CN115859691B - Multi-objective optimal scheduling method for electric heating combined demand response - Google Patents
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Abstract
The invention discloses a multi-target optimal scheduling method for electric heating combined demand response, which comprises the following steps: step S1: constructing a price-charge relation model based on the electric load change amount and the electric price change amount aiming at price type electric demand response; step S2: aiming at the incentive type demand response, constructing an incentive model with the maximum benefit of the user demand response based on a supplier insurance mechanism; step S3: aiming at the heat demand response, constructing a functional relation between indoor temperature change and heat supply power through a first-order thermodynamic model, and obtaining a comfortable heat supply interval based on measuring heat supply comfort level by using a heat sensation voting value index; step S4: and combining a price-charge relation model, an excitation model and a thermal demand response model, constructing a multi-target optimization model by taking the lowest energy cost and the lowest carbon emission as optimization targets, and solving the multi-target optimization model by adopting an improved epsilon-constraint method to obtain a Pareto front. The modeling mode of the comprehensive energy system is improved to a great extent, so that the obtained optimal scheduling strategy is more reliable.
Description
Technical Field
The invention relates to the field of comprehensive energy optimization scheduling, in particular to a multi-objective optimization scheduling method for electric heating combined demand response.
Background
With the proposal of ' carbon peak, carbon neutralization ' and ' target, the transformation of low-carbon energy is urgent, and the energy optimization scheduling problem is not only concerned with economic cost, but also with carbon emission. On the power demand side of the wide area, a large number of flexible loads holds a great flexible regulation potential. Therefore, the demand response mechanism plays a great role in the process of constructing a novel power system, guides the resources on the demand side to change the power consumption mode of the power system, helps the power grid to cut peaks and fill valleys and eliminate new energy, thereby improving flexibility and achieving the purpose of reducing carbon emission.
The demand response brings good benefit and causes the research of a plurality of scholars at home and abroad. The power demand response can be generally classified into price type and incentive type. The price type demand response is based on the market supply and demand principle, and the original electricity utilization habit of the user is changed through price factors, so that the electricity utilization time period of the user is reasonably transferred, and the effect of optimizing a load curve is achieved. For users, the price difference can reduce the electricity consumption cost by changing the electricity consumption behavior along with the guidance, so that the pricing mode can effectively operate.
The key of price type demand response is to describe a price demand elastic matrix, which is simply called demand elasticity or price elasticity, and represents the extent of demand variation caused by a certain extent of price variation in a certain period, so that an electricity selling company can theoretically know the extent of demand of a consumer for self-contained products (electric energy) (such as slightly increasing electricity selling price, greatly reducing or slightly reducing or basically keeping unchanged electricity demand of a user), thereby making a reasonable electricity selling price to maximize self-income. In general, the electricity demand of most users at a certain time is not only related to the current electricity price but also affected by the electricity price at other times. The influence of the current electricity price on the current electricity demand of the user is represented by quantification of the self-elasticity coefficient, and the influence of the electricity price at other moments on the current electricity demand of the user is represented by quantification of the mutual-elasticity coefficient. The self-elasticity coefficient and the mutual elasticity coefficient are combined to form the price demand elasticity matrix. Most of the current researches directly give price type demand response key to describing an elastic matrix, while most of the current researches directly give self-elasticity coefficient and mutual elasticity coefficient and lack objective basis.
The incentive type response refers to that the electric company and the electricity user sign a response contract in advance, and the compensation price and the corresponding response scheme obtained by the participation of the user in the demand response are well reserved. However, even if a contract is signed in advance, uncertainty still exists in response behaviors of users, and how to deal with the uncertainty is also a problem to be solved. In addition, the comprehensive energy system plays an important role in a low-carbon large background due to the characteristics of complementation and flexible energy consumption, and how to coordinate the implementation of the demand response of multiple energy types according to different energy forms and the self-properties of the comprehensive energy system is a problem to be solved.
The above information disclosed in the background section is only for enhancement of understanding of the background of the application and therefore it may contain information that does not form the prior art that is already known to a person of ordinary skill in the art.
Disclosure of Invention
Aiming at the problems that the modeling mode of the existing comprehensive energy system is difficult to adapt to the corresponding energy demand, the electricity consumption behavior of a user is uncertain, and the acquired tuning strategy is not objective and reliable enough, the invention provides a multi-objective optimization scheduling method for electric heating combined demand response, wherein the scheme considers the characteristics of different energy types, adopts different demand response modes, and avoids subjectivity of determining an elastic coefficient in the existing scheme by establishing the relation between the change amount of an electric load and the change amount of an electric price; for the interruptible electric load (motivation type response load), the user is effectively motivated to execute the contract of the interruptible load based on the supplier insurance mechanism, so that the demand response reliability is improved; for heat demand response, measuring heat supply comfort level by using indexes of heat sensation ballot values (thermal sensation vote, TSV) to obtain a comfortable heat supply interval, thereby establishing a heat demand response model more meeting heat supply requirements; finally, taking the lowest energy cost and the lowest carbon emission as objective functions, establishing a multi-objective optimization model, and adopting the improvement
The constraint method is used for solving, the obtained Pareto front is distributed more uniformly, and the obtained optimal solutions are used as the scheduling strategy of the comprehensive energy system, so that the modeling mode of the comprehensive energy system is improved to a great extent, and the obtained optimal scheduling strategy is more reliable.
