CN111429020B - Multi-time scale economic dispatching method of electric heating system considering heat storage characteristics of heat supply network - Google Patents
Multi-time scale economic dispatching method of electric heating system considering heat storage characteristics of heat supply network Download PDFInfo
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Abstract
The invention discloses a multi-time scale economic dispatching method of an electric heating system considering heat storage characteristics of a heat supply network, which comprises the steps of firstly, predicting wind power prediction errors by using an LSTM neural network, introducing the wind power prediction errors into a multi-time scale dispatching model of an electric heating comprehensive system, considering the heat storage characteristics and time delay characteristics of the heat supply network into a multi-time scale strategy, utilizing the heat storage characteristics and the time delay characteristics of the heat supply network in a day-ahead dispatching model and a day-ahead dispatching model to ensure that the heat output of the system does not need to be balanced with heat load in real time, so as to stabilize part of the wind power prediction errors, and further reducing the influence of the wind power prediction errors on the electric heating system by utilizing an energy storage device in the real-time dispatching model. Therefore, the air discarding quantity can be reduced, the system operation cost can be reduced, and the capacity of the energy storage device can be reduced.
Description
Technical Field
The invention relates to a multi-time scale economic dispatching method of an electric heating system considering heat storage characteristics of a heat supply network, and belongs to the field of uncertainty analysis of wind power and new energy grid-connected dispatching.
Background
In recent years, the large-scale development of wind power brings clean energy, and meanwhile, the problems of wind abandoning and the like are also caused by the randomness and fluctuation characteristics of the wind power. In the three north areas of China, the peak regulation capacity of the cogeneration unit is limited due to the coupling characteristic of heat and electricity, and a rising space cannot be provided for wind power absorption, so that the wind abandoning is serious. The proposal of the energy Internet provides a new way for energy interconnection, breaks the limit among energy subsystems such as electricity, heat, cold, gas, traffic and the like, enables energy to be mutually converted, and improves the energy utilization rate. The study of the electric heating integrated system can break through the limit of 'using heat to fix electricity', so that the electric power system and the thermodynamic system operate in a coordinated manner, and the waste wind can be effectively reduced.
At present, students at home and abroad have studied about wind power consumption of an electric heating comprehensive system. The method is mainly divided into three aspects, 1) electric heat conversion equipment or an energy storage device is added to promote wind power absorption, and if a learner proposes an absorption wind curtailment scheme for improving the peak regulation capacity of a unit by configuring heat storage in a thermal power plant. The scholars propose a scheme for configuring an electric boiler for a cogeneration unit to consume the waste wind power. The scholars propose an electric boiler heat supply amount calculation method based on a waste wind consumption coordination scheduling model of the heat storage cogeneration unit and the electric boiler, and the limit waste wind electric quantity consumption is provided, so that the economy of different working modes of the heat storage device and the heat storage cogeneration unit and the electric boiler coordination heat supply are compared. The scholars also propose a method for arranging a heat accumulating electric boiler in an electric heating system to consume wind power. The start-stop control strategy of the peak shaving electric boiler is researched by students, and an electric heating combined system optimization scheduling model based on peak shaving of the secondary heat supply network electric boiler is constructed. There are also studies on the heat pump and air source heat pump arranged in the electric heating system to consume wind power. 