CN111563828B - Steam heat supply network scheduling optimization method based on demand response - Google Patents
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Abstract
The invention discloses a steam heat supply network optimal scheduling method based on demand response, which comprises the following steps: step S1, connecting a steam heat user into a cloud platform which is established by leading a thermodynamic company; step S2, acquiring historical load change conditions of each heat user before demand response, and carrying out situation assessment and classification on the historical load change conditions; s3, agreeing with different types of hot users to participate in a mechanism of demand response, a response period and a load quantity, and signing an agreement with the mechanism; s4, establishing a steam heat supply network optimization scheduling model and an objective function and constraint conditions thereof; s5, solving a steam heat supply network optimization scheduling model by using an optimization algorithm; and step S6, final scheduling and pricing schemes based on two scheduling mechanisms of incentive and price are published on a scheduling platform, and the amount of steam used is regulated by a hot user. The method can effectively smooth the load fluctuation of the steam heat supply network, thereby reducing the overall heat energy supply cost and the operation and maintenance cost.
Description
Technical Field
The invention belongs to the field of advanced control of heating systems, and particularly relates to a steam heat supply network scheduling optimization method based on demand response. And the heat utilization behaviors of different heat users are adjusted, and the load fluctuation of the steam heat supply network is smoothed, so that the operation of the steam pipe network is optimized, the benefit is improved, and the cost is reduced.
Background
With the rapid development of economy and society, the demand for heat supply is growing day by day, and steam heating systems are being applied on a large scale because of the advantages of easier satisfaction of industrial heat requirements, strong adaptability to different types of heat loads, higher efficiency in heat exchange equipment, and the like. However, because the load fluctuation of the user side of the steam heating network is relatively large at present, the operation and maintenance cost of the network is increased, and even a water hammer accident occurs in a water return pipeline when the load change is larger, the industrial production and life are affected very adversely; in addition, the fluctuation of the load inevitably brings about a heat utilization peak period and a valley period, during the peak period, the temperature or the flow rate of the pipe network is higher, compared with a stable heat utilization condition, more heat loss and friction loss can be caused due to higher internal and external temperature difference and higher flow speed in the conveying pipeline, the heat energy carried by steam and the distance capable of being conveyed are reduced, and during the heat utilization valley period, compared with the stable heat utilization condition, the heat exchange efficiency of the steam in the heat exchange equipment is reduced due to lower temperature and flow rate in the pipe network, and a certain heat economic loss is brought. Therefore, how to perform scheduling optimization makes the heat load operation of the steam pipe network under a stable working condition become an important way for reducing the operation and maintenance cost of the steam pipe network and improving the benefit.
The scheduling optimization method based on the demand response is a method which is currently more suitable for the scheduling of a steam pipe network, and the demand response can be divided into Price type PBDR (Price-Based Demand Response) and Incentive type IBDR (Incentive-Based Demand Response) according to the action mechanism of the demand response. The PBDR is mainly used for transmitting price signals reflecting the production cost of heat energy to heat users by making diversified heat supply prices, and the heat users automatically adjust the heat load requirements and the heat consumption time interval distribution and optimize the heat consumption behaviors of the heat users, so that the optimal configuration of heat energy resources is realized; IBDR is mainly to stimulate a hot user to respond to system scheduling by stimulating subsidy measures, adjust thermal behavior, and give the user a pre-agreed load adjustment subsidy. IBDR selects a target of completing load adjustment by contracting with participants capable of receiving the signals in advance by releasing subsidy price signals to the hot user, and settles transaction fees according to the amount of the transaction and the subsidy price. IBDR is mainly controlled directly by a thermal company dispatch center, and when a response request occurs, the user is required to perform an operation according to the content of the protocol by signing an advance agreement with the user, and the user is given a compensation fee agreed by the user.
According to the invention, the heat utilization characteristics of different heat users are considered, the heat utilization behaviors of the different heat users are scheduled by utilizing a demand response method, an excitation type demand response IBDR mechanism is improved, then a price type demand response PBDR is combined with the improved excitation type demand response IBDR, and the price type demand response PBDR is applied to the scheduling of a steam pipe network, and a thermal company and the heat users are coordinated and scheduled on a built cloud platform together, so that the load peaks and troughs of the steam heat network can be effectively smoothed, the peak clipping and valley filling effects are achieved, the severe fluctuation of the pressure of the steam pipe network is avoided, the occurrence of 'water hammer' events is avoided, the operation and maintenance cost is reduced, and the operation stability, the safety and the economic benefit of the steam pipe network are improved.
Disclosure of Invention
The invention aims to provide a steam heat supply network scheduling optimization method based on demand response, which utilizes two mechanisms of PBDR and IBDR to adjust the heat utilization behaviors of different heat users and smooth the load fluctuation of a steam heat supply network, thereby achieving the purposes of optimizing the operation of a steam pipe network, reducing the cost and improving the benefit.