The technical scheme provided by the embodiment of the invention is as follows: a multi-objective optimal scheduling method for electric heating combined demand response comprises the following steps:
step S1: constructing a price-charge relation model based on the electric load change amount and the electric price change amount aiming at price type electric demand response;
step S2: aiming at the incentive type demand response, constructing an incentive model with the maximum benefit of the user demand response based on a supplier insurance mechanism;
step S3: aiming at the heat demand response, constructing a functional relation between indoor temperature change and heat supply power through a first-order thermodynamic model, obtaining a comfortable heat supply interval based on measuring heat supply comfort level by using a heat sensation voting value index, and ensuring that the heat supply power can provide the comfortable heat supply interval in the heat demand response model;
step S4: combining a price-charge relation model, an excitation model and a thermal demand response model, constructing a multi-objective optimization model by taking the lowest energy supply cost and the lowest carbon emission as optimization targets, and adopting improved
Solving a multi-objective optimization model by a constraint method to obtain a Pareto front;
the adoption of the improvement
The method for solving the model by the constraint method comprises the following steps:
step S41, respectively carrying out normalization processing on two single objective functions in the multi-objective optimization model; wherein,
representing energy supply costs->
Normalized objective function, ++>
Represents carbon emission +.>
A normalized objective function;
step S42, for objective functions
and />
Performing single targetOptimizing to obtain two optimal points, wherein the two optimal points are taken as two endpoints of a Pareto front set and respectively marked as A (1, 0) and B (0, 1);
step S43 of
Solving for the objective function to obtain +.>
Optimal solution of->
and />
Is the optimal solution of (a)
Let point C->
Taking a first auxiliary arc through A, B, C three points;
step S44, judging an equation
Whether or not to establish;
if the equation is satisfied, obtaining an optimal solution of the Pareto front according to the first acquisition rule
；
If the equation is not satisfied, obtaining an optimal solution of the Pareto front according to the second acquisition rule
；/>
Step S45 of
Writing constraint conditions, solving N to +.>
Minimum is the optimization problem of the objective function, N Pareto solutions are obtained and combinedFitting to form a Pareto front, and carrying out power scheduling according to Pareto solutions corresponding to the Pareto front.
Preferably, the valence-charge relation model in step S1 is expressed as follows:
wherein ,
representing the change amount of the electric load power at the time t; />
Representing the original power load demand power at the time t; />
For the mutual modulus of elasticity, characterize->
Electric price pair at momenttInfluence of the amount of power load demand at the moment; />
Representing the number of considered time periods before and after, +.>
Indicating time j.
Preferably, the excitation pattern in step S2 is expressed by the formula:
wherein , 
and />
Respectively indicate->
and />
Is the first derivative of (a); />
Indicating that the user increases his own reputation to +.>
Cost of time consuming; />
Representing the user reputation level as +.>
Probability of default at time;
Preferably, in step S3, a functional relationship between the indoor temperature change and the heating power is constructed by a first-order thermodynamic model, and expressed by the following formula:
wherein ,
and />
Respectively representing the indoor temperature and the outdoor temperature at time t, < >>
The indoor temperature at time t-1; />
The thermal power required to be provided for maintaining the indoor temperature at time t is represented; />
、/>
and />
Are constant coefficients and represent characteristic parameters of building thermal inertia; />
The heat capacity corresponding to the unit heating area of the building enclosure structure is used for heating the building heat load; />
The heating area of the building enclosure structure is used for heating the building heat load; />
The indoor heat loss value of a heating building heat load user under the conditions of unit heating area and unit indoor and outdoor temperature difference is calculated; />
For scheduling time intervals.
Preferably, the thermal demand response model in step S3 is expressed by the following formula:
wherein , 
the thermal power required to be provided for maintaining the indoor temperature at time t is represented; />
The heating power corresponding to the indoor heat sensation ticket value tsv=0 is represented.
Preferably, the multi-objective optimization model constructed in step S4 is expressed by the following formula:
wherein , 
representing energy supply costs; />
and />
Respectively representing the electricity purchase price and the natural gas purchase price at the time t; />
The power purchase at the time t is represented; />
and />
Respectively representing the natural gas power consumed by the cogeneration unit and the gas boiler at the moment t; />
Represents the carbon emission amount; />
and />
Respectively representing the carbon emission factor of the power grid and the carbon emission factor of the natural gas network; t represents the total number of scheduling periods.
