanders, a group of heat exchangers, a group of motors and generators;
the simplified mathematical model of the PHES system is established by referring to a mathematical model of battery energy storage (1):
in the formula: t represents a time period, and the value of t is 1,2, … and 24; sSOC,tWhen it is a PHES system tThe state of charge of the segment; epsilon is the energy self-loss rate; etainAnd ηoutEfficiency of the PHES system in charge and discharge states, respectively;
and
charging and discharging power for the PHES system, respectively; delta t is the time interval of adjacent time periods, and 1h is taken;
and
maximum and minimum values of state of charge, respectively;
and
minimum and maximum powers to charge or discharge, respectively, the PHES system;
and
is a binary variable, represents the working state of the PHES,
a value of 1 indicates that PHES is in a charging state,
when the discharge voltage is 1, the PHES is in a discharge state, and the PHES cannot be simultaneously 1;
2) establishing independent microgrid operation scene considering source load uncertainty
Processing wind and light, namely uncertainty and load of a source in a source load, namely uncertainty of the load in the source load by adopting a robust optimization method, wherein the key of robust optimization is the construction of an uncertainty set; the ranges of wind speed, solar radiation intensity and load can be represented by predicted values plus prediction error; in summary, for the independent piconets, the uncertainty set is represented by equation (2);
in the formula: the value of i is 1,2 and 3, which respectively represent three uncertain variables of wind speed, solar radiation intensity and load; t represents a time period, and the value of t is 1,2, … and 24; u. ofi,tThe value of the ith uncertain variable in the t period after the uncertainty is considered;
the predicted value of the ith uncertain variable in the t period is obtained; x is the number ofi,tThe variable is a binary variable, when the variable is 1, the variable indicates that the ith uncertain variable obtains an upper limit value or a lower limit value in a t period, and when the variable is 0, the variable indicates that a predicted value is obtained; Δ ui,tSetting the maximum prediction error of the ith uncertain variable in the t time period according to the historical prediction deviation value; gamma-shapediThe conservative coefficient is introduced, represents the number of the ith uncertain variable which is taken as an upper limit value or a lower limit value in one day, and is set as an integer from 0 to 24;
3) establishing independent micro-grid double-layer optimization configuration model
The optimization configuration process not only relates to the planning problem of a long-time scale, namely the determination of a wind turbine generator, a photovoltaic array, a diesel generator and energy storage capacity, but also relates to the operation problem of a short-time scale, namely the determination of abandoned wind power, abandoned light power, output power of the diesel generator and energy storage at different moments, the determination of a time-shiftable load transfer period and the determination of transfer power, and an independent microgrid double-layer optimization configuration model is constructed, wherein the outer layer is responsible for solving a long-time scale variable, and the inner layer is responsible for solving a short-time scale variable;
establishing an outer layer optimization model:
a. establishing an objective function of an outer optimization model, wherein the outer optimization model is used for meeting the requirements of independent microgridsTotal cost equal-year value C for operators to build independent microgrids under construction and operation requirementstotalThe lowest is the target; the decision variable is the installation quantity or capacity of each device, the single machine capacity of the wind turbine generator and the photovoltaic module is given, and the variable needing to be optimized is the installation quantity; for the heat storage devices and the electrothermal conversion devices of the PHES system, the number of the installed devices is 1, and the variable to be optimized is the installed capacity, the objective functions are equations (3) to (6):
Ctotal＝min(Cinv+Cope) (3)
in the formula: cinvThe equal annual cost of equipment investment, including the annual average investment cost and annual maintenance cost of each equipment; copeThe system annual running cost is calculated by an inner layer optimization model; k is the type of equipment in the independent micro-grid, and 1 to 4 of k respectively represent a wind turbine generator, a photovoltaic array and a heat/cold storage/heat conversion device and an electric heat conversion device of the PHES system; c'f,kInitial investment cost of original unit of kth equipment; considering that the service life of some equipment is lower than the system operation life and needs to be replaced after the operation time reaches the service life, for the convenience of calculation, the unit initial investment cost of each equipment is converted into the equivalent unit initial investment cost C which does not need to be replaced any more by the formula (5)f,kI.e. Cf,kInitial investment cost of an equivalent unit of the kth equipment; cm,kAnnual maintenance cost per unit quantity or capacity for class k devices; a iskThe installation quantity or capacity of the kth equipment; k1The equipment is a set of equipment which does not need to be replaced; k2The equipment set which needs to be replaced within the operating life of the system is provided; lkIndicating that class k devices need to be in class lkReplacing every year; gkReplacing a set of years for a class k device; r is an equal-year value operator which is used for converting the current value into the equal-year value without considering the inflation of the currency; r is the discount rate; l is the system operation life;
b. establishing constraint conditions of outer layer optimization model
Due to the limited area of construction within the area for installation of various types of equipment, the configurable number or capacity of distributed power sources and heat pump electricity storage systems should satisfy the constraint of equation (7):
in the formula: a iskThe installation quantity or capacity of the kth equipment;