2) And the self characteristics of the heat supply network are considered to promote wind power consumption, and if a learner researches a combined heat and power dispatching model which considers the dynamic characteristic constraint of the heat supply pipeline and the heat storage device model, the system running cost is reduced and the wind power consumption is promoted. The learner establishes an electrothermal system model considering the constraint of the thermodynamic network and analyzes the promotion effect of the model on wind power. The heat storage capacity of the existing thermodynamic network is utilized by students to improve the operation flexibility of the power system so as to adapt to a large amount of variable wind energy. Researchers have studied using thermal inertia of a heating area to reduce wind power intermittence and randomness. The learner considers the thermal dynamic characteristics of heat supply pipeline transmission time delay, heat loss and the like in the thermodynamic system and the flexibility of the heat supply requirement of the user, and establishes an electricity-heat comprehensive energy system optimization scheduling model considering the heat storage characteristic of the heat supply network. Document [26] researches a central heating system to provide a CHP unit allowable heat output interval meeting the requirement of maximizing the power output flexibility of the CHP unit, thereby providing a rising space for wind power absorption. The heat storage and release characteristics of a heat supply network and the heat inertia of a heat supply area are utilized by students to absorb waste wind, and an improved combined heat and power scheduling strategy is provided, so that the traditional real-time heat load balancing constraint condition is replaced by maintaining the indoor temperature within a desired range. The students analyze the capacity of participating in the electric heating system to consume wind power under the excitation of time-sharing electricity price aiming at the dynamic characteristics of time delay, heat storage and the like of the heat supply network. The learner establishes an optimal scheduling model of the electric heating combined system considering the characteristics of the heat supply network and the heat load comfort degree elasticity, and not only considers the characteristics of the heat supply network, but also considers the influence of the heat load comfort degree elasticity on the wind power absorption. 3) An improved scheduling strategy is provided at the system side to improve the capacity of absorbing wind power, if a learner considers that the load and wind power prediction accuracy are improved along with the shortening of prediction time, the influence of prediction errors on a scheduling plan is reduced step by adopting rolling scheduling, a multi-time scale rolling scheduling strategy of an electric heating combined system is provided, and a scheduling model of three time scale scheduling plans of day before, rolling and real time is established. The learner considers the multi-type cogeneration unit and the large-scale wind power to establish the electric heating combined rolling scheduling model. The scholars provide an electric heating coordination scheduling method considering the peak shaving activity of the cogeneration for excavating the peak shaving capacity of the cogeneration unit. However, the research of the characteristics of the heat supply network proposed by the above document is only to use the heat supply network as heat storage for decoupling the limitation of the heat and power cogeneration unit by heat fixation, so that a rising space is provided for wind power consumption, but there are two problems in a model, namely, the uncertainty of wind power is not considered, the error exists in wind power prediction, the wind power prediction error is not considered, the influence on the dispatching operation of the electric heating system is brought about, and the application of the transmission delay characteristic of the heat supply network in the multi-time scale dispatching of the electric power system is not considered, so that the heat storage and release function generated by the transmission delay characteristic of the heat supply network cannot be used for compensating the wind power prediction error.
Disclosure of Invention
In order to overcome the defects of the prior art, the invention provides the multi-time scale economic dispatching method of the electric heating system, which considers the heat storage characteristic of the heat supply network, considers the heat storage characteristic and the time delay characteristic of the heat supply network in a multi-time scale strategy, and makes a set start-stop and output and energy storage charging and discharging plan, so that the air discarding quantity can be reduced, the system running cost can be reduced, and the capacity of an energy storage device can be reduced.
The invention is realized by the following technical scheme: the multi-time scale economic dispatching method of the electric heating system considering the heat storage characteristic of the heat supply network is characterized by comprising the following steps of:
step 1: predicting wind power prediction errors by using an LSTM neural network;
step 2: taking the heat storage characteristic and the time delay characteristic of the heat supply network into consideration in a multi-time scale strategy;
step 3: based on the prediction error of the step 1 and the scheduling strategy of the step 2, a multi-time scale scheduling model of the electric heating comprehensive system is established, and a set start-stop and output force and an energy storage charge-discharge plan are formulated.