In order to achieve the above purpose, the invention provides a steam heat supply network scheduling optimization method based on demand response, which comprises the following steps:
step S1, a steam heat user is connected to a cloud platform which is established by leading a thermal company, and a steam price and a scheduling scheme which are issued by the thermal company are received in time;
step S2, acquiring historical load change conditions of each heat user before demand response, and carrying out situation assessment and classification on the historical load change conditions;
step S3, agreeing with different types of hot users to participate in a demand response mechanism, a response period and a load, and signing a corresponding agreement with the mechanism;
s4, establishing a steam heat supply network optimization scheduling model and an objective function and constraint conditions thereof;
s5, solving a steam heat supply network optimization scheduling model by using an optimization algorithm;
and step S6, final scheduling and pricing schemes based on two scheduling mechanisms of incentive and price are published on a scheduling platform, and the amount of steam used is regulated by a hot user.
In the above technical scheme, in step S1, a steam heat user is connected to a cloud platform which is mainly constructed by a thermal company and is used for issuing a steam price and a scheduling scheme, the thermal company issues the steam price and the scheduling scheme on the cloud platform according to the result of a background optimization model, and the heat user can receive relevant information in time at a mobile terminal, so that the steam consumption is adjusted.
Further, the step S2 specifically includes the following steps:
step S201, obtaining the heat load demand change conditions of each heat user in different heat use periods from the data recorded at the measuring device installed by each heat user, and recording the heat price of the steam heat supply network, and preparing for manufacturing a demand response machine for each heat user;
step S202, obtaining the total heat load demand H of the heat supply network in the future period by predicting the heat load demands of different heat users in the steam heat supply network total,t The situation evaluation and classification are carried out on the heat utilization conditions of all heat users, wherein the heat utilization conditions are that the heat utilization period of heat utilization load is relatively fixed, the heat utilization load is relatively regular and stable, the heat utilization load is not easy to carry out dispatching to a relatively large extent, the heat utilization conditions are mainly living heat utilization of residents, and industrial loads (chemical industry and pharmacy) produced continuously are loads which can not be flexibly dispatched; the other class presents more uncertainty and fluctuation, such as industrial heat (intermittent, discontinuous), but the heat utilization period can be adjusted by adjusting the production plan, namely, the load of the industrial heat can be properly reduced in the peak period and the industrial heat can be utilized in the heat utilization valley period, so that the industrial heat (intermittent, discontinuous) can be divided into flexible dispatching loads.
Further, the step S3 is as follows:
the mechanism of action of demand response is mainly two: price-type PBDR (Price-Based Demand Response) and Incentive IBDR (Incentive-Based Demand Response).
According to the load classification of the steam heat supply network, for the non-flexible scheduling load of domestic heat load, the proposal about the reduction or increase of the steam consumption of the heat supply users can be issued on a platform by the heat supply users in a certain quantity through the measure of exciting subsidization and negotiating in advance with the heat supply users in a certain quantity, and the heat supply users can select to cooperate with the heat supply companies to execute related operations so as to obtain a certain economic subsidization, thereby relieving the load of the steam heat supply network in a peak period, and the IBDR mechanism can be suitable for the non-flexible scheduling load; for flexible load scheduling, the PBDR and the IBDR can be used simultaneously, a higher heat price is firstly set in a heat consumption peak period according to a PBDR mechanism, a lower heat price is set in a heat consumption valley period, a heat user is encouraged to choose to use heat in the heat consumption valley period, further, a protocol is agreed with some heat users in advance, advice about suggesting the heat user to reduce or increase the steam consumption is issued on a platform in certain period, the heat user can choose to cooperate with a thermal company to execute related operation, so that certain economic subsidies are obtained, and the PBDR and the IBDR are overlapped, so that the peak clipping and valley filling effects are further achieved.
The mechanism of the steam heating network based on the demand response is modeled next. Firstly, according to the heat balance principle, the total heat load requirement of the steam heat supply network in the t period can be obtained:
H total,t ＝H 1,t +H 2,t (1)
wherein H is 1,t In order to not flexibly schedule the load in the t-th period, the expression is as follows:
H 1,t ＝H 1,IBDR,t (2)
H 2,t for flexible load scheduling in the t-th period, the expression is:
H 2,t ＝H PBDR,t +H IBDR,t (3)
wherein H is PBDR,t And H is IBDR,t The expressions of the heat user load demand power under price type demand response and incentive type demand response are respectively:
wherein alpha is n,t Is a thermal user load shedding factor after IBDR demand response mechanism is performed; z n,t For IBDR, is a span of intervals, which is the closed interval [0,1]]Is a subset of the (1) and represents the variation range of IBDR response degree of the nth heat user in the t period, wherein '0' represents stop of heat supply, '1' represents normal rated heat supply, if z n,t ＝[0.2,0.4]The closed section indicates that a heat user receives the heat load reducing requirement to be 0.2-0.4 times of the original load in the t-th period; h is a n,t Rated heat load demand for the nth heat user;for the heat load demand of the mth heat consumer under price demand response, < + >>An initial thermal load demand for the mth thermal user, e m,tt And e m,st For the m-th heat user's demand elastic coefficient and cross-demand elastic coefficient, P t The heat supply unit price, P, of the heat supply network is optimized by adopting a demand response mechanism in the t period of the heat supply network t 0 Heating prices at time t before implementation of the PBDR; n is the number of hot users participating in IBDR, and M is the number of hot users participating in PBDR.