the maximum installed number or capacity of the kth class of equipment;
step of establishing inner layer optimization model
a. Establishing an objective function of an inner-layer optimization model, wherein the inner layer considers Demand Response (DR) and aims to minimize the annual operation cost of a system under an operation scene considering source load uncertainty; the objective function is formula (8):
in the formula: cDO,dThe oil consumption cost of the diesel engine set on the d day; cDR,dA compensation cost is given to the user participating in incentive type DR for day d; cQ,dThe electricity cost is abandoned for the day d; cT,dPunishment cost for the electricity shortage on day d;
aiming at the characteristic that the wind speed, the solar radiation intensity and the load have seasonality, the seasonal typical days of the wind speed, the solar radiation intensity and the load are used for replacing wind speed, the solar radiation intensity and the load data of each day in the season, and then an uncertain set of uncertain variables of each seasonal section is constructed based on the formula (2); thus, equation (8) is rewritten as:
in the formula: s represents typical days of different season segments, and s takes 1,2 and 3 to represent typical days of spring and autumn, summer and winter respectively; n issDays represented by the s typical day; cDO,s、CDR,s、CQ,sAnd CT,sRespectively the fuel consumption cost of the diesel engine set on the s-th typical day, the compensation cost given to the users participating in DR, the electricity abandoning cost and the electricity shortage punishment cost;
cost 1: calculation of DEG Fuel consumption cost, the fuel consumption cost C of the diesel engine set on the s-th typical dayDO,sCalculated from the formulae (10) to (11);
in the formula: s represents typical days of different season segments, and s takes 1,2 and 3 to represent typical days of spring and autumn, summer and winter respectively; t represents a time period, and the value of t is 1,2, … and 24; delta t is the time interval of adjacent time periods, and 1h is taken; rho is the price of diesel oil, and 6.13 yuan/L is taken; fs,tDEG oil consumption for the t-th typical day; pD,s,tDEG output power for a period t;
and
respectively a lowest allowable output force and a highest allowable output force of the DEG; a and b are respectively the slope and intercept coefficient of the DEG power-fuel curve, and are respectively 0.084 and 0.246;
cost 2: calculation of DR cost without considering load P of t-th typical day when DRL,s,tMainly by the rigid load P during tL0,s,tAnd time-shiftable loads PL1,s,tTwo parts are shown as formula (12);
PL,s,t＝PL0,s,t+PL1,s,t (12)
after DR is implemented, the operator may load P on timeshiftsL1,s,tThe water is utilized; the considered incentive DR is realized by an agreement between an operator and a user, and the operator can adjust the working time interval of the time-shiftable load in the load through the controller when needed and compensate the user according to the adjustment amount;
considering that the working period of adjusting the load can influence the comfort of the user, the user is allowed to shift the load forwards and backwards at most for a period of time; therefore, the time-shiftable load P 'at a certain time period after DR is implemented'L1,s,tRepresented by formulas (13) to (14):
in the formula: s represents typical days of different season segments, and s takes 1,2 and 3 to represent typical days of spring and autumn, summer and winter respectively; t represents a time period, and the value of t is 1,2, … and 24; pL1,s,tTime-shiftable loads for the t-th typical day;
and
is a binary variable when
A time of 1 indicates that there is a time-shiftable load to shift to the current slot,
a time shift load of 1 indicates that the time shift load of the time interval is transferred to other time intervals, and obviously, the two time shift loads cannot be simultaneously 1;
and
loads respectively transferred to the t-1 time interval and the t +1 time interval;
and
load transferred from the time period t to the time period t-1 and the time period t +1 respectively;
the DR cost is mainly a compensation cost required for the user to adjust the time-shiftable load, and is proportional to the adjustment amount of the time-shiftable load, and is represented by equation (15):
in the formula: s represents typical days of different season segments, and s takes 1,2 and 3 to represent typical days of spring and autumn, summer and winter respectively; t represents a time period, and the value of t is 1,2, … and 24; delta t is the time interval of adjacent time periods, and 1h is taken; cDR,sDR cost for the s typical day; alpha is the cost for transferring unit electric quantity to compensate for the user, and the invention takes 0.32 yuan/kWh;
and
load transferred from the time period t to the time period t-1 and the time period t +1 respectively;
cost 3: calculating the electricity abandoning cost, which consists of two parts, namely wind abandoning cost and light abandoning cost, and is expressed as formula (16);
in the formula: s represents typical days of different season segments, and s takes 1,2 and 3 to represent typical days of spring and autumn, summer and winter respectively; t represents a time period, and the value of t is 1,2, … and 24; delta t is the time interval of adjacent time periods, and 1h is taken; beta is the cost of unit electricity abandonment, and 0.6 yuan/kWh is taken; n isWT,s,tAnd nPV,s,tThe number of fans and the number of photovoltaic groups which are disconnected with the independent micro-grid at the t-th typical day are respectively; pWT0,s,tAnd PPV0,s,tThe power generation power of a single fan and a single group of photovoltaic are respectively in the t period of the s typical day;
cost 4: calculating the punishment cost of insufficient electric quantity, wherein the operator is punished when the load power supply is insufficient, the punishment cost is in direct proportion to the electric quantity shortage and is expressed by the following formulas (17) to (20):
P′L,s,t＝PL0,s,t+P′L1,s,t (19)
in the formula, s represents typical days of different season segments, and s takes 1,2 and 3 to represent typical days of spring and autumn, summer and winter respectively; t represents a time period, and the value of t is 1,2, … and 24; delta t is the time interval of adjacent time periods, and 1h is taken; gamma is the unit power shortage punishment cost, and 1.3 yuan/kWh is taken; pT,s,tPower shortage for the t-th typical day; p'L,s,tThe load value after the load participates in demand response for the t-th typical day;