Further, the step 2 includes the following specific steps: the scheduling model is divided into 3 time scales, and a day-ahead scheduling model ([ T, t+T)]) Intra-day rolling correction model ([ t.t +16)]) And real-time correction model ([ t, t+1)]) Taking 15min as 1 scheduling period; when the machine is scheduled in the day before, the machine is started and stopped, the primary output of the machine, the water supply temperature and the water return temperature of the heat source and the heat exchange station are carried out; according to the obtained wind power prediction error e during the rolling correction in the day S The heat storage and release characteristics of the heat supply network are utilized to adjust the output of the unit, the water supply temperature and the water return temperature of the heat source and the heat exchange station; because the response speed of the heat supply network is slower, the wind power prediction error cannot be compensated in the real-time scheduling stage, the energy storage device is utilized to further compensate the residual wind power prediction error e on the basis of the wind power prediction error compensation of the heat supply network during real-time correction F 。
Further, the step 3 of establishing the multi-time scale scheduling model of the electrothermal integrated system comprises the following steps:
step 3.1 day-ahead scheduling model: the day-ahead scheduling is a scheduling plan 24 hours before the predicted time, and mainly comprises the steps of making a conventional unit start-stop, and supplying water temperature and backwater temperature of the conventional unit, the cogeneration unit, the heat source and the heat exchange station;
the cogeneration unit is set to be in a normally open state, only the running cost is considered, the running cost of the cogeneration unit, the starting-up cost of the conventional unit, the running cost of the conventional unit and the wind discarding cost are taken as objective functions, and the formula is as follows:
min F D ＝min(C NC D +C NG，s D +C NG，p D +C loss D )
wherein the method comprises the steps of
Wherein: subscripts n and t are unit numbers and period numbers, and subscript D represents variables in a day-ahead scheduling model and F D Expressed as total cost of the system before day, C NC ,C NG,s ,C NG,p And C loss The method comprises the steps of representing a cogeneration unit operation cost function, a conventional unit starting cost function, a conventional unit operation cost function and a waste air cost function; a, a i chp ，b i chp ，c i chp Expressed as the cost coefficient of the ith cogeneration unit, a i ，b i ，c i Expressed as the cost coefficient of the ith conventional unit, CS i,t The method is represented as a starting cost coefficient of an ith thermal power generating unit at a moment t, U i,t The start-stop state of the ith thermal power generating unit at the time t is shown as day-ahead, 1 is started, 0 is stopped, and P i,t chp ,P i,t ,P i,t W0 And P i,t W Respectively expressed as the output of the ith cogeneration unit, the output of the conventional unit, the predicted active output of wind power and the actual dispatching active output of wind power at the t moment before the day,NC, NG and NW are the numbers of cogeneration units, conventional units and wind power plants, and T is a scheduling period;
the constraint conditions include:
1) Electric power balance constraint
Wherein: p (P) t D,L Expressed as the total electrical load power before day;
2) Heat source, heat exchange station water supply and return temperature and heat exchange constraint
Wherein: q (Q) i,t chp The heat power generated by the ith cogeneration unit is represented by W which is the flow heat equivalent value in the heat supply network and T SH,g,t The temperature T of the heat medium of the water supply pipeline at the heat source SH,h,t The temperature of the heat medium is the temperature of the return water pipeline at the heat source;
Q HE,t ＝ε e ·W·(T HE,g,t -T HE,h,t )
wherein: q (Q) HE,t Represented by the thermal power transferred by the heat exchange station epsilon e Is the effective coefficient of the heat exchange station, T HE,g,t The temperature T of the heat medium of the water supply pipeline at the primary side of the heat exchange station HE,h,t The temperature of the heat medium is the temperature of the primary side return water pipeline of the heat exchange station;
3) Pipeline thermal delay and temperature drop constraints
ΔT loss ＝k loss ·(T start (t)-T out (t))
Wherein: delta T loss T is the temperature drop of the pipeline start (T) is the temperature of the head end of the pipeline at the moment of T, T out (t) is the temperature of the external environment at the moment t, k loss Is the temperature loss coefficient;
T end (t)＝T start (t-T delay )-ΔT loss
wherein: t (T) end (t) is time tTemperature at the end of the pipe T delay Is the heat delay time of the heat supply pipeline.