According to the principle of economic demand, the mechanism of action of PBDR on heat load demand response is mainly described by the demand elastic coefficient, and the elastic relationship between heat load demand and heat supply price is described as follows:
wherein s and T are time instants s, t=1, 2, …, T;and P t 0 The heat supply load at the time s and the heat supply price at the time t before PBDR implementation are respectively; ΔL s And DeltaP t The heat load fluctuation amount at time s and the heat supply price fluctuation amount at time t after the PBDR is implemented are respectively. The load demand fluctuation after the user participates in the PBDR is calculated as follows:
to calculate the load demand of a user after participating in PBDR, first define the user's thermal energy consumption value V (L t ) The net value of heat energy consumption of the user is:
π＝V(L t )-L t P t (10)
next, the first derivative and the second derivative with respect to L are obtained for the formula (8), and the derivative value is set to zero, thereby obtaining
Again, if the original load demand is determinedAfter that, the electric energy consumption value V (L t ) Performing Taylor expansion to obtain the use of the Taylor expansionHousehold electrical energy consumption value:
at this time, by substituting the formulas (11) and (12) into the formula (13), the simplified user heat consumption value can be obtained:
regarding L, the method of (14) t Can be obtained in combination with formula (11):
equation (15) calculates the load demand after considering the influence of the self-elasticity at time t, and in order to consider the load demand under the cross elasticity, the correction equation (15) is available:
finally, combining equation (15) and equation (16) can yield the final PBDR model, specifically as follows:
next, a mechanism for participating in demand response, a response period and a load amount are agreed with the hot user, and a corresponding agreement is signed with the mechanism. More specifically, the thermal company negotiates with each thermal user to inform the thermal company of the mechanism of the incentive type demand response, namely, during the peak period of heat consumption, the thermal company will issue suggestions on the dispatching platform about the reduction or increase of the steam consumption, and if the thermal user can execute related operations according to the suggestions, the thermal user can obtain corresponding economic compensation or discount of the thermal price through a pre-signed agreement. Thermal company statistics of response time period of hot user participation in IBDRAnd responding to the load quantity, and using the IBDR identification to represent a load change receiving interval of the heat user in a corresponding period, if the heat load demand of the heat user i in the period t is between a and b times of the original heat load demand after the heat load demand is responded, the IBDR identification of the user in the period t is as follows: z i,t ＝[a，b] (18)
Wherein 0 < a < 1,0 < b < 1; if hot user i indicates not to participate in IBDR for a certain period of time t-2, then z will be i,t-2 Setting the value "1" indicates that the thermal load is unchanged during this period.
If the thermal load demand of a thermal user i during this period of time t is acceptable to be in the range of between 60% and 100% of the original after the demand response, then the IBDR of that user during this period of time is identified as: z i,t ＝[0.6，1]。
Further, the step S4 includes:
step S401, the maximum net benefit generated by the steam heating network for demand response scheduling is an objective function:
wherein A is the number of hot users capable of flexibly scheduling load; b is the quantity of hot users which cannot flexibly schedule the load; r is R PB The benefit obtained under price type demand response scheduling in one scheduling period is obtained; r is R IB The benefits obtained under excitation type demand response scheduling in one scheduling period are obtained; c (C) 1 The cost after scheduling for demand response mainly comprises heat production cost C p And operation and maintenance cost C m ；C 2 To give the IBDR thermal user a post-subsidy cost, the unit price is decided by the thermal company negotiating with the thermal user, and post-settlement is performed; wherein the method comprises the steps of
R en Is an environmental benefit, and is composed of pollution gas with reduced emission:
in the method, in the process of the invention,and->Is CO 2 、SO 2 And NOx emission reduction; />And->Is CO 2 、SO 2 And the emission reduction value of NOx.
Wherein, c o,t And c m,t The production cost and the operation and maintenance cost of the unit heat in different time periods are respectively. h is a IBDR,i Load regulation for participating IBER for ith hot user, p i The subsidy unit price determined by the negotiation of the thermal company and the thermal user is provided.
Step S402, according to the operation characteristics of the steam heat supply network, providing constraint conditions for the stable operation of the steam heat supply network, including heat load supply and demand balance constraint conditions, heat source operation constraint and load reduction constraint.
Heat load supply and demand balance constraint conditions:
H out,t ＝H total,t (25)
wherein S is i Is the heating power of the ith heat source in the steam heating network in t time periods.
Heat source operating load constraints:
S i,min ≤S i ≤S i,max (27)
wherein S is i,min And S is equal to i,max The minimum and maximum heating power when the ith heat source operates.
Load shedding constraint 1, for PBDR:
this constraint is directed to the amount of load reduction for hot users under the PBDR mechanism,and->The load demands of the heat users before and after PBDR scheduling respectively show that in order to ensure stable operation of the heat network, there is a maximum value for the heat load reduction of the mth heat user, and the maximum value is 20% of the predicted heat load demand in the t period of the heat user.