and
charging power and discharging power of the PHES at the tth typical day time period, respectively; pWT,s,tAnd PPV,s,tWind power and photovoltaic power at the t-th typical day respectively; pD,s,tDEG output power for a period t; pL0,s,tIs the rigid load of the t period of the s typical day; p'L1,s,tTime-shiftable loads for the t-th typical day after DR are implemented; n isWTAnd nPVThe number of the configured fans and the number of the photovoltaic sets are respectively given by the outer layer; n isWT,s,tAnd nPV,s,tThe number of fans and the number of photovoltaic groups which are disconnected with the independent micro-grid at the t-th typical day are respectively; pWT0,s,tAnd PPV0,s,tThe power generation power of a single fan and a single group of photovoltaic are respectively in the t period of the s typical day;
b. determining constraints of an inner optimization model
Constraint 1: a power balance constraint, equation (21);
in the formula, s represents typical days of different season segments, and s takes 1,2 and 3 to represent typical days of spring and autumn, summer and winter respectively; t represents a time period, and the value of t is 1,2, … and 24; delta t is the time interval of adjacent time periods, and 1h is taken; p'L,s,tThe load value after the load participates in demand response for the t-th typical day;
and
charging power and discharging power of the PHES at the tth typical day time period, respectively; pWT,s,tAnd PPV,s,tWind power and photovoltaic power at the t-th typical day respectively; pD,s,tThe output power for the t-th typical day period DEG; pT,s,tPower shortage for the t-th typical day;
constraint 2: the DEG power generation capacity is restricted, and for the independent microgrid, the DEG power generation capacity should not exceed 20%, as shown in a formula (22);
in the formula, s represents typical days of different season segments, and s takes 1,2 and 3 to represent typical days of spring and autumn, summer and winter respectively; t represents a time period, and the value of t is 1,2, … and 24; delta t is the time interval of adjacent time periods, and 1h is taken; n issDays represented by the s typical day; pD,s,tDEG output power for a period t; p'L,s,tThe load value after the load participates in demand response for the t-th typical day;
constraint 3: the power abandonment rate lambda cannot be higher than a certain limit value lambda in order to ensure the utilization level of renewable energy resources0Represented by formula (23);
in the formula, lambda is the electricity abandoning rate; lambda [ alpha ]0The upper limit of the power abandonment rate is; s represents typical days of different season segments, and s takes 1,2 and 3 to represent typical days of spring and autumn, summer and winter respectively; t represents a time period, and the value of t is 1,2, … and 24; delta t is the time interval of adjacent time periods, and 1h is taken; n issDays represented by the s typical day; n isWTAnd nPVThe number of the configured fans and the number of the photovoltaic sets are respectively; n isWT,s,tAnd nPV,s,tThe number of fans and the number of photovoltaic groups which are disconnected with the independent micro-grid at the t-th typical day are respectively; pWT0,s,tAnd PPV0,s,tThe power generation power of a single fan and a single group of photovoltaic are respectively in the t period of the s typical day;
constraint 4: reliability constraint, system power shortage rate Zeta can not be higher than a certain limit value Zeta0；
In the formula, zeta is the power shortage rate; zeta0Is the upper limit of the power shortage; s represents typical days of different season segments, and s takes 1,2 and 3 to represent typical days of spring and autumn, summer and winter respectively; t represents a time period, and the value of t is 1,2, … and 24; delta t is the time interval of adjacent time periods, and 1h is taken; n issDays represented by the s typical day; pT,s,tPower shortage for the t-th typical day; p'L,s,tThe load value after the load participates in demand response for the t-th typical day;
solving of independent micro-grid double-layer optimization configuration model
Solving the outer layer optimization model by using a genetic algorithm with an elite retention strategy; after the configuration result is transmitted to the inner layer by the outer layer, the inner layer optimization model is a mixed integer nonlinear programming model and is difficult to solve directly due to the fact that the nonlinear expression of the oil consumption cost of the diesel generator in the formula (11) causes that the inner layer optimization model is a mixed integer nonlinear programming model, so that the formula (11) needs to be equivalently converted into a linear expression (25) by adopting a Big-M method, and then a Yalmip tool box and a commercial solver Cplex are adopted to realize efficient solution in a programming mode under a Matlab environment;
in the formula: s represents typical days of different season segments, and s takes 1,2 and 3 to represent typical days of spring and autumn, summer and winter respectively; t represents a time period, and the value of t is 1,2, … and 24; sigma1,s,tAnd σ2,s,tAre binary variables, and the sum of the two is 1, sigma1,s,tA time of 1 indicates that the diesel unit is in a shutdown state for the s typical day t period, σ1,s,tWhen the time is 0, the diesel engine set is in a power generation state in the s typical day t period, sigma2,s,tA time of 1 indicates that the diesel engine set is in a power generation state in the s typical day t period, sigma2,s,tWhen the value is 0, the diesel engine set is in a shutdown state in the t period of the s typical day; m1、M2、M3And M4For sufficiently large numbers, M in actual calculation1And M4The rated oil consumption per hour of the diesel generator can be taken, M2And M3The rated power of the diesel generator can be taken; fs,tDEG oil consumption for the s typical day t period; pD,s,tThe output power for the t-th typical day period DEG;
and
respectively a lowest allowable output force and a highest allowable output force of the DEG; a and b are the slope and intercept coefficient, respectively, of the DEG power-fuel curve.