4) Unit output upper and lower limits and climbing constraint
P i,min chp ≤P i,t chp ≤P i,max chp
U i,t P i,min ≤P i,t ≤U i,t P i,max
-R D,i chp Δt≤P i,t chp -P i,t-1 chp ≤R U,i chp Δt
-R D,i Δt≤P i,t -P i,t-1 ≤R U,i Δt
Wherein: p (P) i,min chp And P i,max chp The lower limit and the upper limit of the electric power of the i-th cogeneration unit are respectively P i,min And P i,max The lower limit and the upper limit of the electric power of the ith conventional unit are respectively R U,i chp And R is D,i chp Respectively represents the upper limit of the ascending and descending slopes of the electric power of the ith cogeneration unit, R U,i And R is D,i Respectively representing the upper limit of the ascending and descending slopes of the electric power of the ith conventional unit;
5) Wind power output constraint
0≤P i,t D,W ≤P i,t D,W0
6) Cogeneration electrothermal coupling constraint
Q i,t chp ＝η chp P i,t chp
Q i,min chp ≤Q i,t chp ≤Q i,max chp
Wherein: η (eta) chp Represents the thermoelectric ratio of the cogeneration unit, and eta is taken herein chp ＝0.75，Q i,min chp and Q i,max chp The lower limit and the upper limit of the heat power of the i-th cogeneration unit are respectively;
7) Heating pipe temperature constraint
T SH,g,t ≤T g max
T SH,h,t ≥T h min
Wherein: t (T) g max For the upper limit of the temperature of the water supply pipeline, T h min A lower temperature limit for the water supply line;
step 3.2, rolling the correction model within days: the intra-day correction is a plan 4 h-15 min before the predicted point, and is mainly based on the decision result before the day and the wind power prediction error e obtained by introducing S The output of the unit, the water supply temperature and the backwater temperature of the heat source and the heat exchange station are adjusted by utilizing the heat storage and release characteristics of the heat supply network;
taking the total cost of the system as an objective function, the formula is as follows:
min F S ＝min(C NC S +C NG，s S +C NG，p S +C loss S )
wherein the method comprises the steps of
Wherein: the superscript S denotes the variables in the day-ahead scheduling model, ΔP i,t ch P, and ΔP i,t Electric power adjustment amounts expressed as a cogeneration unit and a conventional unit;
the constraint conditions include:
1) Electric power balance constraint
0≤|e S1 |≤|e S |
Wherein: p (P) t S,L Expressed as total electric load power at time t in day, P t S,W Expressed as actual dispatch power of wind power in the day e S1 A prediction error compensated for the heat supply network by the stored heat energy;
2) Heat source, heat exchange station water supply and return temperature and heat exchange constraint
Wherein: delta T SH,g , t The temperature of the heating medium of the water supply pipeline at the heat source is regulated by delta T SH,h,t The temperature regulating quantity of the heat medium of the return water pipeline at the heat source is used; q (Q) HE,t ＝ε e ·W·((T HE,g,t +ΔT HE,g,t )-(T HE,h,t +ΔT HE,h,t ))
Wherein: delta T HE,g,t The temperature regulation quantity delta T of the heat medium of the primary side water supply pipeline of the heat exchange station HE,h,t The temperature adjustment quantity of the heat medium of the primary side return water pipeline of the heat exchange station is obtained;
3) Heating pipe temperature constraint
T SH,g,t +ΔT SH,g,t ≤T g max
T SH,h,t +ΔT SH,h,t ≥T h min
The unit output correction quantity constraint and the wind power output constraint are the same as those in step 3.1;
step 3.3, real-time correction of the model: the real-time correction plan is formulated 15min before the predicted point, and the wind power prediction error e after the heat supply network compensation in the intra-day scheduling is further compensated by utilizing the energy storage device F The objective is to take the minimum air discarding quantity and the minimum cut load quantity, namely the maximum charge and discharge quantity of the energy storage device as an objective function formula as follows:
min F F ＝min[e F (P i,t dc -P i,t ch )]
wherein: the superscript F denotes the variables in the day-ahead scheduling model, P ch And P dc Respectively representing the charging power and the discharging power of the electric storage device;
the constraint conditions include:
power storage device restraint
Wherein: u (U) i,t ch And U i,t dc Respectively representing a charging state and a discharging state of an ith power storage device at a moment t, wherein 1 is an operating state, and 0 represents an inactive state; p (P) i,t ch And P i,t dc Respectively representing the charging power and the discharging power of the ith power storage device at the moment t; p (P) i,min ch And P i,max ch Respectively represent a lower limit and an upper limit of charging power, P i,min dc And P i,max dc Respectively represent lower and upper discharge power limits, E i,t ESS Expressed as capacity of the ith power storage device at time t, E i,min ESS And E is i,max ESS Respectively representing the lower limit and the upper limit of the capacity of the ith power storage device, beta ch andβ dc Respectively representing a charging coefficient and a discharging coefficient;
the unit output correction quantity constraint and the wind power output constraint are the same as the step 3.1.