Load shedding constraint 2, for IBDR:
is required to meet the load reduction factor alpha in any period n,t Within the range negotiated with hot user n:
α n,t ∈z n,t (29)
further, the step S5 specifically includes:
step S501, initializing data, initializing the position and speed of a particle population, setting the population size to Q (according to the size of the problem to be optimized), wherein the position of each particle in the population corresponds to a scheduling scheme in a scheduling period, a time period in one particle is set as a dimension of a solution space, and the Q-th particle is:
wherein P is 1 To P T Representing time-sharing heat price of steam heat supply network in each time period, and setting p initially 0 To p 1 Random value of Z 1 To Z T Representing the load shedding factor vector for all users in the 1 st through T-th time periods, each particle is initially a random value in the range that the hot user is individually receptive to:
Z t,q ＝[random(z 1,t ),random(z 2,t ),......,random(z N,t )] (31)
where N is the total number of hot users involved in IBDR.
Initializing the speed of particle q:
step S502, inputting individual particles into a steam heat supply network optimization scheduling model, and calculating an objective function value corresponding to each particle as the individual fitness value:
step S503, the position and speed of the particle q are updated according to the historical optimal value qbest of the particle q and the historical optimal value gbest of all the current particle groups:
wherein w is an inertia coefficient; r is (r) 1 、r 2 The value range is [0,1] as two random functions]To increase search randomness; c 1 、c 2 Two acceleration constants are used to adjust the learning maximum step size.
Step S504, judging whether a termination condition is met, taking the termination condition as that the residual error of the historical optimal value of the particle swarm is smaller than a certain range, wherein the specific range can be determined according to specific model requirements, defaulting to take the residual error of the historical optimal value of less than 0.1% as the termination condition, and outputting a final optimal scheduling result if the termination condition is met.
Further, step S6, the steam heat network is coordinated and optimized according to the final scheduling and pricing schemes of the two scheduling mechanisms of IBDR and PBDR,
outputting an optimal scheduling result according to the model, namely:
wherein P is t,op Representing the optimal heating price of the steam heating network in the t-th period; z is Z t,op To represent the load shedding factor vector for each hot user participating in the IBDR scheduling mechanism during the t-th period:
Z t,op ＝[z 1,t ,z 2,t ,......,z N,t ] (37)
in different time periods t, according to the result P output by the model t,op Setting heat supply prices in different time periods according to Z t,op The operation state of the hot users participating in the IBDR schedule in the steam heat network is adjusted.
And the thermal company distributes the optimized price information and the dispatching scheme on a platform, and a thermal user acquires related information to adjust the used steam quantity.
The beneficial effects of the invention are as follows:
the invention provides a steam heat supply network dispatching optimization method based on demand response, which considers the heat utilization characteristics of different heat users, and dispatches the heat utilization behaviors of the different heat users by using the demand response method, thereby reducing the peak demand of a heat supply network and smoothing the load fluctuation of the steam heat supply network; the method is characterized in that an excitation type demand response IBDR mechanism is improved, a PBDR mechanism and an improved IBDR mechanism are combined and creatively applied to the optimal scheduling of the steam heating network, a thermal company monitors the running state of the current heating network in real time in the background and obtains an optimal running scheduling scheme by means of model calculation, real-time scheduling information is issued through a cloud platform, a thermal user adjusts own heating behaviors according to the scheme which is good with the thermal company before, and load fluctuation of the steam heating network is smoothed, so that the purposes of optimizing the running of the steam heating network, reducing cost and improving benefit are achieved.
Drawings
The invention will be further described with reference to the drawings and examples.
FIG. 1 is the main steps of the method of the present invention.
FIG. 2 is a schematic diagram of an information interaction and scheduling platform.
Detailed Description
The invention will now be described in further detail with reference to the accompanying drawings. The drawings are simplified schematic representations which merely illustrate the basic structure of the invention and therefore show only the structures which are relevant to the invention.
The invention considers the heat utilization characteristics of different heat users, and utilizes the demand response method to schedule the heat utilization behaviors of different heat users, thereby reducing the peak demand of the heat supply network, smoothing the demand profile, cutting peaks and filling valleys, smoothing the load fluctuation of the steam heat supply network, avoiding the occurrence of the phenomenon of 'water hammer' in the pipe network, and reducing the overall heat energy supply cost and the operation and maintenance cost.
The present model predictive control method will be described below with reference to example 1.
Example 1
With reference to fig. 1 and 2, the on-demand precise regulation and control method of the model-based central heating system of the invention comprises the following steps:
step S1, a steam heat user is connected to a cloud platform which is established by leading a thermal company, and a steam price and a scheduling scheme which are issued by the thermal company are received in time;
step S2, acquiring historical load change conditions of each heat user before demand response, and carrying out situation assessment and classification on the historical load change conditions;
step S3, agreeing with different types of hot users to participate in a demand response mechanism, a response period and a load, and signing a corresponding agreement with the mechanism;
s4, establishing a steam heat supply network optimization scheduling model and an objective function and constraint conditions thereof;
s5, solving a steam heat supply network optimization scheduling model by using an optimization algorithm;
and step S6, final scheduling and pricing schemes based on two scheduling mechanisms of incentive and price are published on a scheduling platform, and the amount of steam used is regulated by a hot user.
Step S1, as shown in FIG. 2, a steam heat user is connected to a cloud platform which is established by a heat power company in a leading mode, the cloud platform is used for issuing steam price and scheduling schemes, the heat power company issues the steam price and scheduling schemes on the cloud platform according to the result of a background optimization model, and the heat user can receive relevant information at a mobile terminal in time, so that the steam consumption is adjusted.