The invention relates to an independent microgrid capacity optimal configuration method considering uncertainty and demand response, which comprises the steps of firstly providing a basic structure diagram of an independent microgrid containing heat pump electricity storage, and explaining the working principle of heat pump electricity storage; secondly, establishing an uncertain scene set with adjustable conservative degree based on a robust optimization idea for representing the influence of source load uncertainty on the operation cost of the independent microgrid; then, an independent micro-grid double-layer robust optimization configuration model is constructed, wherein the outer layer model configures the capacity of the wind turbine generator, the photovoltaic array, the diesel generator and the heat pump for power storage by taking the annual value of the total cost as a target, the inner layer model considers the demand response on the basis of the established uncertain scene set, and the operation optimization is carried out by taking the annual operation cost of the system as the target; finally, the model is solved by a genetic algorithm with embedded mixed integer linear programming. The method has the advantages of being scientific and reasonable, comprehensive in considered elements, clear in model level, high in calculation efficiency, strong in applicability and good in effect.
Drawings
Fig. 1 is a basic structure diagram of a microgrid when a PHES is in a charging state;
fig. 2 is a basic structure diagram of the microgrid when the PHES is in a discharge state;
FIG. 3 is a graph of annual wind speed over time within an independent microgrid;
FIG. 4 is a graph of annual solar radiation intensity over time within an individual piconet;
FIG. 5 is a graph of annual load in an isolated microgrid over time;
FIG. 6 is a graph of wind speed within an individual micro-net at different typical times of day;
FIG. 7 is a graph of solar radiation intensity within individual micro-nets at different typical times of day;
FIG. 8 is a graph of load in individual micro-nets at different typical times of day;
fig. 9 is a flowchart of a solution of an independent microgrid double-layer optimization configuration model;
FIG. 10 is a diagram of the operation of the spring and autumn typical day under scheme 2;
FIG. 11 is a diagram illustrating the operation of the spring and autumn typical day in case 3;
FIG. 12 is a diagram of the operation of the typical spring and autumn day under scenario 4;
FIG. 13 is a diagram of the operation of a typical winter day for scenario 4;
fig. 14 is a diagram of the operation condition of a typical winter day in the case of the scheme 5.
Detailed Description
The invention relates to an independent microgrid capacity optimal configuration method considering uncertainty and demand response, which comprises the following steps:
1) independent micro-grid basic structure for providing heat pump-containing electricity storage
The independent micro-grid mainly comprises a wind turbine generator, a photovoltaic array, a diesel generator, a load and a PHES system, wherein the wind turbine generator and the photovoltaic array are connected to an alternating current bus through converters of different types; the load takes into account both rigid and time-shiftable types of load. In order to conveniently describe the operation principle of the PHES in different working states, independent microgrid basic structures of the PHES in a charging state and a discharging state are constructed, which correspond to fig. 1 (charging state) and fig. 2 (discharging state), respectively.
2) Establishing independent microgrid operation scene considering source load uncertainty
The meteorological data and the load data of a certain area in a certain year are selected for example analysis, wherein the hour-scale data of the wind speed and the load are actually measured data, the solar radiation intensity data are generated by combining average daily total solar radiation of the area in a month with Homer software, the annual wind speed change curve with time is shown in figure 3, the annual solar radiation intensity change curve with time is shown in figure 4, the annual load change curve with time is shown in figure 5, the annual average wind speed is 7.80m/s, the daily average solar radiation is 4.13kWh/m2, the average load is 1.02MW, and the maximum load is 1.77 MW.
Because 365 days of the year are simultaneously optimized, the optimized variables are more, the calculated amount is larger, and therefore, aiming at the characteristic that the wind speed, the solar radiation intensity and the load are seasonal, the seasonal typical days of the wind speed, the solar radiation intensity and the load are used for replacing the wind speed, the solar radiation intensity and the load data of each day in the season, and then the uncertain set of the uncertain variables of each seasonal typical day is constructed based on the formula (2). When an uncertain set of typical day uncertain variables of each season is constructed, the conservative coefficient of wind speed, solar radiation intensity and load is calculated by taking 6 as an example; the maximum prediction errors of the wind speed, the solar radiation intensity and the load are calculated by taking 10% as an example, and the wind speed typical day scene of different season segments with uncertainty is obtained and is respectively shown in fig. 6, the solar radiation intensity typical day scene of different season segments with uncertainty is obtained and is shown in fig. 7, and the load typical day scene of different season segments with uncertainty is obtained and is shown in fig. 8.
3) Establishing independent micro-grid double-layer optimization configuration model
An independent microgrid double-layer optimization configuration model is constructed by using formulas (3) to (24), and all parameters in the model are set as: economic parameters of the distributed power supply are shown in table 1; the economic and technical parameters of lithium battery energy storage and PHES are shown in Table 2; the system operation year is 20 years; the current sticking rate is 5%; the upper limit of the electricity abandonment rate is 10 percent; the upper limit of the power shortage rate is 0.3 percent; each time interval can be time-shifted by 10% of the load of the time interval; the initial charge state of the energy storage is 0.2, and the maximum charge-discharge power is 20% of the rated capacity.