The invention has the beneficial effects that: according to the invention, the prediction error of wind power is predicted by utilizing an LSTM neural network, the prediction error is introduced into a multi-time scale scheduling model of an electric heating system taking the heat storage characteristic of a heat supply network into consideration, the transmission delay characteristic and the heat storage characteristic of the heat supply network are combined, the start-stop of a unit and the preliminary output of the unit are formulated in the day-ahead scheduling, the start-stop of the unit and the preliminary output of the unit are corrected by utilizing the heat storage capacity of the heat supply network according to the prediction error in the day-ahead scheduling, and the wind power prediction error is further reduced by an energy storage device in the real-time scheduling, so that the wind power consumption capacity of the electric heating system is improved, the running cost of the system is reduced and the capacity of the energy storage device is reduced.
Description of the drawings:
FIG. 1 is a flow chart of a multi-time scale rolling schedule for an electrothermal integrated system that takes into account the thermal storage characteristics of a heat supply network.
Fig. 2 shows Case1 and Case2 air reject volumes.
FIG. 3 is a Case1 and Case2 system heat output scenario.
Fig. 4 shows Case1 and Case2 energy storage charge and discharge conditions.
The specific embodiment is as follows:
the invention is further explained below with reference to the drawings.
A multi-time scale economic dispatching method of an electric heating system considering heat storage characteristics of a heat supply network comprises the following steps:
step 1: predicting wind power prediction errors by using an LSTM neural network;
step 2: taking the heat storage characteristic and the time delay characteristic of the heat supply network into consideration in a multi-time scale strategy; the method comprises the following specific steps: the scheduling model of the invention is divided into 3 time scales: day-ahead scheduling model ([ T, t+T)]) Intra-day rolling correction model ([ t.t +16)]) And real-time correction model ([ t, t+1)]) 1 scheduling period in 15 min. The first part is used for starting and stopping the unit, performing initial output of the unit, and supplying water temperature and backwater temperature of the heat source and the heat exchange station; the second part is based on the obtained wind power prediction error e S The heat storage and release characteristics of the heat supply network are utilized to adjust the output of the unit, the water supply temperature and the water return temperature of the heat source and the heat exchange station. Because the response speed of the heat supply network is slower, the wind power prediction error cannot be compensated in the real-time scheduling stage, the third part further compensates the rest wind power prediction error e by using the energy storage device on the basis of compensating the wind power prediction error by the heat supply network F . FIG. 1 illustrates the overall flow of a multi-time scale rolling schedule.
Step 3: based on the prediction error of the step 1 and the scheduling strategy of the step 2, a multi-time scale scheduling model of the electric heating comprehensive system is established, and a set start-stop and output force and an energy storage charge-discharge plan are formulated. The method comprises the following specific steps:
the scheduling model of the invention is divided into 3 time scales: day-ahead scheduling model ([ T, t+T)]) Intra-day rolling correction model ([ t.t +16)]) And real-time correction model ([ t, t+1)]) 1 scheduling period in 15 min. The first part firstly uses the improved VMD decomposition provided by the invention to predict the power data P of wind power day ahead D,W0 Divided into 3 layers, and the wind power day-ahead data P of 1 layer D,W0,U1 Making a unit start-stop, a unit and an electric boiler preliminary output; second part predicts the wind power within the day power data P by improving VMD decomposition S ,W0 Divided into 3 layers, and data P of 1 layer in wind power day is used S,W0,U1 Formulating the output adjustment quantity of a unit and an electric boiler, and generating layer 2 data P in the wind power day S,W0,U2 The method comprises the steps of preparing a charge-discharge plan of an energy type electricity storage device, a unit and an electric boiler output adjustment quantity; third part is passed throughImproving VMD decomposition to predict data P of wind power in real time F,W0 Divided into 3 layers, and wind power is used for generating real-time layer 2 data P F,W0,U2 The charge and discharge regulating variable of the energy type electricity storage device, the set and the output regulating variable of the electric boiler are prepared, and the wind power is used for generating real-time layer 3 data P F,W0,U3 And (5) making a charge and discharge plan of the power type electricity storage device. FIG. 1 illustrates the overall flow of a multi-time scale rolling schedule.