Preferably, the objective function of the multi-objective optimization model constructed in step S4 meets constraint conditions, and the constraint conditions at least include power supply constraint and heat supply constraint;
the power supply constraint formula is as follows:
the heat supply constraint formula is as follows:
wherein , 
and />
Respectively representing the power generation and heating power of the cogeneration unit at the moment t;
the power of the photovoltaic power generation at the moment t; />
and />
Respectively representing the discharge power and the charging power of the electric energy storage at the time t; />
and />
The power consumption and the heating power of the electric boiler at the time t are respectively; />
Power representing an interruptible load signed by a user; />
Heating power of gas boiler, +.>
Representing the change amount of the electric load power at the time t;
representing the original electrical load demand power at time t.
Preferably, if the equation is satisfied, an optimal solution of the Pareto front is obtained according to the first acquisition rule
The method comprises the steps of carrying out a first treatment on the surface of the The method comprises the following steps:
the connection points A (1, 0) and B (0, 1) are taken as Utopia lines, and the equal division points are taken as vertical lines perpendicular to the Utopia lines, and the abscissa of the intersection point of the first auxiliary arc is
，/>
Is the optimal solution of Pareto front.
Preferably, if the equation is not satisfied, obtaining an optimal solution of the Pareto front according to the second acquisition rule
The method comprises the steps of carrying out a first treatment on the surface of the The method comprises the following steps:
taking A as a tangent point to serve as a second auxiliary arc passing through the C point, and taking B as a third auxiliary arc passing through the C point; the connection points A (1, 0) and B (0, 1) are taken as Utopia lines, N equally dividing points are taken as vertical lines perpendicular to the Utopia lines, and the abscissa of the intersection point between the connection points A (1, 0) and B (0, 1) and the arc sections AC and BC is
。
Preferably, the normalization processing is performed on two single objective functions in the multi-objective optimization model, and the formula is as follows:
wherein ,
and />
Respectively obtaining optimal solutions by taking the lowest cost as an objective function and the lowest carbon emission as an objective function; />
For an objective function of +.>
A corresponding cost value; />
For an objective function of +.>
Corresponding carbon emission values.
The invention has the beneficial effects that: the invention relates to a multi-objective optimal scheduling method for electric heating combined demand response, which is characterized in that firstly, aiming at electric power demand response, two response forms of price type and excitation type are combined; for price type demand response, a market share model (market share model, MSM) and a discrete attraction model (discrete attraction model, DAM) are adopted, an elastic matrix is analyzed and calculated, the elastic coefficient is determined according to the ground, and the relation between the change amount of the electric load and the change amount of the electric price is established, so that subjectivity of determining the elastic coefficient in the existing scheme is avoided. For the interruptible electric load, based on a supplier insurance theory (provider insurance), a user insurance credibility model is adopted, an insurance mechanism is introduced, the user is effectively stimulated to execute the contract of the interruptible load, and the demand response reliability is improved. For heat demand response, considering the ambiguity of a user on heat supply comfort level, building a relation between indoor temperature change and heat supply power through a first-order thermodynamic model, and then measuring the heat supply comfort level by using indexes of heat sensation ballot values (thermal sensation vote, TSV) to obtain a comfortable heat supply section, thereby building a heat demand response model more meeting heat supply requirements. Finally, taking the lowest energy cost and the lowest carbon emission as objective functions, establishing a multi-objective optimization model, and adopting the improvement
The constraint method solves the multiple objective functions, the coupling relation of the two objective functions is compact, the solved optimal solution space is more reliable, the obtained Pareto front is distributed more uniformly, the obtained multiple optimal solutions are used as the scheduling strategy of the comprehensive energy system, the modeling mode of the comprehensive energy system is improved to a great extent, and the obtained optimal scheduling strategy is more reliable.
The foregoing summary is merely an overview of the present invention, and is intended to be implemented in accordance with the teachings of the present invention in order that the same may be more fully understood, and in order that the same or additional objects, features and advantages of the present invention may be more fully understood.
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Other features, objects and advantages of the present invention will become more apparent upon reading of the detailed description of non-limiting embodiments made with reference to the following drawings. The drawings are only for purposes of illustrating the preferred embodiments and are not to be construed as limiting the invention. Also, like reference numerals are used to designate like parts throughout the figures.
FIG. 1 is a flow chart of a multi-objective optimal scheduling method for electric heating combined demand response according to the present invention.
FIG. 2 is a schematic diagram of an improved ε -constraint method of the invention.
FIG. 3 is a schematic diagram of a second modified ε -constraint method of the invention.
Detailed Description
For the purpose of making the objects, technical solutions and advantages of the present invention more apparent, the present invention will be described in further detail with reference to the accompanying drawings and examples, it being understood that the detailed description herein is merely a preferred embodiment of the present invention, which is intended to illustrate the present invention, and not to limit the scope of the invention, as all other embodiments obtained by those skilled in the art without making any inventive effort fall within the scope of the present invention.
Before discussing the exemplary embodiments in more detail, it should be mentioned that some exemplary embodiments are described as processes or methods depicted as flowcharts. Although a flowchart depicts operations (or steps) as a sequential process, many of the operations (or steps) can be performed in parallel, concurrently, or at the same time. Furthermore, the order of the operations may be rearranged. The process may be terminated when its operations are completed, but may have additional steps not included in the figures; the processes may correspond to methods, functions, procedures, subroutines, and the like.
