CN110378058B - Method for establishing optimal response model of electrothermal coupling micro-grid by comprehensively considering reliability and economy - Google Patents
Method for establishing optimal response model of electrothermal coupling micro-grid by comprehensively considering reliability and economy Download PDFInfo
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Abstract
The invention discloses a method for establishing an optimal response model of an electrothermal coupling micro-grid with comprehensive consideration of reliability and economy, wherein the electrothermal coupling micro-grid comprises a micro-grid, a heat supply network and an electric boiler, and the micro-grid comprises an electric load, an electric energy bus for supplying power to the electric load, distributed photovoltaic, an electric storage device and a connecting wire connected with an upper-level power grid; the heat supply network comprises a heat energy bus, a heat load and a heat storage device; the electric boiler is connected with an electric energy bus and a heat energy bus; s1, establishing an output model of each element in an electrothermal coupling micro-grid; s2, establishing an electrothermal joint response mechanism; s3, evaluating reliability and economy of the electric heating coupling micro-grid based on the electric heating combination response; s4, establishing an electrothermal joint response model by taking the optimal comprehensive benefit as a target; and S5, solving an electrothermal combination optimal response model based on a genetic algorithm. The advantages are that: the comprehensive reliability and economy considered optimal demand response of the electric heating combination is realized, and the comprehensive benefit maximization of the electric heating coupling micro-grid is realized.
Description
Technical Field
The invention relates to the field of comprehensive energy system optimization operation, in particular to a method for establishing an optimal response model of an electrothermal coupling micro-grid by comprehensively considering reliability and economy.
Background
In the collaborative development process of cities and energy sources, in order to further improve the energy source utilization efficiency and the operation safety and economy of the energy source system, a unified social comprehensive energy source system needs to be constructed, the inherent modes of independent planning and independent operation of each energy source system are broken, and overall design and operation optimization are carried out. The concept of energy internet and integrated energy system has been developed. And as a key node of the energy internet, the comprehensive energy micro-grid receives a great deal of attention due to a flexible operation mode of the comprehensive energy micro-grid. Integrated energy microgrids typically encompass integrated power, air, heating, cooling, etc. energy systems, as well as related communication and information infrastructure. In the comprehensive energy microgrid, coupling and interaction between various energy sources from production and transmission to consumption are increasingly enhanced, wherein the comprehensive energy microgrid which uses a cogeneration unit, an electric boiler and the like as an energy conversion center for electric heating coupling is most commonly applied, is one of main expression forms of the energy Internet, and is an important direction of the adjustment of a distributed energy structure in China at present.
The energy supply reliability can be used for measuring the capability of the comprehensive energy system for supplying uninterrupted power, gas, cold, heat and other energy sources to users so as to ensure the energy consumption requirements of multiple types of users. For an electrothermal coupled micro-grid, the result of reliability evaluation can characterize the stability of the whole system. The evaluation of the energy supply reliability of the system is particularly important in the context of a continuous increase in load and insufficient supply capacity. The demand response is a way for users to actively adjust own electricity consumption behaviors under policies such as electricity price or excitation to obtain benefits, and further expands single electric energy demand response into comprehensive demand response considering various energy types such as electric heat, so that the smoothness of a multi-energy mutual aid and load curve can be realized, and further energy supply reliability indexes are improved. However, the introduction of the comprehensive demand response also needs to pay corresponding economic cost to realize the excitation to the user by the energy company for supplying power and heat, so that the reliability improvement and the economic cost after the comprehensive balance response are needed, an optimal demand response scheme is designed, and the electric heating combined optimal demand response comprehensively considering the reliability and the economy is realized.
Disclosure of Invention
The invention aims to provide a method for establishing an optimal response model of an electrothermal coupling micro-grid by comprehensively considering reliability and economy, so as to solve the problems in the prior art.
In order to achieve the above purpose, the technical scheme adopted by the invention is as follows:
the method for establishing the optimal response model of the electric heating coupling micro-grid comprehensively considering reliability and economy comprises the micro-grid, a heat supply network and an electric boiler, wherein elements in the micro-grid comprise electric loads, electric energy buses for supplying power to the electric loads, distributed photovoltaics, an electric storage device and connecting wires connected with an upper-level power grid; the elements in the heat supply network comprise a heat energy bus, a heat load and a heat storage device; the electric boiler is connected with the electric energy bus and the heat energy bus to couple electric energy and heat energy; comprises the following steps of the method,
s1, building an output model of each element in an electrothermal coupling micro-grid;
s2, establishing an electrothermal joint response mechanism;
s3, evaluating reliability and economy of the electric heating coupling micro-grid based on the electric heating combination response;
s4, establishing an electrothermal joint response model by taking the optimal comprehensive benefit as a target;
and S5, solving an electrothermal combination optimal response model based on a genetic algorithm.
Preferably, the step S1 includes the following specific contents,
s11, building a photovoltaic output model; counting historical data to obtain an annual illumination intensity sequence, and determining an annual photovoltaic output model by combining the relationship between illumination intensity and photovoltaic output;
S12, establishing an electric and thermal load output model; the real-time electrical load can be obtained through typical year-week, week-day and day-hour curves, calculated as,
L t ＝L p ×P w ×P d ×P h (t)
wherein L is p For peak annual load, P w As a percentage of year-week load corresponding to the t-th hour, P d P is the corresponding cycle-day load percentage coefficient h (t) is the corresponding day-to-hour load percentage factor;
the annual time sequence data of the thermal load is obtained through investigation of an actual area;
s13, establishing a power storage device power model; the real-time running condition of the electric storage device is characterized by two parameters of charge and discharge power and charge state, the dynamic model is as follows,
wherein S is SOC (t) is the state of charge of the power storage device at time t; p (P) CES 、P DES Charging and discharging power of the power storage unit respectively; deltateta CES 、η DES Respectively charging and discharging efficiency; e (E) SOC.max Is rated capacity;
s14, building an output model of the heat storage device; the state change of the heat storage device is calculated and obtained according to the heat storage capacity, the input and output heat power and the heat loss, the calculation formula is as follows,
S(t)＝S(t-1)+P hs (t)Δt-η×S(t-1)
wherein S (t) and S (t-1) respectively represent the heat storage capacities at the time t and the time t-1, and P hs (t) represents the output power of the heat storage device at time t, and η represents the heat storage efficiency of the heat storage device;
S15, establishing an electric boiler output model; the calculation formula of the heating power of the electric boiler is as follows,
Q eb ＝η eb P eb
wherein Q is eb Indicating the heating power of the electric boiler, eta eb Representing the thermoelectric power ratio, P eb Representing the electrical power of the device.