And S2, acquiring historical load change conditions of each heat user before demand response, and carrying out situation assessment and classification on the historical load change conditions. Comprising the following steps:
step S201, obtaining the heat load demand change conditions of each heat user in different heat use periods from the data recorded at the measuring device installed by each heat user, and recording the heat price of the steam heat supply network, and preparing for manufacturing a demand response machine for each heat user;
step S202, predicting the heat load demands of different heat users in a steam heat supply network, evaluating and classifying situations, wherein one type is that the heat consumption period of the heat load is relatively fixed, the heat consumption is relatively regular and stable, the heat consumption is not easy to carry out large-degree scheduling, the living heat of residents is mainly used, and the industrial loads (chemical industry and pharmacy) of continuous production are not flexibly scheduled; the other class presents more uncertainty and fluctuation, such as industrial heat (intermittent, discontinuous), but the heat utilization period can be adjusted by adjusting the production plan, namely, the load of the industrial heat can be properly reduced in the peak period and the industrial heat can be utilized in the heat utilization valley period, so that the industrial heat (intermittent, discontinuous) can be divided into flexible dispatching loads.
And S3, agreeing the mechanism, the response time period and the load quantity of participation demand response with different types of hot users, and signing corresponding agreements with the mechanism, the response time period and the load quantity.
The mechanism of action of demand response is mainly two: price-type PBDR (Price-Based Demand Response) and Incentive IBDR (Incentive-Based Demand Response).
According to the load classification of the steam heat supply network, for the non-flexible scheduling load of domestic heat load, the proposal about the reduction or increase of the steam consumption of the heat supply users can be issued on a platform by the heat supply users in a certain quantity through the measure of exciting subsidization and negotiating in advance with the heat supply users in a certain quantity, and the heat supply users can select to cooperate with the heat supply companies to execute related operations so as to obtain a certain economic subsidization, thereby relieving the load of the steam heat supply network in a peak period, and the IBDR mechanism can be suitable for the non-flexible scheduling load; for flexible load scheduling, the PBDR and the IBDR can be used simultaneously, a higher heat price is firstly set in a heat consumption peak period according to a PBDR mechanism, a lower heat price is set in a heat consumption valley period, a heat user is encouraged to choose to use heat in the heat consumption valley period, further, a protocol is agreed with some heat users in advance, advice about suggesting the heat user to reduce or increase the steam consumption is issued on a platform in certain period, the heat user can choose to cooperate with a thermal company to execute related operation, so that certain economic subsidies are obtained, and the PBDR and the IBDR are overlapped, so that the peak clipping and valley filling effects are further achieved.
The mechanism of the steam heating network based on the demand response is modeled next. Firstly, according to the heat balance principle, the total heat load requirement of the steam heat supply network in the t period can be obtained:
H total,t ＝H 1,t +H 2,t (37)
wherein H is 1,t In order to not flexibly schedule the load in the t-th period, the expression is as follows:
H 1,t ＝H 1,IBDR,t (38)
H 2,t for flexible load scheduling in the t-th period, the expression is:
H 2,t ＝H PBDR,t +H IBDR,t (39)
wherein H is PBDR,t And H is IBDR,t The expressions of the heat user load demand power under price type demand response and incentive type demand response are respectively:
wherein alpha is n,t Is a thermal user load shedding factor after IBDR demand response mechanism is performed;
z n,t for IBDR, is a span of intervals, which is the closed interval [0,1]]Is a subset of the (1) and represents the variation range of IBDR response degree of the nth numbered hot user in the t time period, wherein '0' represents stop of heat supply, '1' represents normal rated heat supply, if z n,t ＝[0.2,0.4]The closed section indicates that a heat user receives the heat load reducing requirement to be 0.2-0.4 times of the original load in the t-th period; h is a n,t Rated heat load demand for the nth numbered heat consumer;for the heat load demand of the mth numbered heat consumer under price demand response, +.>An initial thermal load demand for the mth thermal user, e m,tt And e m,st The heat demand elastic coefficient and the cross demand elastic coefficient of the heat user are numbered for the mth; p (P) t The heat supply unit price, P, of the heat supply network is optimized by adopting a demand response mechanism in the t period of the heat supply network t 0 For the heating price of the PBDR at time t before implementation, N is the number of heat users participating in IBDR, and M is the number of heat users participating in PBDR.