Examples: as shown in fig. 1, the multi-objective optimization scheduling method for electric heating combined demand response provided by the embodiment of the invention includes the following steps:
step S1: and constructing a price-charge relation model based on the electric load change amount and the electric price change amount aiming at price type electric demand response.
Further, for price type electricity demand response, an elastic matrix is analyzed and calculated by adopting a market share model (market share model, MSM) and a discrete attraction model (discrete attraction model, DAM), and a relation model between the change amount of the electric load and the change amount of the electricity price is established.
It will be appreciated that in the economic theory, the spring rate is defined as the ratio of the rate of change of demand to the rate of change of price, which is more complex in the electricity market. Since the time-of-use electricity rate mechanism affects the amount of change in the power load at a certain time not only by the electricity rate at that time but also by the electricity rates at other times, there are self-elastic coefficients and mutual-elastic coefficients.
The elastic coefficient matrix at a certain moment is assumed to be:
in formula (1):
the number of the front and rear time periods considered; />
The elastic matrix direction at the moment i is represented; />
Is->
The t-th element in (2) represents the influence of electricity price at t on the electricity load demand at i;
Further, the self-elasticity coefficient
The relationship between the change in electricity rate and the change in demand at the same time is shown:
in formula (2):
and />
The change amount of the power load power at the moment i and the original power load demand power are respectively; />
and />
The change amount of the electricity price at the moment i and the original electricity price are respectively.
Further, the coefficient of mutual elasticity
The relationship between the electricity rate change and the demand change at different times is shown: />
In the formula (3),
and />
The change amount of the electric load power at the moment j and the original electric load demand power are respectively.
It will be appreciated that the MSM market share model characterizes the size of the share of a commodity in the market, expressed by the following formulas (4) - (5):
in formulas (4) - (5):
the market share of the commodity c at the moment i; />
The demand of the commodity c at the moment i; />
Indicating the total demand of the same kind of commodity at the moment i; />
Representing the number of similar commodities;
indicating the demand of the product h at time i.
It will be appreciated that the DAM discrete appeal model reveals that the market share of a commodity is primarily influenced by its market appeal. If a commodity has higher market appeal, it can acquire higher market share and higher market demand. The change in power load caused by the change in electricity price can thus also be combined with the ideas of MSM and DAM corresponding to the electricity demand response problem. Price and attraction are a negative correlation, lower price means higher attraction, and thus higher demand, so the elements in the power demand elastic matrix can be written as:
in the formulas (6) to (8),
the self-elasticity coefficient at the time i is represented; />
Representing the mutual elasticity coefficient of the moment i to the moment j; />
The mutual elasticity coefficient of the moment j to the moment i is represented; />
The amount of change in the demand of the commodity c at the time i;
the amount of change in the demand of the commodity c at the time j is indicated; />
The amount of change in the market share of the commodity c at the time i is represented.
It can be understood that, by means of MCI (Multiplicative competitive interaction model) model to quantify the relationship between the attractive force and the price of a commodity, for the power demand response scenario, it can be obtained that:
in formula (9): t is the total scheduling period number;
the attractive force of the electric load of the commodity c at the moment i is shown; />
A fixed constant is used for representing the influence coefficient of the price of the commodity c at the moment i on the attractive force of the electric load; />
The fluctuation of electricity price at the moment i is represented; />
The influence coefficient of electricity price on the attraction of the power load at the time t is shown; />
The electricity price at time t is shown.
Further, by bringing the formula (9) into the formula (4), the formula (10) can be obtained:
in the formula (10) of the present invention,
an influence coefficient indicating the price of commodity j on its attractive force; />
Indicating the fluctuation of electricity prices at the moment j.
Further, by combining the power demand elastic matrix, the following formulas can be obtained by respectively taking formulas (10) into formulas (6) - (8):
in the formulas (11) - (13),
the influence coefficient of electricity price at the moment i on the attraction of the electric load is represented; />
The influence coefficient of electricity price on the attraction of the power load at the moment j is represented; />
The market share of commodity c at time j is represented, where commodity refers to the power load demand.
After the value of the elastic coefficient is deduced, a price-load relation model between the load change amount and the price at a certain moment and the elastic coefficient can be established, wherein the price-load relation model is expressed as follows by a formula:
in the formula (14) of the present invention,
representing the change amount of the electric load power at the time t; />
Representing the original power load demand power at the time t; />
Is the coefficient of mutual elasticity, which is expressed as->
Influence of electricity price at moment on power load demand at moment t; j represents the number of considered time periods before and after; j represents the moment j.
Step S2: for incentive type demand response, constructing an incentive model with maximum benefit of user demand response based on a supplier insurance mechanism.