Preferably, the step S2 includes the following specific contents,
s21, establishing an electric heating joint response mechanism based on electricity price; under the electricity price at peak-valley time, the user can automatically adjust the electricity consumption behavior of the user, and transfer partial electricity consumption at peak-valley time to the electricity consumption so as to reduce the electricity consumption, wherein the calculation formulas of the electricity consumption variation of the user at different moments are as follows,
wherein Q is on ，Q mid And Q is equal to off Respectively represent peak, flat and valley periodsOriginal electricity consumption, deltaQ on ，ΔQ mid And DeltaQ off The electric load change amounts, P, respectively representing peak, flat, valley periods on ，P mid And P off Electricity prices respectively representing peak, flat, valley periods, Δp on ，ΔP mid And delta P off Respectively representing the electricity price change amount of 3 time periods, wherein epsilon is the electricity price elastic coefficient;
the original load and the load change amount under the time-sharing electricity price are synthesized, the real-time electricity load after adopting the demand response mechanism based on the electricity price is obtained,
wherein L is 0 (T) and L (T) each represent a load at time T before and after implementation of the peak-to-valley electricity price, T on ，T mid And T is off Respectively represent peak-to-valley period, deltaT of electricity consumption on ，ΔT mid And DeltaT off Respectively representing the duration of 3 periods;
the electricity storage device is used as a special electric load, and is charged at the time of electricity price of time sharing, and is discharged at the time of electricity price valley, so that a load curve is further flattened, and corresponding benefits are obtained; for the response of the heat load based on electricity price, through the cooperation of the electric boiler and the heat storage, the electric boiler works with maximum power in the valley electricity price period, redundant heat is fed into the heat storage device besides supplying normal heat load, and the electric boiler and the heat storage device are combined to supply the heat load in the peak electricity price period, wherein the heat storage device is preferentially used;
based on the response of electricity prices, the energy company needs to pay a corresponding economic cost to induce the user to participate in the response, namely, the change of electricity selling profits after the peak-valley electricity price is implemented, the calculation formula is as follows,
wherein C is PSDR For the cost of single electricity price response, P all The flat electricity price before the implementation of the peak-valley electricity price.
S22, establishing an electrothermal joint response mechanism based on excitation; the electrothermal joint response mechanism based on excitation is characterized in that when the peak load period or the reliability of a user is affected, part of users are excited to actively cut down the load by providing economic rewards of the user response, so that the reliability level of other important loads is ensured;
For the electric load response based on excitation, after receiving a response signal sent by an energy company in emergency, a user determines a response proportion by combining with the power failure willingness of the user, and a response model is expressed as follows
P ti ＝θk i ×P t
Wherein P is ti Representing the load after response, P t Representing the original load, k i The electric load reduction ratio specified by the energy company, and theta represents the power failure wish of a user;
for the thermal load response based on excitation, similar to the electrical load, after the thermal load point receives the reduction signal, the reduction proportion of the thermal load is determined comprehensively considering own will, and the response model is expressed as follows
Q ti ＝λh i ×Q t
In which Q ti Representing the thermal load after the response; q (Q) t Representing the original load; h is a i A heat load reduction ratio specified for an energy company; λ represents the interruption heating wish of the user;
the incentive-based response requires the energy company to pay an economic cost, namely incentive fee, and the fee calculation formula under single incentive is as follows
C IBDR ＝(P ti -P t )×t ir ×E+(Q ti -Q t )×t ir ×C h
Wherein C is IBDR E is compensation after unit electric quantity is reduced for the cost of single excitation response; c (C) h Is compensation after unit heat reduction, t ir Representing the response time.
Preferably, the step S3 specifically includes the following,
s31, acquiring an initial electric load, a thermal load and a photovoltaic output curve;
S32, judging whether the electric load and the thermal load participate in the response based on the electricity price, if so, updating the time sequence power of an electric load curve and the electric boiler by combining a response mechanism based on the electricity price, and calculating the response cost by combining a calculation formula of the response based on the electricity price in the step S21; if not, maintaining the previous electric load curve and the time sequence power of the electric boiler unchanged;
s33, determining the fault-free operation time of each element by combining the output model of each element;
s34, pushing the analog clock for a certain period of time, judging whether the fault element is sampled or not, and if yes, executing a step S35; if not, executing step S37;
s35, sampling the fault repair time of the fault element, judging whether the fault element is an upper-level power grid fault, if so, enabling the micro-grid to operate in an island mode, supplying electric loads by the photovoltaic and electric storage devices, and executing the step S38; if not, executing step S36;
s36, updating a force and load curve corresponding to the fault element, wherein the fault element is a photovoltaic, energy storage device or electric boiler, and executing a step S38; the output of the photovoltaic power generation device is zero after the photovoltaic power generation device fails, the energy storage device does not participate in operation any more after the energy storage device fails, and the heat load is supplied by the heat storage device after the electric boiler fails;
S37, judging whether the payload is larger than the capacity of the connecting line, if so, executing a step S38; if not, executing step S310;
s38, judging whether the photovoltaic and energy storage device meets the power supply balance or not by combining the real-time output and the load of the photovoltaic and energy storage device, and if yes, directly executing the step S39; if not, starting the active reduction of the electric heating load by combining the electric load response based on excitation and the thermal load response model based on excitation in the step S22, and calculating the reduction cost by combining a cost calculation formula in the step S22; cutting off the load until the power supply is balanced if the power supply cannot be restored after the reduction, counting reliability indexes such as the power shortage quantity, the heat shortage quantity and the like, and executing a step S39;
s39, sampling the new running time of the fault element by combining the element output model;
s310, judging whether the running time in the step S39 reaches the specified simulation duration, if so, counting the annual power shortage and the annual heat shortage of the electric heating coupling micro-grid and ending the evaluation flow; if not, return to step S34.