TABLE 1 economic and technical parameters of distributed power supplies
TABLE 2 economic and technical parameters of energy storage
4) Solving independent micro-grid double-layer optimization configuration model
The solving flow chart is shown in fig. 9. Solving the outer layer optimization model by using a genetic algorithm with an elite retention strategy; after the configuration result is transmitted to the inner layer by the outer layer, the inner layer optimization model is a mixed integer nonlinear programming model and is difficult to solve directly due to the fact that the nonlinear expression of the oil consumption cost of the diesel generator in the formula (11) causes that the inner layer optimization model is a mixed integer nonlinear programming model, therefore, the formula (11) needs to be equivalently converted into a linear form by adopting a Big-M method, as shown in the formula (25), and then the high-efficiency solution can be realized by programming in a Matlab environment by adopting a Yalmip tool box and a commercial solver Cplex.
In order to fully analyze and consider the influence of source charge uncertainty and DR on a configuration result and use the heat pump electricity storage system as a system energy storage scheme to compare with the economic advantage of battery energy storage, the invention is provided with the following 5 schemes for comparison analysis.
Scheme 1: selecting a lithium ion battery with the highest global application ratio in the current battery energy storage as a system energy storage scheme, and considering source charge uncertainty and DR, wherein the parameters of the lithium ion battery are shown in a table 2;
scheme 2: selecting PHES as a system energy storage scheme, and not considering source load uncertainty and DR;
scheme 3: under the configuration result of the scheme 2, calculating each economic cost of the microgrid when source load uncertainty is considered and the minimum system annual operating cost is taken as a target;
scheme 4: selecting PHES as a system energy storage scheme, and calculating an optimal configuration result when source load uncertainty is considered;
scheme 5: PHES is selected as a system energy storage scheme, and DR is further considered on the basis of considering source load uncertainty, namely the model of the invention.
The optimal configuration results under each scheme are shown in table 3, and the configuration results are analyzed in different ways.
Firstly, analyzing configuration results under different energy storage schemes
The configuration results under the lithium battery energy storage scheme and the PHES energy storage scheme respectively correspond to the scheme 1 and the scheme 2 in the table 3, and comparison shows that: although the energy storage capacity required to be configured when the PHES is adopted is higher than 417kWh when the lithium battery is adopted, the total cost of the PHES energy storage scheme is still reduced by 192.3 ten thousand yuan compared with the total cost of the lithium battery energy storage scheme. This is because: although the cycle efficiency of the PHES is 20% lower than that of the lithium battery, the PHES uses gravel with lower cost as a heat storage medium, so that the unit capacity cost of the PHES is far lower than that of the lithium battery, and the service life of the PHES is longer than that of the lithium battery, so that the replacement cost of equipment is reduced.
TABLE 3 comparison of the results of the optimized configurations for different scenarios
Analysis of influence of source load uncertainty on configuration result
Comparing scheme 2 and scheme 3, it can be seen that: the same configuration results, the annual operating cost of the system of the scheme 3 is increased by 73.8 ten thousand yuan, and the DEG power generation capacity accounts for 3.56 percent. This is because: when uncertainty is not considered, the obtained configuration scheme cannot well cope with the adverse effect (the wind-light output is reduced, the load is increased) brought by source charge uncertainty to the system operation, the use of the diesel generator must be increased in order to meet the requirements of the system power shortage rate and the power abandon rate, and as a result, the system operation cost is greatly increased. Taking the typical spring and autumn days as an example, the operation conditions of the two schemes on the typical days are respectively shown in fig. 10 and fig. 11, and it can be found that: on this typical day, when uncertainty is not taken into account, it is sufficient to satisfy the operating conditions without starting the diesel generator (corresponding to fig. 10), whereas after the uncertainty is added, it is necessary to start the diesel generator only by the fact that the energy storage has failed to satisfy the requirements, thus increasing the cost.
In case of the scheme 4, source load uncertainty is taken into consideration during planning, and compared with the scheme 3, the annual value of investment cost and the like of the scheme 4 is increased by 8.9 ten thousand yuan, the annual running cost of the system is reduced by 27.8 ten thousand yuan, the total cost is reduced by 18.9 ten thousand yuan, and the proportion of the DEG generated energy, the power abandonment rate and the power shortage rate are all reduced. The operation conditions in spring and autumn at the typical day under the scheme 4 are shown in fig. 12, and the diesel generator can be not started and the operation requirements can be met.
In conclusion, although the configuration result considering the source load uncertainty increases the investment cost, the configuration result can better cope with the uncertainty operation scene which may appear in the future, and the economical efficiency is better than the scheme without considering the uncertainty in the long term.
Analysis of influence of DR on configuration result
It is thus demonstrated that if the system operator considers DR after the microgrid has been built, a more rational deployment scenario can be obtained by considering the impact of DR during planning.
While the present invention has been described in detail and with reference to specific embodiments thereof, it will be apparent to one skilled in the art that various changes and modifications can be made therein without departing from the spirit and scope thereof as defined in the appended claims.