It can be appreciated that, for interruptible electrical loads, based on the supplier insurance theory (provider insurance), a user insurance credibility model is adopted, and an insurance mechanism is introduced, so that the user is effectively stimulated to execute the contract of the interruptible load, and the reliability of the demand response is improved.
It can be understood that the interruptible load is one of the excitation type demand response, the electric power user and the electric power company sign demand response contracts in advance, the electric power company pays a certain compensation to the user, and the user cuts down the own power demand according to the contracts, thereby achieving the effect of being beneficial to the operation of the electric power system.
In particular, in the incentive type demand response, the electric power company makes a demand response contract with the electric power consumer, but the response behavior of the consumer often has uncertainty, and the reliability of compliance with the contract is difficult to be ensured. The main idea of the provider insurance mechanism is that the electric company and the interruptible user enter into insurance contracts according to credibility classification. The user may be interrupted from selecting his or her reputations to fulfill the demand response contract as S, he or she may be interrupted from increasing his or her reputations to a cost, the utility company pays the user a corresponding subsidy, while paying a portion of the pure insurance fund, the sum of which is referred to as the insurance fund. If the user breaks the contract, the corresponding breaking fee of the company needs to be paid. By rationally designing the relationship between the insurance policy and the default policy and the user credibility, the user is motivated to adhere to the demand response contract, and the uncertainty of the interruptible load demand response can be reduced.
It can be appreciated that the user's reputation is reflected in both response and quality of response:
in equation (15): x represents the user response credibility;
probability of responding to an interruptible instruction (the interruptible instruction is a power value of electricity which is issued by a power grid and is expected to be cut down by a user) for the user; />
The quality coefficient of the response instruction for the user.
It can be appreciated that the utility company pays the insurance corresponding to the user according to the contract with the user, and the specific amount is related to the credibility index selected by the user, and the expression is:
in equation (16):
for a reputation level of +.>
The sum of the corresponding patch and the pure insurance gold, namely the insurance gold; />
Improving the user's own reputation to +.>
Cost of time consuming;
representing the user reputation level as +.>
Probability of default at time; />
Representing user reputationThe level is->
There is a need to pay the electric company for the default.
Further, according to the supplier insurance theory, it is possible to design
The form of (2) is:
in formula (17):
and />
Let alone->
And
is a first derivative of (a).
Further, under the above and disclosed design preconditions, the economic benefit of the user can be expressed as:
in formula (18): u represents the economic benefit of the user;
representing the user's actual reputation as +.>
Cost of real time expenses; />
Representing the user reputation level as +.>
Probability of default at time; />
Representing a reputation level that a user is truly able to reach; />
Representing the power of the user signed interruptible load.
As can be seen from (18), the economic benefit U of the user is the actual credibility on the premise that the contract is signed
Is a function of (i.e.)
For a pair of
The derivation can be obtained:
as can be seen from the actual situation, the actual credibility of the user
The larger it promotes its own reputation to the demand response contract specified reputation +>
The required cost is->
The smaller the offending probability +.>
The smaller the contract, but if the contract made by the user specifies the credibility + ->
Lower than the actual reputation of the user>
Then corresponds to the actual credibility of the user
Is not fully utilized and the demand response benefit is not maximized. From this, it can be analyzed that the demand response gain of the user follows the actual reputation +.>
Proximity contract designation reputation +>
Gradually increase when exceeding->
After that, the decrease starts again. I.e. when->
At the time, the user's demand response benefit->
The maximum value is taken. />
Further, it can be deduced therefrom that if the user wishes to maximize his own economic benefit, it is necessary to satisfy:
i.e. when
The user's actual reputation reaches the reputation level of the signed policy, and the maximum benefit is obtained. This also encourages users to follow interruptible load contracts from an economic standpoint, solving to some extent the problem of uncertainty in the user's response. In the formula (19), ∈>
and />
Respectively indicate->
and />
Is a first derivative of (a).
In this embodiment, the electric demand response is divided into a price type demand response and an interruptible load (the interruptible load refers to an agreement with the power grid, and may be temporarily disconnected from the power grid at the moment of peak load or under the fault condition of the power grid, and obtain a certain compensated electric load, such as an electric automobile, an air conditioner, etc.), and the thermal demand response is modeled as an ambiguity model of user comfort, so as to establish the electric heating combined demand response model. The electrical demand response is divided into a price type demand response and an interruptible load. The price type demand response guides the user to change the electricity consumption behavior by adjusting the time-sharing electricity price, is essentially based on the market supply and demand principle, and changes the original electricity consumption habit of the user by price factors, so that the electricity time period of the user is reasonably transferred, and the effect of optimizing the load curve is achieved. For users, due to price difference, the electricity consumption cost can be reduced by changing the electricity consumption behavior of the users along with the guiding, and the actual electricity consumption requirement at the requirement side can be regulated and controlled based on the price floating of the supply-demand relation, so that the stability of the power grid load is improved.