Preferably, in step S4, the electrothermal joint response model is built with the best comprehensive benefit as the target, and the objective function is
Wherein W is p Represents the average total number of electrothermal responses per year based on electricity price, W q Represents the average total number of yearly stimulus-based responses, i and j represent the current price of electricity and the number of stimulus-based responses, respectively, C PSDR And C IBDR Respectively representing the cost of the single electricity price response and the incentive response; r is R e And R is R h Respectively represent annual lack supply quantity and annual lack supply quantity, omega e And omega h The energy loss costs per unit of electric energy and thermal energy are represented, respectively.
Preferably, constraint conditions are set for the electrothermal joint response model established with the aim of optimizing the comprehensive benefit, so as to realize the optimal benefit, wherein the constraint conditions are as follows,
A. a power balance constraint; the electrothermal output of the electrothermal coupling micro-grid needs to be matched with the electrothermal load, the power balance constraint is set as follows,
P PV (t)+P battery (t)+P grid (t)＝L ele (t)
P EB (t)+P HS (t)＝L heat (t)
wherein P is PV (t) is the distributed photovoltaic output at the moment t, P grid (t) is the power supply power of the upper power grid at the moment t, P battery (t) is the real-time power of the electricity storage device, L ele (t)、L heat (t) the electric and thermal loads in the micro-grid at the time t, P HS (t) is the output of the heat storage equipment at the moment t,P EB (t) is the electric boiler output at time t;
B. tie line capacity constraints; the net load within the microgrid cannot be greater than the maximum capacity of the tie-line, which is set as follows,
L ele (t)-P PV (t)-P battery (t)＜C con
wherein C is con Representing the maximum capacity of the tie line;
C. Optimizing the constraint of the value range of the object; when adjusting the optimization object, the magnitude relation of peak-to-valley electricity price must be ensured, and the load reduction proportion based on excitation cannot be more than 1, the value range constraint of the optimization object is set as follows,
P off ≤P mid ≤P on
0≤k i ≤1
0≤h i ≤1
D. equipment operation constraints; the operation constraint of the equipment is that the charge and discharge or charge and discharge power of the electricity storage and heat storage device cannot exceed the maximum allowable limit value, the capacity of the stored energy must meet the capacity limit of the equipment, and the heating power of the electric boiler cannot exceed the maximum allowable heating power of the equipment.
Preferably, the step S5 specifically includes the following,
s51, generating an initial population; determining the initial coding of the population scale and the individuals, namely, the initial value of the optimization object in each individual;
s52, calculating an objective function of the individual meeting the constraint; calculating the reliability index and response cost of the individuals under the value of the current optimization object by combining the reliability and economic evaluation method, and calculating the objective function value of each individual in the population according to the objective function in the step S4;
s53, judging the optimal individual convergence; judging whether the optimal individual meeting the constraint in the step S52 converges or not, if yes, decoding and outputting the optimal individual to be used as an optimal electrothermal joint response mechanism; if not, executing step S54;
S54, selecting a child; selecting individuals in combination with roulette algorithmCalculating the objective function value of each individual, forming a disc according to a proportion, and determining which individuals are selected in a mode of generating random numbers according to the larger area of the individual on the disc as the comprehensive benefit of the individual is maximum, so that the individuals with larger objective function are guaranteed to be transmitted to the next generation with larger probability; let the total number of individuals in the population be N, and the objective function value of individual i be f i The chance that individual i is selected is,
s55, performing cross operation; among the selected subunits, the individual with larger objective function is subjected to single-point cross according to a certain probability;
s56, mutation operation; the gene codes are 01-transformed by combining the mutation probabilities in the crossed individuals, and the process returns to step S52.
The beneficial effects of the invention are as follows: the invention introduces the basic structure of the electrothermal coupling micro-grid, the output model of main elements and the electrothermal combined response mechanism under the two policies of analyzing electricity price and excitation; on the basis, a micro-grid reliability and economy assessment method considering the electric heating combination demand response is provided, the overall economy optimization after the reliability cost is considered is taken as an objective function, and an electric heating combination optimal response model is established; finally, a model optimization solving method based on a genetic algorithm is designed. The optimization result of the model proves that the optimal response model provided by the invention can guide an energy company to design a reasonable response scheme, and the comprehensive benefit maximization of the electric heating coupling micro-grid is realized.
Drawings
Fig. 1 is a schematic structural diagram of an electrothermal coupling micro-grid according to an embodiment of the present invention;
FIG. 2 is a flow chart of a method for establishing an optimal response model in an embodiment of the invention;
FIG. 3 is a schematic flow chart of reliability and economy assessment of an electrothermal coupled microgrid based on electrothermal joint response in an embodiment of the present invention;
FIG. 4 is a model solving process based on genetic algorithm in an embodiment of the invention.
Detailed Description
The present invention will be described in further detail with reference to the accompanying drawings, in order to make the objects, technical solutions and advantages of the present invention more apparent. It should be understood that the detailed description is presented by way of example only and is not intended to limit the invention.
As shown in fig. 1 to fig. 4, in this embodiment, a method for establishing an optimal response model of an electrothermal coupling micro-grid is provided, where reliability and economy are comprehensively considered, the electrothermal coupling micro-grid includes a micro-grid, a heat supply grid and an electric boiler, and elements in the micro-grid include an electric load, an electric energy bus for supplying power to the electric load, a distributed photovoltaic, an electric storage device and a connecting line connected with an upper-level power grid; the elements in the heat supply network comprise a heat energy bus, a heat load and a heat storage device; the electric boiler is connected with the electric energy bus and the heat energy bus to couple electric energy and heat energy; comprises the following steps of the method,
S1, building an output model of each element in an electrothermal coupling micro-grid;
s2, establishing an electrothermal joint response mechanism;
s3, evaluating reliability and economy of the electric heating coupling micro-grid based on the electric heating combination response;
s4, establishing an electrothermal joint response model by taking the optimal comprehensive benefit as a target;
and S5, solving an electrothermal combination optimal response model based on a genetic algorithm.
In this embodiment, the electrothermal coupling micro-grid is typically represented by a micro-grid and a heat supply network, and conversion and coupling between different energy types are realized through energy conversion equipment such as an electric boiler, and an energy hub model is combined, where the micro-grid includes an electric load, an electric energy bus for supplying power to the load, a distributed photovoltaic, an electricity storage device, and a micro-grid interconnecting line connected with an upper-level power grid; the heat supply network mainly comprises a heat energy bus, a heat load and a heat storage device; the electric boiler realizes the coupling of two energy sources by connecting an electric energy bus and a heat energy bus.