According to the principle of economic demand, the mechanism of action of PBDR on heat load demand response is mainly described by the demand elastic coefficient, and the elastic relationship between heat load demand and heat supply price is described as follows:
wherein s and T are time instants s, t=1, 2, …, T;and P t 0 The heat supply load at the time s and the heat supply price at the time t before PBDR implementation are respectively; ΔL s And DeltaP t The heat load fluctuation amount at time s and the heat supply price fluctuation amount at time t after the PBDR is implemented are respectively. The load demand fluctuation after the user participates in the PBDR is calculated as follows:
to calculate the load demand of a user after participating in PBDR, first define the user's thermal energy consumption value V (L t ) The net value of heat energy consumption of the user is:
π＝V(L t )-L t P t (46)
next, the first derivative and the second derivative with respect to L are obtained for the formula (8), and the derivative value is set to zero, thereby obtaining
Again, if the original load demand is determinedAfter that, the electric energy consumption value V (L t ) And (3) carrying out Taylor expansion to obtain the consumption value of the electric energy of the user after the Taylor expansion:
at this time, by substituting the formulas (11) and (12) into the formula (13), the simplified user heat consumption value can be obtained:
regarding L, the method of (14) t Can be obtained in combination with formula (11):
equation (15) calculates the load demand after considering the influence of the self-elasticity at time t, and in order to consider the load demand under the cross elasticity, the correction equation (15) is available:
finally, combining equation (15) and equation (16) can yield the final PBDR model, specifically as follows:
next, a mechanism for participating in demand response, a response period and a load amount are agreed with the hot user, and a corresponding agreement is signed with the mechanism. More specifically, the thermal company negotiates with each thermal user to inform the thermal company of the mechanism of the motivating demand response, namely, during the peak period of heat consumption, the thermal company will issue suggestions on the dispatching platform about the reduction or increase of the steam usage, and if the thermal user can execute related operations according to the suggestions, the thermal user can obtain corresponding economic compensation or discount of the thermal price through a pre-signed agreement. The thermal company counts the response time period and the response load quantity of the heat user participating in the IBDR, for example, the heat load demand of the heat user i in three time periods of t-1, t, t+1 can be accepted as the original three intervals of [0.4,0.6], [0.2,0.8] and [0,1] after the demand response, and then the IBDR of the user in the three time periods is identified as follows:
z i,t-1 ＝[0.4,0.6],z i,t ＝[0.2,0.8],z i,t+1 ＝[0,1] (54)
if the hot user indicates not to participate in IBDR for a certain period of time t-2, then z will be i,t-2 Setting the value "1" indicates that no change is made during this period.
Step S4, a steam heat supply network optimization scheduling model and an objective function and constraint conditions thereof are established, wherein the step comprises the following steps:
step S401, the maximum net benefit generated by the steam heating network for demand response scheduling is an objective function:
wherein A is the number of hot users capable of flexibly scheduling load; b is the quantity of hot users which cannot flexibly schedule the load; r is R PB The benefit obtained under price type demand response scheduling in one scheduling period is obtained; r is R IB Obtained for excitation type demand response scheduling in one scheduling periodThe obtained benefit; c (C) 1 The cost after scheduling for demand response mainly comprises heat production cost C p And operation and maintenance cost C m ；C 2 To give post-patch costs to participating IBDR hot users, where
R en Is an environmental benefit, and is composed of pollution gas with reduced emission:
in the method, in the process of the invention,and->Is CO 2 、SO 2 And NOx emission reduction; />And->Is CO 2 、SO 2 And the emission reduction value of NOx.
Wherein, c o,t And c m,t The production cost and the operation and maintenance cost of the unit heat in different time periods are respectively. h is a IBDR,i Load regulation for participating IBER for ith hot user, p i The subsidy unit price determined by the negotiation of the thermal company and the thermal user is provided.
Step S402, according to the operation characteristics of the steam heat supply network, providing constraint conditions for the stable operation of the steam heat supply network, including heat load supply and demand balance constraint conditions, heat source operation constraint and load reduction constraint.
Heat load supply and demand balance constraint conditions:
H out,t ＝H total,t (61)
wherein S is i Is the heating power of the ith heat source in the steam heating network in t time periods.
Heat source operating load constraints:
S i,min ≤S i ≤S i,max (63)
wherein S is i,min And S is equal to i,max The minimum and maximum heating power when the ith heat source operates.
Load shedding constraint 1, for PBDR:
this constraint is directed to the amount of load reduction for hot users under the PBDR mechanism,and->The load demands of the hot users before and after PBDR scheduling are respectively shown in the formula, wherein the formula shows that in order to ensure the stable operation of the heat supply network, a maximum value exists for the heat load reduction quantity of the mth hot user, and the maximum value is taken as the predicted heat in the t period of the hot user by default20% of the load demand.
Load shedding constraint 2, for IBDR:
is required to meet the load reduction factor alpha in any period n,t Within the range negotiated with hot user n:
α n,t ∈z n,t (65)
step S5, solving a steam heat supply network optimization scheduling model by using an optimization algorithm, taking a particle swarm algorithm as an example, and specifically comprising the following steps:
step S501, initializing data, initializing the position and speed of a particle population, setting the population size to Q (according to the size of the problem to be optimized), wherein the position of each particle in the population corresponds to a scheduling scheme in a scheduling period, a time period in one particle is set as a dimension of a solution space, and the Q-th particle is:
wherein P is 1 To P T Representing time-sharing heat price of steam heat supply network in each time period, and setting p initially 0 To p 1 Random value of Z 1 To Z T Representing the load shedding factor vector for all users in the 1 st through T-th time periods, each particle is initially a random value in the range that the hot user is individually receptive to:
Z t,q ＝[random(z 1,t ),random(z 2,t ),......,random(z N,t )] (67)
where N is the total number of hot users involved in IBDR.