Step S3: for heat demand response, a functional relation between indoor temperature change and heat supply power is constructed through a first-order thermodynamic model, a comfortable heat supply interval is obtained based on heat supply comfort level measurement by using a heat sensation voting value index, and the heat supply power is ensured in the heat demand response model to provide the comfortable heat supply interval.
For heat demand response, considering the ambiguity of a user on heat supply comfort level, firstly, building a relation between indoor temperature change and heat supply power through a first-order thermodynamic model, then measuring the heat supply comfort level by using a heat sensation ballot value (thermal sensation vote, TSV) index, obtaining a comfortable heat supply section, and ensuring that the heat supply power is positioned in the section in an optimized model.
It can be understood that for the heat load, as the heating building has the self thermodynamic characteristics and the human body has perception ambiguity to the temperature change in a certain range, the heat load demand response is established by adopting the heat supply comfort model, so that the heat use comfort of the user is ensured to be in the range of the demand.
Further, the relation between indoor temperature change and heating power is constructed through a first-order thermodynamic model:
formulas (20) - (23):
and />
Indoor temperature and outdoor temperature, respectively, representing time of day, < >>
Representation->
Indoor temperature at time; />
Representation->
The thermal power required to be provided for maintaining the indoor temperature at the moment;
、/>
and />
Are constant coefficients and represent characteristic parameters of building thermal inertia; />
The heat capacity corresponding to the unit heating area of the building enclosure structure is used for heating the building heat load; />
The heating area of the building enclosure structure is used for heating the building heat load; />
The indoor heat loss value of a heating building heat load user under the conditions of unit heating area and unit indoor and outdoor temperature difference is calculated; />
For scheduling time intervals.
Further, after the relation between the building temperature and the heating power is established, the heating power is required to ensure that the indoor temperature can be controlled in a comfortable interval of a user. The human body cannot perceive the temperature change within a certain range, namely, the human body has certain ambiguity for the heat supply comfort level, so the invention adopts the heat sensation ballot value (thermal sensation vote, TSV) to measure the influence on the user comfort level when the indoor temperature changes within a certain range, and indexes and quantifies the user comfort level. At the position of
At this time, the TSV value corresponding to the most comfortable temperature of the human body is 0, and the corresponding heating power is +.>
. The invention selects the extreme value of the corresponding allowable user comfort level when the TSV is 0.1, and the corresponding heating power is thatThe lower limit is->
and />
Therefore, the heating power needs to satisfy the constraint:
meanwhile, the sum of the actual heating power should be kept the same as the sum of the standard heating power in one scheduling period:
the heat demand response model provides greater flexibility for the heat supply power, namely, the actual heat supply power in the user comfort range can be determined according to the actual running condition of the system at each moment, the sum in one period is only required to be unchanged, the standard heat supply power is not required to be met at each moment, and the flexible regulation mode can reduce the running cost or the carbon emission of the system and excavate the demand response potential of the system.
Step S4: combining a price-charge relation model, an excitation model and a thermal demand response model, constructing a multi-objective optimization model by taking the lowest energy supply cost and the lowest carbon emission as optimization targets, and adopting improved
The constraint method solves the multi-objective optimization model to obtain Pareto fronts.
1) The energy supply system for multi-objective optimized dispatching comprises equipment including a cogeneration unit (combined heat and power, CHP), a Gas Boiler (GB), an Electric Boiler (EB) and an electric Energy Storage (ES). The objective function of the optimal schedule is to minimize energy costs and carbon emissions:
formulas (26) - (27):
representing the running cost; />
and />
Respectively representing the electricity purchase price and the natural gas purchase price at the time t; />
The power purchase at the time t is represented; />
and />
Respectively representing the natural gas power consumed by the cogeneration unit CHP and the gas boiler GB at the moment t; />
Represents the carbon emission amount; />
and />
Respectively representing the carbon emission factor of the power grid and the carbon emission factor of the natural gas network; t represents the number of scheduling cycles, taken herein as 24 hours, i.e. t=24.
Further, the above multi-objective optimization model also needs to satisfy the following solution constraints:
in terms of the electrothermal united demand response, it is necessary to satisfy the demand response constraints of the above-described formulas (14), (24) to (25), as well as the power supply constraint represented by the following formula (28), the heat supply constraint represented by the formula (29), as shown in the following formula:
formulas (28) - (29):
and />
Respectively representing the power generation and heating power of the cogeneration unit at the moment t; />
The power of the photovoltaic power generation at the moment t; />
and />
Respectively representing the discharge power and the charging power of the electric energy storage at the time t; />
and />
The power consumption and the heating power of the electric boiler at the time t are respectively; />
Power representing an interruptible load signed by a user; />
Heating power of gas boiler, +.>
The variable representing the electrical load power at time t; />
Representing the original electrical load demand power at time t.