Example 1
In this embodiment, the step S1 includes the following specific details, considering the time sequence characteristics of the micro-grid, the output model of each main element is as follows:
s11, building a photovoltaic output model; counting historical data to obtain an annual illumination intensity sequence, and determining an annual photovoltaic output model by combining the relationship between illumination intensity and photovoltaic output;
S12, establishing an electric and thermal load output model; the real-time electrical load can be obtained through typical year-week, week-day and day-hour curves, calculated as,
L t ＝L p ×P w ×P d ×P h (t)
wherein L is p For peak annual load, P w As a percentage of year-week load corresponding to the t-th hour, P d P is the corresponding cycle-day load percentage coefficient h (t) is the corresponding day-to-hour load percentage factor;
the annual time sequence data of the thermal load is obtained through investigation of an actual area;
s13, establishing a power storage device power model; the real-time running condition of the electric storage device is mainly characterized by two parameters of charge and discharge power and charge state, the dynamic model is as follows,
wherein S is SOC (t) is the state of charge of the power storage device at time t; p (P) CES 、P DES Charging and discharging power of the power storage unit respectively; deltateta CES 、η DES Respectively charging and discharging efficiency; e (E) SOC.max Is rated capacity;
s14, building an output model of the heat storage device; the state change of the heat storage device is calculated and obtained according to the heat storage capacity, the input and output heat power and the heat loss, the calculation formula is as follows,
S(t)＝S(t-1)+P hs (t)Δt-η×S(t-1)
wherein S (t) and S (t-1)) The heat storage capacities at time t and time t-1 are respectively represented by P hs (t) represents the output power of the heat storage device at time t, and η represents the heat storage efficiency of the heat storage device;
S15, establishing an electric boiler output model; the calculation formula of the heating power of the electric boiler is as follows,
Q eb ＝η eb P eb
wherein Q is eb Indicating the heating power of the electric boiler, eta eb Representing the thermoelectric power ratio, P eb Representing the electrical power of the device.
Example two
In this embodiment, the step S2 includes the following specific details,
s21, establishing an electric heating joint response mechanism based on electricity price; under the electricity price at peak-valley time, the user can automatically adjust the electricity consumption behavior of the user, and transfer partial electricity consumption at peak-valley time to the electricity consumption so as to reduce the electricity consumption, wherein the calculation formulas of the electricity consumption variation of the user at different moments are as follows,
wherein Q is on ，Q mid And Q is equal to off The original electricity consumption respectively representing peak, flat and valley periods, deltaQ on ，ΔQ mid And DeltaQ off The electric load change amounts, P, respectively representing peak, flat, valley periods on ，P mid And P off Electricity prices respectively representing peak, flat, valley periods, Δp on ，ΔP mid And delta P off Respectively representing the electricity price change amount of 3 time periods, wherein epsilon is the electricity price elastic coefficient;
by integrating the original load and the load change amount under the time-of-use electricity price, the real-time electric load after adopting the demand response mechanism based on the electricity price can be obtained,
wherein L is 0 (T) and L (T) each represent a load at time T before and after implementation of the peak-to-valley electricity price, T on ，T mid And T is off Respectively represent peak-to-valley period, deltaT of electricity consumption on ，ΔT mid And DeltaT off Respectively representing the duration of 3 periods;
the electricity storage device is used as a special electric load, and takes off and land under the time-sharing electricity price by adopting a low-electricity-storage high-electricity-generation strategy, namely, the electricity price is charged in a low-electricity-price period and discharged in a high-electricity-price period, so that the curve is further smoothly met and corresponding benefits are obtained. For the influence of the heat load based on electricity price, the electric boiler is mainly matched with the heat storage device, the electric boiler works at the maximum power in the valley electricity price period, excessive heat is fed into the heat storage device except for supplying normal heat load, and the electric boiler is required to be combined with the heat storage device to supply the heat load in the peak electricity price period, wherein the heat storage device is preferentially used.
Under the response based on electricity price, the energy company needs to pay a corresponding economic cost to induce the user to participate in the response, namely, the electricity selling income is changed after the peak-valley electricity price is implemented, the calculation formula is as follows,
wherein C is PSDR For the cost of single electricity price response, P all The flat electricity price before the implementation of the peak-valley electricity price. Response economy analysis based on electricity prices can be performed in combination with the above formula.
S22, establishing an electrothermal joint response mechanism based on excitation; the electrothermal joint response mechanism based on excitation is characterized in that when the peak load period or the reliability of a user is affected, a part of users are excited to actively cut down the load by providing corresponding economic rewards of the users, so that the reliability level of other important loads is ensured; the manner of incentive is determined in advance by the power supplier's and user-specified bi-directional contracts.
For the electric load response based on excitation, after receiving a response signal sent by an energy company in emergency, a user determines a response proportion by combining with the power failure willingness of the user, and a response model is expressed as follows
P ti ＝θk i ×P t
Wherein P is ti Representing the load after response, P t Representing the original load, k i The electric load reduction ratio specified by the energy company, and theta represents the power failure wish of a user;
for the thermal load response based on excitation, similar to the electrical load, after the thermal load point receives the reduction signal, the reduction proportion of the thermal load is determined comprehensively considering own will, and the response model is expressed as follows
Q ti ＝λh i ×Q t
In which Q ti Representing the thermal load after the response; q (Q) t Representing the original load; h is a i A heat load reduction ratio specified for an energy company; lambda represents the interruption heat supply willingness of a user, and the range is between 0 and 1, so that the index distribution is satisfied;
the incentive-based response requires the energy company to pay an economic cost, namely incentive fee, and the fee calculation formula of the single incentive response is as follows
C IBDR ＝(P ti -P t )×t ir ×E+(Q ti -Q t )×t ir ×C h
Wherein C is IBDR E is compensation after unit electric quantity is reduced for the cost of single excitation response; c (C) h Is compensation after unit heat reduction, t ir Representing the response time.