Initializing the speed of particle q:
step S502, inputting individual particles into a steam heat supply network optimization scheduling model, and calculating an objective function value corresponding to each particle as the individual fitness value:
step S503, the position and speed of the particle q are updated according to the historical optimal value qbest of the particle q and the historical optimal value gbest of all the current particle groups:
wherein w is an inertia coefficient; r is (r) 1 、r 2 The value range is [0,1] as two random functions]To increase search randomness; c 1 、c 2 Two acceleration constants are used to adjust the learning maximum step size.
Step S504, judging whether a termination condition is met, taking the termination condition as that the residual error of the historical optimal value of the particle swarm is smaller than a certain range, wherein the specific range can be determined according to specific model requirements, defaulting to take the residual error of the historical optimal value of less than 0.1% as the termination condition, and outputting a final optimal scheduling result if the termination condition is met.
And S6, carrying out coordination optimization on the steam heat supply network according to final scheduling and pricing schemes of two scheduling mechanisms of IBDR and PBDR.
Outputting an optimal scheduling result according to the model, namely:
wherein P is t,op Representing the optimal heating price of the steam heating network in the t-th period; z is Z t,op To represent the load shedding factor vector for each hot user participating in the IBDR scheduling mechanism during the t-th period:
Z t,op ＝[z 1,t ,z 2,t ,......,z N,t ] (37)
in different time periods t, according to the result P output by the model t,op Setting heat supply prices in different time periods according to Z t,op The operation state of the hot users participating in the IBDR schedule in the steam heat network is adjusted.
And the thermal company distributes the optimized price information and the dispatching scheme on a platform, and a thermal user acquires related information to adjust the used steam quantity.
Claims (5)
1. A steam heat supply network optimization scheduling method based on demand response is characterized by comprising the following steps:
step S1, a steam heat user is connected to a cloud platform which is established by leading a thermal company, and a steam price and a scheduling scheme which are issued by the thermal company are received in time;
step S2, acquiring historical load change conditions of each heat user before demand response, and carrying out situation assessment and classification on the historical load change conditions;
step S3, agreeing with different types of hot users to participate in a demand response mechanism, a response period and a load, and signing a corresponding agreement with the mechanism;
s4, establishing a steam heat supply network optimization scheduling model and an objective function and constraint conditions thereof;
s5, solving a steam heat supply network optimization scheduling model by using an optimization algorithm;
step S6, publishing final scheduling and pricing schemes of two scheduling mechanisms based on incentive and price on a scheduling platform, and regulating the used steam amount by a hot user;
the step S3 is as follows:
for heat users incapable of flexibly dispatching loads, an excitation type IBDR mechanism is adopted, and the heat users and part of heat users negotiate in advance to determine the heat use peak period through the measure of excitation subsidy, so that a thermodynamic public company can issue suggestions about the reduction or increase of the steam use amount of the heat users on a platform, and the heat users can select to cooperate with a thermodynamic company to execute related operations to obtain certain economic subsidy, thereby relieving the load of a steam heat network in the peak period;
for a heat user with flexibly-scheduled load, a price type PBDR and an incentive type IBDR mechanism are adopted, according to the PBDR mechanism, firstly, the heat price is increased in the heat consumption peak period, the heat price is reduced in the heat consumption valley period, the heat user is encouraged to select to use heat in the heat consumption valley period, and agreements are made with part of the heat users in advance, in certain period, a heat company can issue suggestions about the heat user to reduce or increase the steam consumption on a platform, the heat user can select to cooperate with a heat company to execute related operations, a certain economic subsidy is obtained, and the peak clipping and valley filling effects are further achieved through superposition of the price type PBDR and the incentive type IBDR mechanism;
the thermal company counts the response time period and the response load quantity of the heat user participating in the IBDR, and uses the IBDR identification in the corresponding time period to represent the load change receiving interval of the heat user, if the heat load demand of the heat user i in the time period t is between a and b times of the original heat load demand after the heat load demand is responded, the IBDR identification of the user in the time period is: z i,t ＝[a，b]Wherein 0.ltoreq.a < 1, 0.ltoreq.b < 1; if hot user i indicates not to participate in IBDR for a certain period of time t-2, then z will be i,t-2 Setting a value of "1" to indicate that the thermal load does not change during this period;
the step S4 specifically comprises the following steps:
step S401, the maximum net benefit generated by the steam heat supply network for demand response scheduling is an objective function;
step S402, according to the operation characteristics of the steam heat supply network, providing constraint conditions for the stable operation of the steam heat supply network, wherein the constraint conditions comprise heat load supply and demand balance constraint conditions, heat source operation constraint and load reduction constraint;
in the step S401, the maximum net benefit generated by the steam heating network in response to the demand response scheduling is an objective function, specifically:
wherein A is the number of hot users capable of flexibly scheduling load; b is inflexible in scheduling loadNumber of hot users; r is R PB The benefit obtained under price type demand response scheduling in one scheduling period is obtained; r is R IB The benefits obtained under excitation type demand response scheduling in one scheduling period are obtained; r is R en Is environmental benefit and is composed of pollution gas with reduced emission; c (C) 1 Costs after scheduling for demand response, including heat production cost C p And operation and maintenance cost C m ；C 2 To give the participation incentive IBDR thermal users the cost of post subsidizing, its unit price is decided by the thermal company and thermal users negotiation, post settlement;