It will be appreciated that the multi-objective optimization problem is a class of mathematical optimization problems that optimize two or more objective functions simultaneously. Unlike the single-objective optimization problem, the optimal solution is not one solution, but a plurality of optimal compromise solutions, also called non-dominant solutions or Pareto optimal solutions, and the optimal solutions form Pareto fronts. The uniformity of Pareto front distribution is an important index for measuring the performance of a multi-objective algorithm, and is conventional
The constraint method takes one target as an objective function and the other objective functions as constraint conditions, and converts the multi-objective optimization problem into a single-objective optimization problem, but the obtained Pareto front distribution is uneven. The present invention thus employs an improved +.>
The constraint method utilizes Utopia lines to improve the uniformity of the Pareto front set distribution.
S41, respectively carrying out normalization processing on two single objective functions in the multi-objective optimization model; wherein,
representing energy supply costs->
Normalized objective function, ++>
Represents carbon emission +.>
Normalized objective function.
Further, for the case considered by the invention, the two objective functions are normalized first, so that the values thereof are between [0,1], and the influence of dimension is removed:
in formulas (30) - (31):
and />
Is an objective function after normalization; />
and />
Respectively obtaining optimal solutions by taking the lowest cost as an objective function and the lowest carbon emission as an objective function; />
For an objective function of +.>
A corresponding cost value; />
For an objective function of +.>
Corresponding carbon emission values.
S42, respectively
and />
And (3) performing single-objective optimization on the objective function to obtain two end points of which the two optimal points are Pareto front edge sets, wherein the two end points are respectively marked as A (1, 0) and B (0, 1).
S43 to
Solving for the objective function to obtain +.>
Optimal solution of->
and />
Optimal solution of->
Let point C->
And a first auxiliary arc is formed by passing through A, B, C three points.
S44, judging equation
Whether or not to establish;
if so, as shown in FIG. 2, the connection points A (1, 0) and B (0, 1) are taken as Utopia lines, N equally divided points are taken as vertical lines perpendicular to the lines, and the abscissa of the intersection point with the auxiliary arc is
，/>
The optimal solution of the Pareto front is obtained;
if not, as shown in fig. 3, taking a as a tangent point to make a second auxiliary arc passing through a point C, and taking B as a tangent point to make a third auxiliary arc passing through the point C; the connection points A (1, 0) and B (0, 1) are taken as Utopia lines, N equal division points are taken as vertical lines (each equal division point is provided with a vertical line) perpendicular to the Utopia lines, and the abscissa of the intersection point between the arc section AC and the arc section BC is
。
S45, obtaining each
After that (each bisecting point has a vertical line and thus a plurality of crossing points with the circular arc), the method comprises the steps of +.>
Writing constraint conditions, solving N to +.>
And the minimum is the optimization problem of the objective function, namely N Pareto solutions can be obtained, the fitting can form a Pareto front, and the power scheduling is carried out according to the Pareto solutions corresponding to the Pareto front.
The above embodiments are preferred embodiments of a multi-objective optimal scheduling method for electric heating combined demand response according to the present invention, and are not limited to the specific embodiments, but the scope of the present invention is not limited to the specific embodiments, and all equivalent changes of shape and structure according to the present invention are within the scope of the present invention.
Claims (5)
1. A multi-objective optimal scheduling method for electric heating combined demand response is characterized by comprising the following steps of: the method comprises the following steps:
step S1: constructing a price-charge relation model based on the electric load change amount and the electric price change amount aiming at price type electric demand response;
step S2: aiming at the incentive type demand response, constructing an incentive model with the maximum benefit of the user demand response based on a supplier insurance mechanism;
step S3: aiming at the heat demand response, constructing a functional relation between indoor temperature change and heat supply power through a first-order thermodynamic model, obtaining a comfortable heat supply interval based on measuring heat supply comfort level by using a heat sensation voting value index, and ensuring that the heat supply power can provide the comfortable heat supply interval in the heat demand response model;
step S4: combining a price-charge relation model, an excitation model and a thermal demand response model, constructing a multi-objective optimization model by taking the lowest energy supply cost and the lowest carbon emission as optimization targets, and adopting improved
Solving a multi-objective optimization model by a constraint method to obtain a Pareto front;
the adoption of the improvement
The constraint method solves the multi-objective optimization model to obtain a Pareto front, and comprises the following steps:
step S41, respectively carrying out normalization processing on two single objective functions in the multi-objective optimization model; wherein,
represents energy supply cost->
Representing normalized objective function, ++>
Represents carbon emission amount, +.>
Representing the normalized objective function;
step S42, for objective functions
and />
Carrying out single-objective optimization to obtain two optimal points, wherein the two optimal points are used as two endpoints of a Pareto front edge set and are respectively marked as A (1, 0) and B (0, 1);
step S43 of
Solving for the objective function to obtain +.>
Optimal solution of->
and />
Optimal solution of->
Let point C
Taking a first auxiliary arc through A, B, C three points;
step S44, judging an equation
Whether or not to establish;
if the equation is satisfied, obtaining an optimal solution of the Pareto front according to the first acquisition rule
；