Example III
In this embodiment, reliability and economy of the electro-thermal coupling micro-grid are evaluated based on the electro-thermal joint response, as shown in fig. 3. By combining the time sequence characteristics of the elements with a demand response mechanism, reliability evaluation is carried out by adopting Monte Carlo simulation after the electrothermal combination response is considered, and economic representation of the response is calculated. The fault influence of elements such as photovoltaic, electricity storage, heat storage, an electric boiler and an upper power grid is considered mainly, and the fault-free working time and the fault repair time of each element are respectively subjected to exponential distribution taking the equipment fault rate and the repair rate as parameters.
The reliability index is represented by the lack of power supply and lack of power supply, and demand response fees are calculated respectively. Under the background of rapid increase of electric load, it is possible that the net load in the electric heating coupling micro-grid is larger than the capacity of the connecting wire so that the connecting wire is in overload operation and the coincidence is cut off, and the condition that the electric heating coupling micro-grid cannot function normally due to the fact that the connectivity of the electric heating coupling micro-grid is influenced by equipment fault is also possible, and the two conditions need to be considered comprehensively. Therefore, the step S3 specifically comprises the following,
s31, acquiring an initial electric load, a thermal load and a photovoltaic output curve;
s32, judging whether the electric load and the thermal load participate in the response based on the electricity price, if so, updating the time sequence power of an electric load curve and the electric boiler by combining a response mechanism based on the electricity price, and calculating the response cost by combining a calculation formula of the response based on the electricity price in the step S21; if not, maintaining the previous electric load curve and the time sequence power of the electric boiler unchanged;
s33, determining the fault-free operation time of each element by combining the output model of each element;
s34, pushing the analog clock for a certain period of time, judging whether the fault element is sampled or not, and if yes, executing a step S35; if not, executing step S37;
S35, sampling the fault repair time of the fault element, judging whether the fault element is an upper-level power grid fault, if so, enabling the micro-grid to operate in an island mode, supplying electric loads by the photovoltaic and electric storage devices, and executing the step S38; if not, executing step S36;
s36, updating a force and load curve corresponding to the fault element, wherein the fault element is a photovoltaic, energy storage device or electric boiler, and executing a step S38; the output of the photovoltaic power generation device is zero after the photovoltaic power generation device fails, the energy storage device does not participate in operation any more after the energy storage device fails, and the heat load is supplied by the heat storage device after the electric boiler fails;
s37, judging whether the payload is larger than the capacity of the connecting line, if so, executing a step S38; if not, executing step S310;
s38, judging whether the photovoltaic and energy storage device meets the power supply balance or not by combining the real-time output and the load of the photovoltaic and energy storage device, and if yes, directly executing the step S39; if not, starting the active reduction of the electric heating load by combining the electric load response based on excitation and the thermal load response model based on excitation in the step S22, and calculating the reduction cost by combining a cost calculation formula in the step S22; cutting off the load until the power supply is balanced if the power supply cannot be restored after the reduction, counting reliability indexes such as the power shortage quantity, the heat shortage quantity and the like, and executing a step S39;
S39, sampling the new running time of the fault element by combining the element output model;
s310, judging whether the running time in the step S39 reaches the specified simulation duration, if so, counting the annual power shortage and the annual heat shortage of the electric heating coupling micro-grid and ending the evaluation flow; if not, return to step S34.
Example IV
In this embodiment, in step S4, the electrothermal joint response model is built with the best comprehensive benefit as the target. The demand response of the electrothermal coupling micro-grid needs to pay a certain economic cost while improving the reliability index, and an electrothermal combined response model with optimal comprehensive benefit as a target is provided for the reliability and the economical efficiency of the comprehensive balanced response, and the objective function of the model is that
Wherein W is p Represents the average total number of electrothermal responses per year based on electricity price, W q Represents the average total number of yearly stimulus-based responses, i and j represent the current price of electricity and the number of stimulus-based responses, respectively, C PSDR And C IBDR Respectively representing the cost of the single electricity price response and the incentive response; r is R e And R is R h Respectively represent annual lack supply quantity and annual lack supply quantity, omega e And omega h The energy loss costs per unit of electric energy and thermal energy are represented, respectively.
In this embodiment, the electrothermal joint response model with the best comprehensive benefit as the target uses the peak-to-valley electricity price P on 、P mid 、P off And an electric load reduction ratio k defined by an energy company i Heat load reduction ratio h i To optimize the object, it affects the proportion of the coincidence response. Along with the improvement of the response proportion, the energy loss cost of the electric heating coupling micro-grid is reduced, but the response cost paid by an energy company is increased, and a reasonable response scheme needs to be determined to realize the overall optimal benefit. Thus, constraint conditions are set for the electrothermal joint response model established with the aim of comprehensive benefit optimization to realize the benefit optimization, the constraint conditions are specifically as follows,
A. a power balance constraint; the electrothermal output of the electrothermal coupling micro-grid needs to be matched with the electrothermal load, the power balance constraint is set as follows,
P PV (t)+P battery (t)+P grid (t)＝L ele (t)
P EB (t)+P HS (t)＝L heat (t)
wherein P is PV (t) is the distributed photovoltaic output at the moment t, P grid (t) is the power supply power of the upper power grid at the moment t, P battery (t) is the real-time power of the electricity storage device, L ele (t)、L heat (t) the electric and thermal loads in the micro-grid at the time t, P HS (t) the output of the heat storage equipment at the moment t, P EB (t) is the electric boiler output at time t;
B. tie line capacity constraints; the tie line plays a role of connecting the micro-grid with the upper-level grid, and has certain transmission limit, namely, the net load in the micro-grid cannot be larger than the maximum capacity of the tie line, otherwise, partial electric load needs to be cut off to ensure that the tie line cannot run in an overload mode, and the capacity constraint of the tie line is set as follows;
L ele (t)-P PV (t)-P battery (t)＜C con
Wherein C is con Representing the maximum capacity of the tie line;
C. optimizing the constraint of the value range of the object; when adjusting the optimization object, the magnitude relation of peak-to-valley electricity price must be ensured, and the load reduction proportion based on excitation cannot be more than 1, the value range constraint of the optimization object is set as follows,
P off ≤P mid ≤P on
0≤k i ≤1
0≤h i ≤1
D. equipment operation constraints; the operation constraint of the equipment is that the charge and discharge or charge and discharge power of the electricity storage device and the heat storage device cannot exceed the maximum allowable limit value, the capacity of the stored energy must meet the capacity limit of the equipment, and the heating power of the electric boiler cannot exceed the maximum allowable heating power of the equipment.