wherein H is PBDR,t And H is IBDR,t Representing heat user load demand power, P, under price type demand response and incentive type demand response, respectively t The heat supply unit price of the heat supply network is optimized by adopting a demand response mechanism in the t period,and->Is CO 2 、SO 2 And NOx emission reduction; />And->Is CO 2 、SO 2 With the value of the emission reduction of NOx,
wherein, c o,t And c m,t The production cost and the operation and maintenance cost of the unit heat in different time periods are respectively H total,t The total heat load requirement of the steam heat supply network in the t-th period; h is a IBDR,i Load regulation for participation of ith hot user in IBDR, p i The subsidy unit price determined by negotiation of the thermal company and the thermal user is provided;
in the step S402:
the heat load supply and demand balance constraint conditions are as follows:
H out,t ＝H total,t (7)
wherein S is i The heating power of the ith heat source in t time periods in the steam heating network is set;
the heat source operating load constraints are:
S i,min ≤S i ≤S i,max (9)
wherein S is i,min And S is equal to i,max The minimum and maximum heating power when the ith heat source operates are respectively;
load shedding constraint 1, for PBDR:
the constraint aims at the load of a hot user under the PBDR mechanismThe amount of the cutting-off amount is reduced,and->The load demands of the heat users before and after PBDR scheduling are respectively shown in the formula, wherein the maximum value exists for the heat load reduction of the mth heat user, and the maximum value is 20% of the predicted heat load demands in the t period of the heat user;
load shedding constraint 2, for IBDR:
is required to meet the load reduction factor alpha in any period n,t Within the range negotiated with hot user n:
α n,t ∈z n,t (11)
z n,t is a range of intervals, which is a closed interval [0,1]]Is indicative of the extent of IBDR response by heat consumer n over the t-th period, "0" means to stop heating and "1" means to normal nominal heating.
2. The steam heat supply network optimization scheduling method based on demand response as claimed in claim 1, wherein:
in the step S1, a steam heat user is connected to a cloud platform which is mainly constructed by a thermal power company and is used for issuing a steam price and a scheduling scheme, the thermal power company issues the steam price and the scheduling scheme on the cloud platform according to the result of a background optimization model, and the heat user can receive relevant information at a mobile terminal in time, so that the steam consumption is adjusted.
3. The steam heating network optimizing and scheduling method based on demand response according to claim 1, wherein the step S2 specifically comprises the following steps:
step S201, obtaining the heat load demand change conditions of each heat user in different heat use periods from the data recorded at the measuring device installed by each heat user, and recording the heat price of the steam heat supply network, and preparing for manufacturing a demand response machine for each heat user;
step S202, obtaining the total heat load demand H of the heat supply network in the future period by predicting the heat load demands of different heat users in the steam heat supply network total,t And carrying out situation assessment and classification on the heat utilization conditions of all heat users, wherein one type is the load which can not be flexibly scheduled, and the other type is the load which can be flexibly scheduled.
4. The steam heating network optimization scheduling method based on demand response according to claim 1, wherein step S5 is specifically implemented by solving a steam heating network optimization scheduling model by adopting a particle swarm algorithm, and includes the following steps:
step S501, initializing data, initializing the position and speed of a particle population, setting the population scale to Q, wherein the position of each particle individual in the population corresponds to a scheduling scheme in a scheduling period, and the Q-th particle is:
wherein P is 1 To P T Representing time-sharing heat price of steam heat supply network in each time period, and setting p initially 0 To p 1 Random value of Z 1 To Z T Representing the load shedding factor vector for all users in the 1 st through T-th time periods, each particle is initially a random value in the range that the hot user is individually receptive to:
Z t,q ＝[random(z 1,t ),random(z 2,t ),......,random(z N,t )] (13)
wherein N is the total number of hot users involved in IBDR;
initializing the speed of particle q:
step S502, inputting individual particles into a steam heat supply network optimization scheduling model, and calculating an objective function value corresponding to each particle as the individual fitness value:
step S503, the position and speed of the particle q are updated according to the historical optimal value of the particle q and the historical optimal values in all the current particle groups;
step S504, judging whether a termination condition is met, taking the termination condition as that the residual error of the particle swarm history optimal value is smaller than a certain range, and if the termination cycle is met, outputting a final optimal scheduling result.
5. The steam heating network optimizing and scheduling method based on demand response according to claim 1, wherein the step S6 is specifically
Outputting an optimal scheduling result according to the model, namely:
wherein P is t,op Representing the optimal heating price of the steam heating network in the t-th period; z is Z t,op To represent the load shedding factor vector for each hot user participating in the IBDR scheduling mechanism during the t-th period:
Z t,op ＝[z 1,t ,z 2,t ,......,z N,t ] (17)
n is the total number of hot users participating in IBDR, and in different time periods t, the result P is output according to the model t,op Setting heat supply prices in different time periods according to Z t,op Adjusting the running state of a heat user participating in IBDR scheduling in a steam heat supply network;
the results are published on a dispatch platform and the amount of steam used is regulated by the hot user.
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