If the equation is not satisfied, obtaining an optimal solution of the Pareto front according to the second acquisition rule
；
Step S45 of
Writing constraint conditions, solving N to +.>
The minimum is the optimization problem of the objective function, N Pareto solutions are obtained and fitted to form a Pareto front, and power scheduling is carried out according to the Pareto solutions corresponding to the Pareto front;
the valence-charge relation model in the step S1 is expressed as follows by a formula:
wherein ,
representation oftThe amount of change in the electrical load power at the moment; />
Representation oftThe power is required by the original electric load at the moment; />
For the mutual modulus of elasticity, characterize->
Electric price pair at momenttInfluence of the amount of power load demand at the moment; />
Representing the number of considered front and rear time periods, j representing the moment j;
the excitation pattern in step S2 is expressed by the formula:
wherein ,
and />
Respectively indicate->
and />
Is the first derivative of (a);
indicating that the user increases his own reputation to +.>
Cost of time consuming; />
Representing the user reputation level as +.>
Probability of default at time; />
and />
Respectively indicate->
And
is the first derivative of (a); />
Representing the user's actual reputation as +.>
Cost of real time expenses;
representing the user reputation level as +.>
Probability of default at time;
the thermal demand response model in step S3 is expressed by the formula:
wherein ,
representation oftThe thermal power required to be provided for maintaining the indoor temperature at the moment; />
The corresponding heating power when the indoor thermal sensation ticket value tsv=0 is represented;
if the equation is satisfied, obtaining an optimal solution of the Pareto front according to the first acquisition rule
The method comprises the steps of carrying out a first treatment on the surface of the The method comprises the following steps:
the connection points A (1, 0) and B (0, 1) are taken as Utopia lines, and the equal division points are taken as vertical lines perpendicular to the Utopia lines, and the abscissa of the intersection point of the first auxiliary arc is
，/>
The optimal solution of the Pareto front is obtained;
if the equation is not satisfied, obtaining an optimal solution of the Pareto front according to a second acquisition rule
The method comprises the steps of carrying out a first treatment on the surface of the The method comprises the following steps:
taking A as a tangent point to serve as a second auxiliary arc passing through the C point, and taking B as a third auxiliary arc passing through the C point;
the connection points A (1, 0) and B (0, 1) are taken as Utopia lines, N equally dividing points are taken as vertical lines perpendicular to the Utopia lines, and the abscissa of the intersection point between the connection points A (1, 0) and B (0, 1) and the arc sections AC and BC is
。
2. The multi-objective optimal scheduling method for electric heating combined demand response according to claim 1, wherein in step S3, a functional relationship between indoor temperature change and heating power is constructed through a first-order thermodynamic model, and the functional relationship is expressed as follows:
wherein ,
and />
Respectively representtIndoor temperature and outdoor temperature at the moment, +.>
The indoor temperature at time t-1; />
Representation oftThe thermal power required to be provided for maintaining the indoor temperature at the moment; />
、/>
and />
Are constant coefficients and represent characteristic parameters of building thermal inertia; />
The heat capacity corresponding to the unit heating area of the building enclosure structure is used for heating the building heat load; />
The heating area of the building enclosure structure is used for heating the building heat load; />
The indoor heat loss value of a heating building heat load user under the conditions of unit heating area and unit indoor and outdoor temperature difference is calculated; />
For scheduling time intervals.
3. The multi-objective optimization scheduling method of electric heat joint demand response according to claim 1, wherein the multi-objective optimization model constructed in step S4 is expressed by the following formula:
wherein ,
representing energy supply costs; />
and />
Respectively representtThe electricity purchase price and the natural gas purchase price at any time;
representation oftTime of dayPurchasing electric power; />
and />
Respectively representtNatural gas power consumed by the cogeneration unit and the gas boiler at any time; />
Represents the carbon emission amount; />
and />
Respectively representing the carbon emission factor of the power grid and the carbon emission factor of the natural gas network; t represents the total number of scheduling periods.
4. A multi-objective optimization scheduling method for electric heating joint demand response according to claim 1 or 3, wherein the objective function of the multi-objective optimization model constructed in step S4 meets constraint conditions, and the constraint conditions at least include power supply constraint and heat supply constraint;
the power supply constraint formula is as follows:
the heat supply constraint formula is as follows:
wherein ,
and />
Respectively representing the power generation and heating power of the cogeneration unit at the moment t; />
The power of the photovoltaic power generation at the moment t; />
and />
Respectively representing the discharge power and the charging power of the electric energy storage at the time t;
and />
The power consumption and the heating power of the electric boiler at the time t are respectively; />
Power representing an interruptible load signed by a user; />
Heating power of gas boiler, +.>
The variable representing the electrical load power at time t;
representing the original electrical load demand power at time t.
5. The multi-objective optimization scheduling method for electric heat combined demand response according to claim 3, wherein the normalization processing is performed on two single objective functions in the multi-objective optimization model, and the formula is as follows:
wherein , 
and />
Respectively obtaining optimal solutions by taking the lowest cost as an objective function and the lowest carbon emission as an objective function; />
For an objective function of +.>
A corresponding cost value; />
For an objective function of +.>
Corresponding carbon emission values. />
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