Example five
As shown in fig. 4, in the present embodiment, the optimal response model solving method is based on a genetic algorithm. The genetic algorithm is selected to solve the model, the essence is a simulation of the biological evolution process, and the optimal individuals in the final population are converged to an optimal value through the inheritance of the generation. In the genetic algorithm, representing each individual is a chromosome, wherein the chromosome consists of a plurality of gene segments, each gene segment has independent attribute, the chromosome of the individual is binary coded so as to facilitate subsequent genetic operation, and one chromosome is divided into five gene segments by combining with an optimization object to respectively represent peak electricity price, flat electricity price, valley electricity price, electric load reduction proportion under excitation and heat load reduction proportion under excitation; the data value of each gene segment is an optimization object, is expressed by adopting binary codes, is arranged into chromosomes in sequence, and carries out subsequent genetic operation; setting the value range of the coding object to meet the constraint of the value range of the optimizing object; the power balance and tie-line capacity constraints are used as preconditions in the reliability evaluation process to determine whether to cut or shed the load. The model solving flow based on the genetic algorithm is as follows (the step S5 specifically includes the following
S51, generating an initial population; determining the initial coding of the population scale and the individuals, namely, the initial value of the optimization object in each individual;
s52, calculating an objective function of the individual meeting the constraint; calculating the reliability index and response cost of the individuals under the value of the current optimization object by combining the reliability and economic evaluation method, and calculating the objective function value of each individual in the population according to the objective function in the step S4;
s53, judging the optimal individual convergence; judging whether the optimal individual meeting the constraint in the step S52 converges or not, if yes, decoding and outputting the optimal individual to be used as an optimal electrothermal joint response mechanism; if not, executing step S54;
s54, selecting a child; selecting individuals by combining a roulette algorithm, calculating an objective function value of each individual, forming a disc according to a proportion, and determining which individuals are selected in a mode of generating random numbers as the area of the individual on the disc is larger, so that the individuals with larger objective functions are transmitted to the next generation with larger probability; let the total number of individuals in the population be N, and the objective function value of individual i be f i Individual then i The probability of being selected is that,
/>
s55, performing cross operation; among the selected subunits, the individual with larger objective function is subjected to single-point cross according to a certain probability;
S56, mutation operation; the gene codes are 01-transformed by combining the mutation probabilities in the crossed individuals, and the process returns to step S52.
Example six
In this embodiment, an electric heating coupling micro-grid in an industrial park is taken as an example for analysis.
(1) Computer profile and base parameters
The basic structure of the electrically and thermally coupled microgrid in the industrial park is shown in figure 1. The installed capacity of the photovoltaic unit is 5.4MW; the rated capacity of the power storage device is 18MWh, the maximum allowable charge and discharge power is 1.35MW, and the charge and discharge efficiency is 0.85; the maximum electric power of the electric boiler is 6MW and the thermoelectric ratio is 0.9; the rated capacity of the heat storage device is 12MWh, the maximum heat charging and discharging power is 2.4MW, and the heat charging and discharging efficiency is 0.9. The peak value of the electric load in the micro-grid is 20MW, the peak value of the thermal load is 4.6MW, and the maximum capacity of the interconnection line of the electric heating coupling micro-grid is 16MW.
Setting the electricity price of the demand response to be 0.5 yuan/(kW.h), and selecting 11: 00-15: 00, 19: 00-21: 00 is the peak time; 00: 00-07:00 is valley; the rest time periods are normal times, and the price elasticity coefficients of different time periods are shown in table 1. In terms of excitation response, the compensation Ch for reducing the unit heat amount is 3.2 yuan/(kw·h), and the compensation E for reducing the unit electric quantity is 8 yuan/(kw·h). The energy loss cost per unit electric energy and heat energy is 0.8 yuan/(kW.h) and 0.6 yuan/(kW.h), respectively. The reliability parameters of various types of equipment are shown in table 2.
TABLE 1 price elastic matrix
TABLE 2 element reliability parameters
(2) Calculation example results
The industrial area has now implemented demand response, peak electricity price 0.7 yuan/(kW.h), level at ordinary times 0.5 yuan/(kW.h), valley electricity price 0.3 yuan/(kW.h), electric load regulation reduction proportion 80%, heat load proportion 70%. But the response parameters are set by referring to the values of the general industry, and the model provided by the invention is not combined for optimization solution.
In order to prove that the response model provided by the invention can realize the optimization of the response strategy, the annual power shortage quantity R under three scenes of no-demand response, adoption of the current demand response and adoption of the demand response strategy after optimization solution are respectively compared e Heat supply R h Demand response cost C DR Comprehensive benefit I DR The results are shown in Table 3:
TABLE 3 reliability index and comprehensive benefit under different scenarios
Scene 1 in the table indicates that no response is required, and scenes 2 and 3 respectively indicate the existing response strategy and the optimized response strategy. Under the scene 3, the response scheme after the model optimization provided by the invention is as follows: peak electricity price 0.76 yuan/(kW.h), ordinary electricity price 0.47 yuan/(kW.h), valley electricity price 0.25 yuan/(kW.h), electric load regulation reduction proportion 74%, heat load proportion 65%. Compared with the prior art, the reliability index is better although the investment of demand response is increased after optimization, and the reliability loss and the response cost are considered, so that the overall response economy of the electric heating coupling micro-grid is better after optimization, and the effectiveness of the model provided by the invention is proved.
By adopting the technical scheme disclosed by the invention, the following beneficial effects are obtained:
the invention provides a method for establishing an optimal response model of an electrothermal coupling micro-grid by comprehensively considering reliability and economy, which introduces a basic structure of the electrothermal coupling micro-grid and an output model of main elements and analyzes an electrothermal joint response mechanism under two policies of electricity price and excitation; on the basis, a micro-grid reliability and economy assessment method considering the electric heating combination demand response is provided, the overall economy optimization after the reliability cost is considered is taken as an objective function, and an electric heating combination optimal response model is established; finally, a model optimization solving method based on a genetic algorithm is designed. The model optimization result in the invention proves that the optimal response model can guide an energy company to design a reasonable response scheme, and the comprehensive benefit maximization of the electric heating coupling micro-grid is realized.