Claims (1)
1. An independent microgrid capacity optimization configuration method considering uncertainty and demand response is characterized by comprising the following steps:
1) independent micro-grid basic structure for providing heat pump-containing electricity storage
The independent micro-grid comprises a Wind Turbine (WT), a photovoltaic array (PV), a diesel generator (DEG), a load and heat pump electric storage (PHES) system, wherein the wind turbine and the photovoltaic array are connected to an alternating current bus through different types of converters; the load considers two types of loads, namely rigid load and time-shifting load;
the PHES system comprises a heat storage device, an electric heat conversion device and Working Fluid (WF), wherein the heat storage device comprises a heat storage tank and a cold storage tank, and heat storage media are respectively filled in the heat storage tank and the cold storage tank; the electric-heat conversion device comprises a group of coaxial compressors and expanders, a group of heat exchangers, a group of motors and generators;
the simplified mathematical model of the PHES system is established by referring to a mathematical model of battery energy storage (1):
in the formula: t represents a time period, and the value of t is 1,2, … and 24; sSOC,tThe state of charge of the PHES system in the t period; epsilon is the energy self-loss rate; etainAnd ηoutEfficiency of the PHES system in charge and discharge states, respectively;
and
charging and discharging power for the PHES system, respectively; delta t is the time interval of adjacent time periods, and 1h is taken;
and
maximum and minimum values of state of charge, respectively;
and
minimum and maximum powers to charge or discharge, respectively, the PHES system;
and
is a binary variable, represents the working state of the PHES,
a value of 1 indicates that PHES is in a charging state,
when the discharge voltage is 1, the PHES is in a discharge state, and the PHES cannot be simultaneously 1;
2) establishing independent microgrid operation scene considering source load uncertainty
Processing wind and light, namely uncertainty and load of a source in a source load, namely uncertainty of the load in the source load by adopting a robust optimization method, wherein the key of robust optimization is the construction of an uncertainty set; the ranges of wind speed, solar radiation intensity and load can be represented by predicted values plus prediction error; in summary, for the independent piconets, the uncertainty set is represented by equation (2);
in the formula: the value of i is 1,2 and 3, which respectively represent three uncertain variables of wind speed, solar radiation intensity and load; t represents a time period, and the value of t is 1,2, … and 24; u. ofi,tThe value of the ith uncertain variable in the t period after the uncertainty is considered;
the predicted value of the ith uncertain variable in the t period is obtained; x is the number ofi,tThe variable is a binary variable, when the variable is 1, the variable indicates that the ith uncertain variable obtains an upper limit value or a lower limit value in a t period, and when the variable is 0, the variable indicates that a predicted value is obtained; Δ ui,tSetting the maximum prediction error of the ith uncertain variable in the t time period according to the historical prediction deviation value; gamma-shapediThe conservative coefficient is introduced, represents the number of the ith uncertain variable which is taken as an upper limit value or a lower limit value in one day, and is set as an integer from 0 to 24;
3) establishing independent micro-grid double-layer optimization configuration model
The optimization configuration process not only relates to the planning problem of a long-time scale, namely the determination of a wind turbine generator, a photovoltaic array, a diesel generator and energy storage capacity, but also relates to the operation problem of a short-time scale, namely the determination of abandoned wind power, abandoned light power, output power of the diesel generator and energy storage at different moments, the determination of a time-shiftable load transfer period and the determination of transfer power, and an independent microgrid double-layer optimization configuration model is constructed, wherein the outer layer is responsible for solving a long-time scale variable, and the inner layer is responsible for solving a short-time scale variable;
establishing an outer layer optimization model:
a. establishing an objective function of an outer layer optimization model, wherein the outer layer is an annual value C of the total cost of the operators for constructing the independent micro-grid under the condition of meeting the construction and operation requirements of the independent micro-gridtotalThe lowest is the target; the decision variable is the installation number or capacity of each equipment, for the wind turbine andthe photovoltaic module is provided with a given single-machine capacity, and the variable needing to be optimized is the installation number; for the heat storage devices and the electrothermal conversion devices of the PHES system, the number of the installed devices is 1, and the variable to be optimized is the installed capacity, the objective functions are equations (3) to (6):
Ctotal＝min(Cinv+Cope) (3)
in the formula: cinvThe equal annual cost of equipment investment, including the annual average investment cost and annual maintenance cost of each equipment; copeThe system annual running cost is calculated by an inner layer optimization model; k is the type of equipment in the independent micro-grid, and 1 to 4 of k respectively represent a wind turbine generator, a photovoltaic array and a heat/cold storage/heat conversion device and an electric heat conversion device of the PHES system; c'f,kInitial investment cost of original unit of kth equipment; considering that the service life of some equipment is lower than the system operation life and needs to be replaced after the operation time reaches the service life, for the convenience of calculation, the unit initial investment cost of each equipment is converted into the equivalent unit initial investment cost C which does not need to be replaced any more by the formula (5)f,kI.e. Cf,kInitial investment cost of an equivalent unit of the kth equipment; cm,kAnnual maintenance cost per unit quantity or capacity for class k devices; a iskThe installation quantity or capacity of the kth equipment; k1The equipment is a set of equipment which does not need to be replaced; k2The equipment set which needs to be replaced within the operating life of the system is provided; lkIndicating that class k devices need to be in class lkReplacing every year; gkReplacing a set of years for a class k device; r is an equal-year value operator which is used for converting the current value into the equal-year value without considering the inflation of the currency; r is the discount rate; l is the system operation life;
b. establishing constraint conditions of outer layer optimization model
Due to the limited area of construction within the area for installation of various types of equipment, the configurable number or capacity of distributed power sources and heat pump electricity storage systems should satisfy the constraint of equation (7):
in the formula: a iskThe installation quantity or capacity of the kth equipment;
the maximum installed number or capacity of the kth class of equipment;
step of establishing inner layer optimization model
a. Establishing an objective function of an inner-layer optimization model, wherein the inner layer considers Demand Response (DR) and aims to minimize the annual operation cost of a system under an operation scene considering source load uncertainty; the objective function is formula (8):
in the formula: cDO，dThe oil consumption cost of the diesel engine set on the d day; cDR，dA compensation cost is given to the user participating in incentive type DR for day d; cQ，dThe electricity cost is abandoned for the day d; cT，dPunishment cost for the electricity shortage on day d;
aiming at the characteristic that the wind speed, the solar radiation intensity and the load have seasonality, the seasonal typical days of the wind speed, the solar radiation intensity and the load are used for replacing wind speed, the solar radiation intensity and the load data of each day in the season, and then an uncertain set of uncertain variables of each seasonal section is constructed based on the formula (2); thus, equation (8) is rewritten as:
in the formula: s represents typical days of different season segments, and s takes 1,2 and 3 to represent typical days of spring and autumn, summer and winter respectively; n issDays represented by the s typical day; cDO，s、CDR，s、CQ，sAnd CT，sRespectively the fuel consumption cost of the diesel engine set on the s-th typical day, the compensation cost given to the users participating in DR, the electricity abandoning cost and the electricity shortage punishment cost;
cost 1: calculation of DEG Fuel consumption cost, the fuel consumption cost C of the diesel engine set on the s-th typical dayDO，sCalculated from the formulae (10) to (11);
in the formula: s represents typical days of different season segments, and s takes 1,2 and 3 to represent typical days of spring and autumn, summer and winter respectively; t represents a time period, and the value of t is 1,2, … and 24; delta t is the time interval of adjacent time periods, and 1h is taken; rho is the price of diesel oil, and 6.13 yuan/L is taken; fs，tDEG oil consumption for the t-th typical day; pD，s，tDEG output power for a period t;
and
respectively a lowest allowable output force and a highest allowable output force of the DEG; a and b are respectively the slope and intercept coefficient of the DEG power-fuel curve, and are respectively 0.084 and 0.246;
cost 2: calculation of DR cost without considering load P of t-th typical day when DRL，s，tMainly by the rigid load PL of the t period0，s，tAnd time-shiftable loads PL1，s，tTwo parts are shown as formula (12);
PL，s，t＝PL0，s，t+PL1，s，t (12)
after DR is implemented, the operator may load P on timeshiftsL1，s，tThe water is utilized; the considered incentive DR is realized by an agreement between an operator and a user, and the operator can adjust the working time interval of the time-shiftable load in the load through the controller when needed and compensate the user according to the adjustment amount;
considering that the working period of adjusting the load can influence the comfort of the user, the user is allowed to shift the load forwards and backwards at most for a period of time; therefore, the time-shiftable load P 'at a certain time period after DR is implemented'L1，s，tRepresented by formulas (13) to (14):
in the formula: s represents typical days of different season segments, and s takes 1,2 and 3 to represent typical days of spring and autumn, summer and winter respectively; t represents a time period, and the value of t is 1,2, … and 24; pL1，s，tTime-shiftable loads for the t-th typical day;
and
is a binary variable when
A time of 1 indicates that there is a time-shiftable load to shift to the current slot,
a time shift load of 1 indicates that the time shift load of the time interval is transferred to other time intervals, and obviously, the two time shift loads cannot be simultaneously 1;
and
loads respectively transferred to the t-1 time interval and the t +1 time interval;
and
load transferred from the time period t to the time period t-1 and the time period t +1 respectively;
the DR cost is mainly a compensation cost required for the user to adjust the time-shiftable load, and is proportional to the adjustment amount of the time-shiftable load, and is represented by equation (15):
in the formula: s represents typical days of different season segments, and s takes 1,2 and 3 to represent typical days of spring and autumn, summer and winter respectively; t represents a time period, and the value of t is 1,2, … and 24; delta t is the time interval of adjacent time periods, and 1h is taken; cDR，sDR cost for the s typical day; alpha is the charge of transferring unit electric quantity compensation to user, and the invention takes 0.32 yuan/kWh
And
load transferred from the time period t to the time period t-1 and the time period t +1 respectively;
cost 3: calculating the electricity abandoning cost, which consists of two parts, namely wind abandoning cost and light abandoning cost, and is expressed as formula (16);
in the formula: s represents typical days of different season segments, and s takes 1,2 and 3 to represent typical days of spring and autumn, summer and winter respectively; t represents a time period, and the value of t is 1,2, … and 24; delta t is the time interval of adjacent time periods, and 1h is taken; beta is the cost of unit electricity abandonment, and 0.6 yuan/kWh is taken; n isWT，s，tAnd nPV，s，tThe number of fans and the number of photovoltaic groups which are disconnected with the independent micro-grid at the t-th typical day are respectively; pWT0，s，tAnd PPV0，s，tThe power generation power of a single fan and a single group of photovoltaic are respectively in the t period of the s typical day;
cost 4: calculating the punishment cost of insufficient electric quantity, wherein the operator is punished when the load power supply is insufficient, the punishment cost is in direct proportion to the electric quantity shortage and is expressed by the following formulas (17) to (20):