The foregoing is merely a preferred embodiment of the present invention and it should be noted that modifications and adaptations to those skilled in the art may be made without departing from the principles of the present invention, which is also intended to be covered by the present invention.
Claims (3)
1. The method for establishing the optimal response model of the electric heating coupling micro-grid comprehensively considering reliability and economy comprises the micro-grid, a heat supply network and an electric boiler, wherein elements in the micro-grid comprise electric loads, electric energy buses for supplying power to the electric loads, distributed photovoltaics, an electric storage device and connecting wires connected with an upper-level power grid; the elements in the heat supply network comprise a heat energy bus, a heat load and a heat storage device; the electric boiler is connected with the electric energy bus and the heat energy bus to couple electric energy and heat energy; the method is characterized in that: comprises the following steps of the method,
s1, building an output model of each element in an electrothermal coupling micro-grid;
s2, establishing an electrothermal joint response mechanism; the step S2 includes the following specific matters,
s21, establishing an electric heating joint response mechanism based on electricity price; under the electricity price at peak-valley time, the user can automatically adjust the electricity consumption behavior of the user, and transfer partial electricity consumption at peak-valley time to the electricity consumption so as to reduce the electricity consumption, wherein the calculation formulas of the electricity consumption variation of the user at different moments are as follows,
wherein Q is on ，Q mid And Q is equal to off The original electricity consumption respectively representing peak, flat and valley periods, deltaQ on ，ΔQ mid And DeltaQ off The electric load change amounts, P, respectively representing peak, flat, valley periods on ，P mid And P off Electricity prices respectively representing peak, flat, valley periods, Δp on ，ΔP mid And delta P off Respectively representing the electricity price change amount of 3 time periods, wherein epsilon is the electricity price elastic coefficient;
the original load and the load change amount under the time-sharing electricity price are synthesized, the real-time electricity load after adopting the demand response mechanism based on the electricity price is obtained,
wherein L is 0 (T) and L (T) each represent a load at time T before and after implementation of the peak-to-valley electricity price, T on ，T mid And T is off Respectively represent peak-to-valley period, deltaT of electricity consumption on ，ΔT mid And DeltaT off Respectively representing the duration of 3 periods;
the electricity storage device is used as a special electric load, and is charged at the time of electricity price of time sharing, and is discharged at the time of electricity price valley, so that a load curve is further flattened, and corresponding benefits are obtained; for the response of the heat load based on electricity price, through the cooperation of the electric boiler and the heat storage, the electric boiler works with maximum power in the valley electricity price period, redundant heat is fed into the heat storage device besides supplying normal heat load, and the electric boiler and the heat storage device are combined to supply the heat load in the peak electricity price period, wherein the heat storage device is preferentially used;
based on the response of electricity prices, the energy company needs to pay a corresponding economic cost to induce the user to participate in the response, namely, the change of electricity selling profits after the peak-valley electricity price is implemented, the calculation formula is as follows,
Wherein C is PSDR For the cost of single electricity price response, P all Flat electricity prices before peak-to-valley electricity prices are implemented;
s22, establishing an electrothermal joint response mechanism based on excitation; the electrothermal joint response mechanism based on excitation is characterized in that when the peak load period or the reliability of a user is affected, part of users are excited to actively cut down the load by providing economic rewards of the user response, so that the reliability level of other important loads is ensured;
for the electric load response based on excitation, after receiving a response signal sent by an energy company in emergency, a user determines a response proportion by combining with the power failure willingness of the user, and a response model is expressed as follows
P ti ＝θk i ×P t
Wherein P is ti Representing the load after response, P t Representing the original load, k i The electric load reduction ratio specified by the energy company, and theta represents the power failure wish of a user;
for the thermal load response based on excitation, similar to the electrical load, after the thermal load point receives the reduction signal, the reduction proportion of the thermal load is determined comprehensively considering own will, and the response model is expressed as follows
Q ti ＝λh i ×Q t
In which Q ti Representing the thermal load after the response; q (Q) t Representing the original load; h is a i A heat load reduction ratio specified for an energy company; λ represents the interruption heating wish of the user;
The incentive-based response requires the energy company to pay an economic cost, namely incentive fee, and the fee calculation formula under single incentive is as follows
C IBDR ＝(P ti -P t )×t ir ×E+(Q ti -Q t )×t ir ×C h
Wherein C is IBDR E is compensation after unit electric quantity is reduced for the cost of single excitation response; c (C) h Is compensation after unit heat reduction, t ir Representing response time;
s3, evaluating reliability and economy of the electric heating coupling micro-grid based on the electric heating combination response; the step S3 specifically includes the following,
s31, acquiring an initial electric load, a thermal load and a photovoltaic output curve;
s32, judging whether the electric load and the thermal load participate in the response based on the electricity price, if so, updating the time sequence power of an electric load curve and the electric boiler by combining a response mechanism based on the electricity price, and calculating the response cost by combining a calculation formula of the response based on the electricity price in the step S21; if not, maintaining the previous electric load curve and the time sequence power of the electric boiler unchanged;
s33, determining the fault-free operation time of each element by combining the output model of each element;
s34, pushing the analog clock for a certain period of time, judging whether the fault element is sampled or not, and if yes, executing a step S35; if not, executing step S37;
s35, sampling the fault repair time of the fault element, judging whether the fault element is an upper-level power grid fault, if so, enabling the micro-grid to operate in an island mode, supplying electric loads by the photovoltaic and electric storage devices, and executing the step S38; if not, executing step S36;
S36, updating a force and load curve corresponding to the fault element, wherein the fault element is a photovoltaic, energy storage device or electric boiler, and executing a step S38; the output of the photovoltaic power generation device is zero after the photovoltaic power generation device fails, the energy storage device does not participate in operation any more after the energy storage device fails, and the heat load is supplied by the heat storage device after the electric boiler fails;
s37, judging whether the payload is larger than the capacity of the connecting line, if so, executing a step S38; if not, executing step S310;