P′L，s，t＝PL0，s，t+P′L1，s，t (19)
in the formula, s represents typical days of different season segments, and s takes 1,2 and 3 to represent typical days of spring and autumn, summer and winter respectively; t generationA table period, taking values of 1,2, …, 24; delta t is the time interval of adjacent time periods, and 1h is taken; gamma is the unit power shortage punishment cost, and 1.3 yuan/kWh is taken; pT，s，tPower shortage for the t-th typical day; p'L，s，tThe load value after the load participates in demand response for the t-th typical day;
and
charging power and discharging power of the PHES at the tth typical day time period, respectively; pWT，s，tAnd PPV，s，tWind power and photovoltaic power at the t-th typical day respectively; pD，s，tDEG output power for a period t; pL0，s，tIs the rigid load of the t period of the s typical day; p'L1，stTime-shiftable loads for the t-th typical day after DR are implemented; n isWTAnd nPVThe number of the configured fans and the number of the photovoltaic sets are respectively given by the outer layer; n isWT，s，tAnd nPV，s，tThe number of fans and the number of photovoltaic groups which are disconnected with the independent micro-grid at the t-th typical day are respectively; pWT0，s，tAnd PPV0，s，tThe power generation power of a single fan and a single group of photovoltaic are respectively in the t period of the s typical day;
b. determining constraints of an inner optimization model
Constraint 1: a power balance constraint, equation (21);
in the formula, s represents typical days of different season segments, and s takes 1,2 and 3 to represent typical days of spring and autumn, summer and winter respectively; t represents a time period, and the value of t is 1,2, … and 24; delta t is the time interval of adjacent time periods, and 1h is taken; p'L，s，tThe load value after the load participates in demand response for the t-th typical day;
and
charging power and discharging power of the PHES at the tth typical day time period, respectively; pWT，s，tAnd PPV，s，tWind power and photovoltaic power at the t-th typical day respectively; pD，s，tThe output power for the t-th typical day period DEG; pT，s，tPower shortage for the t-th typical day;
constraint 2: the DEG power generation capacity is restricted, and for the independent microgrid, the DEG power generation capacity should not exceed 20%, as shown in a formula (22);
in the formula, s represents typical days of different season segments, and s takes 1,2 and 3 to represent typical days of spring and autumn, summer and winter respectively; t represents a time period, and the value of t is 1,2, … and 24; delta t is the time interval of adjacent time periods, and 1h is taken; n issDays represented by the s typical day; pD，s，tDEG output power for a period t; p'L，s，tThe load value after the load participates in demand response for the t-th typical day;
constraint 3: the power abandonment rate lambda cannot be higher than a certain limit value lambda in order to ensure the utilization level of renewable energy resources0Represented by formula (23);
in the formula, lambda is the electricity abandoning rate; lambda [ alpha ]0The upper limit of the power abandonment rate is; s represents typical days of different season segments, and s takes 1,2 and 3 to represent typical days of spring and autumn, summer and winter respectively; t represents a time period, and the value of t is 1,2, … and 24; delta t is the time interval of adjacent time periods, and 1h is taken; n issDays represented by the s typical dayCounting; n isWTAnd nPVThe number of the configured fans and the number of the photovoltaic sets are respectively; n isWT，s，tAnd nPV，s，tThe number of fans and the number of photovoltaic groups which are disconnected with the independent micro-grid at the t-th typical day are respectively; pWT0，s，tAnd PPV0，s，tThe power generation power of a single fan and a single group of photovoltaic are respectively in the t period of the s typical day;
constraint 4: reliability constraint, system power shortage rate Zeta can not be higher than a certain limit value Zeta0；
In the formula, zeta is the power shortage rate; zeta0Is the upper limit of the power shortage; s represents typical days of different season segments, and s takes 1,2 and 3 to represent typical days of spring and autumn, summer and winter respectively; t represents a time period, and the value of t is 1,2, … and 24; delta t is the time interval of adjacent time periods, and 1h is taken; n issDays represented by the s typical day; pT，s，tPower shortage for the t-th typical day; p'L，s，tThe load value after the load participates in demand response for the t-th typical day;
solving of independent micro-grid double-layer optimization configuration model
Solving the outer layer optimization model by using a genetic algorithm with an elite retention strategy; after the configuration result is transmitted to the inner layer by the outer layer, the inner layer optimization model is a mixed integer nonlinear programming model and is difficult to solve directly due to the fact that the nonlinear expression of the oil consumption cost of the diesel generator in the formula (11) causes that the inner layer optimization model is a mixed integer nonlinear programming model, so that the formula (11) needs to be equivalently converted into a linear expression (25) by adopting a Big-M method, and then a Yalmip tool box and a commercial solver Cplex are adopted to realize efficient solution in a programming mode under a Matlab environment;
in the formula: s represents typical days of different seasons, and s 1,2 and 3 represent spring, autumn and summer respectivelyAnd winter typical days; t represents a time period, and the value of t is 1,2, … and 24; sigma1，s，tAnd σ2，s，tAre binary variables, and the sum of the two is 1, sigma1，s，tA time of 1 indicates that the diesel unit is in a shutdown state for the s typical day t period, σ1，s，tWhen the time is 0, the diesel engine set is in a power generation state in the s typical day t period, sigma2，s，tA time of 1 indicates that the diesel engine set is in a power generation state in the s typical day t period, sigma2，s，tWhen the value is 0, the diesel engine set is in a shutdown state in the t period of the s typical day; m1、M2、M3And M4For sufficiently large numbers, M in actual calculation1And M4The rated oil consumption per hour of the diesel generator can be taken, M2And M3The rated power of the diesel generator can be taken; fs，tDEG oil consumption for the s typical day t period; pD，s，tThe output power for the t-th typical day period DEG;
and
respectively a lowest allowable output force and a highest allowable output force of the DEG; a and b are the slope and intercept coefficient, respectively, of the DEG power-fuel curve.
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