s38, judging whether the photovoltaic and energy storage device meets the power supply balance or not by combining the real-time output and the load of the photovoltaic and energy storage device, and if yes, directly executing the step S39; if not, starting the active reduction of the electric heating load by combining the electric load response based on excitation and the thermal load response model based on excitation in the step S22, and calculating the reduction cost by combining a cost calculation formula in the step S22; cutting off the load until the power supply is balanced if the power supply cannot be restored after the reduction, counting the reliability indexes of the power shortage and the power shortage, and executing a step S39;
s39, sampling the new running time of the fault element by combining the element output model;
s310, judging whether the running time in the step S39 reaches the specified simulation duration, if so, counting the annual power shortage and the annual heat shortage of the electric heating coupling micro-grid and ending the evaluation flow; if not, returning to the step S34;
S4, establishing an electrothermal joint response model by taking the optimal comprehensive benefit as a target; in step S4, the electrothermal joint response model is built with the optimal comprehensive benefit as the target, and the objective function is
Wherein W is p Represents the average total number of electrothermal responses per year based on electricity price, W q Represents the average total number of yearly stimulus-based responses, i and j represent the current price of electricity and the number of stimulus-based responses, respectively, C PSDR And C IBDR Respectively representing the cost of the single electricity price response and the incentive response; r is R e And R is R h Respectively represent annual lack supply quantity and annual lack supply quantity, omega e And omega h Respectively representing the energy loss cost of unit electric energy and heat energy;
setting constraint conditions for the electrothermal joint response model established by taking the comprehensive benefit as the target to realize the benefit optimization, wherein the constraint conditions are as follows,
A. a power balance constraint; the electrothermal output of the electrothermal coupling micro-grid needs to be matched with the electrothermal load, the power balance constraint is set as follows,
P PV (t)+P battery (t)+P grid (t)＝L ele (t)
P EB (t)+P HS (t)＝L heat (t)
wherein P is PV (t) is the distributed photovoltaic output at the moment t, P grid (t) is the power supply power of the upper power grid at the moment t, P battery (t) is the real-time power of the electricity storage device, L ele (t)、L heat (t) the electric and thermal loads in the micro-grid at the time t, P HS (t) the output of the heat storage equipment at the moment t, P EB (t) is the electric boiler output at time t;
B. tie line capacity constraints; the net load within the microgrid cannot be greater than the maximum capacity of the tie-line, which is set as follows,
L ele (t)-P PV (t)-P battery (t)＜C con
wherein C is con Representing the maximum capacity of the tie line;
C. optimizing the constraint of the value range of the object; when adjusting the optimization object, the magnitude relation of peak-to-valley electricity price must be ensured, and the load reduction proportion based on excitation cannot be more than 1, the value range constraint of the optimization object is set as follows,
P off ≤P mid ≤P on
0≤k i ≤1
0≤h i ≤1
D. equipment operation constraints; the equipment operation constraint is that the charge and discharge or charge and discharge power of the electricity storage and heat storage device cannot exceed the maximum allowable limit value, the capacity of the stored energy must meet the equipment capacity limit, and the heating power of the electric boiler cannot exceed the maximum allowable heating power of the equipment;
and S5, solving an electrothermal combination optimal response model based on a genetic algorithm.
2. The method for establishing an optimal response model of an electrothermal coupling micro-network taking reliability and economy into consideration as set forth in claim 1, wherein the method comprises the following steps: the step S1 includes the following specific matters,
s11, building a photovoltaic output model; counting historical data to obtain an annual illumination intensity sequence, and determining an annual photovoltaic output model by combining the relationship between illumination intensity and photovoltaic output;
S12, establishing an electric and thermal load output model; the real-time electrical load can be obtained through typical year-week, week-day and day-hour curves, calculated as,
L t ＝L p ×P w ×P d ×P h (t)
wherein L is p For peak annual load, P w As a percentage of year-week load corresponding to the t-th hour, P d P is the corresponding cycle-day load percentage coefficient h (t) is the corresponding day-to-hour load percentage factor;
the annual time sequence data of the thermal load is obtained through investigation of an actual area;
s13, establishing a power storage device power model; the real-time running condition of the electric storage device is characterized by two parameters of charge and discharge power and charge state, the dynamic model is as follows,
wherein S is SOC (t) is the state of charge of the power storage device at time t; p (P) CES 、P DES Charging and discharging power of the power storage unit respectively; deltateta CES 、η DES Respectively charging and discharging efficiency; e (E) SOC.max Is rated capacity;
s14, building an output model of the heat storage device; the state change of the heat storage device is calculated and obtained according to the heat storage capacity, the input and output heat power and the heat loss, the calculation formula is as follows,
S(t)＝S(t-1)+P hs (t)Δt-η×S(t-1)
wherein S (t) and S (t-1) respectively represent the heat storage capacities at the time t and the time t-1, and P hs (t) represents the output power of the heat storage device at time t, and η represents the heat storage efficiency of the heat storage device;
S15, establishing an electric boiler output model; the calculation formula of the heating power of the electric boiler is as follows,
Q eb ＝η eb P eb
wherein Q is eb Indicating the heating power of the electric boiler, eta eb Representing the thermoelectric power ratio, P eb Representing the electrical power of the device.
3. The method for establishing an optimal response model of an electrothermal coupling micro-network taking reliability and economy into consideration as set forth in claim 1, wherein the method comprises the following steps: the step S5 specifically includes the following,
s51, generating an initial population; determining the initial coding of the population scale and the individuals, namely, the initial value of the optimization object in each individual;
s52, calculating an objective function of the individual meeting the constraint; calculating the reliability index and response cost of the individuals under the value of the current optimization object by combining the reliability and economic evaluation method, and calculating the objective function value of each individual in the population according to the objective function in the step S4;
s53, judging the optimal individual convergence; judging whether the optimal individual meeting the constraint in the step S52 converges or not, if yes, decoding and outputting the optimal individual to be used as an optimal electrothermal joint response mechanism; if not, executing step S54;
s54, selecting a child; selecting individuals by combining a roulette algorithm, calculating an objective function value of each individual, forming a disc according to a proportion, and determining which individuals are selected in a mode of generating random numbers as the area of the individual on the disc is larger, so that the individuals with larger objective functions are transmitted to the next generation with larger probability; let the total number of individuals in the population be N, and the objective function value of individual i be f i The chance that individual i is selected is,
s55, performing cross operation; among the selected subunits, the individual with larger objective function is subjected to single-point cross according to a certain probability;
s56, mutation operation; the gene codes are 01-transformed by combining the mutation probabilities in the crossed individuals, and the process returns to step S52.
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