Step 3.1 day-ahead scheduling model: the day-ahead scheduling is a scheduling plan 24 hours before the predicted time, and mainly establishes the start and stop of a conventional unit, and the water supply temperature and the water return temperature of the conventional unit, the cogeneration unit, the heat source and the heat exchange station. The cogeneration unit is set to be in a normally open state, only the running cost is considered, the running cost of the cogeneration unit, the starting-up cost of the conventional unit, the running cost of the conventional unit and the wind discarding cost are taken as objective functions, and the formula is as follows:
min F D ＝min(C NC D +C NG，s D +C NG，p D +C loss D )
wherein the method comprises the steps of
Wherein: subscripts n and t are unit numbers and time period numbers; the superscript D represents a variable in the day-ahead scheduling model; f (F) D Expressed as total cost of the system before day, C NC ,C NG,s ,C NG,p And C loss The method comprises the steps of representing a cogeneration unit operation cost function, a conventional unit starting cost function, a conventional unit operation cost function and a waste air cost function; a, a i chp ，b i chp ，c i chp Expressed as the i-th cogeneration unit cost coefficient; a, a i ，b i ，c i Expressed as the cost coefficient of the ith conventional unit; CS (circuit switching) i,t The method is represented as a starting cost coefficient of an ith thermal power generating unit at a moment t; u (U) i,t The method is characterized in that the method is represented as a start-stop state of an ith thermal power unit before the day at the time t, wherein 1 is started, and 0 is stopped; p (P) i,t chp ,P i,t ,P i,t W0 And P i,t W Respectively representing the output of the ith cogeneration unit, the output of the conventional unit, the predicted active output of wind power and the actual dispatching active output of wind power at the t moment before the day; NC, NG and NW are the numbers of cogeneration units, conventional units and wind power plants, T is a scheduling period,
the constraint conditions include:
1) Electric power balance constraint
Wherein: p (P) t D,L Expressed as the total electrical load power before day.
2) Heat source, heat exchange station water supply and return temperature and heat exchange constraint
Wherein: q (Q) i,t chp The heat power generated by the ith cogeneration unit is represented by W which is the flow heat equivalent value in the heat supply network and T SH,g,t The temperature T of the heat medium of the water supply pipeline at the heat source SH,h,t The temperature of the heat medium is the temperature of the return water pipeline at the heat source.
Q HE,t ＝ε e ·W·(T HE,g,t -T HE,h,t )
Wherein: q (Q) HE,t Represented by the thermal power transferred by the heat exchange station epsilon e Is the effective coefficient of the heat exchange station, T HE,g,t The temperature T of the heat medium of the water supply pipeline at the primary side of the heat exchange station HE,h,t The temperature of the heat medium is the temperature of the primary side return water pipeline of the heat exchange station.
3) Pipeline thermal delay and temperature drop constraints
ΔT loss ＝k loss ·(T start (t)-T out (t))
Wherein: delta T loss T is the temperature drop of the pipeline start (T) is the temperature of the head end of the pipeline at the moment of T, T out (t) is the temperature of the external environment at the moment t, k loss As a temperature loss coefficient, the temperature of the material is,
T end (t)＝T start (t-T delay )-ΔT loss
wherein: t (T) end (T) is the temperature of the end of the pipeline at the moment T, T delay Is the heat delay time of the heat supply pipeline.
4) Unit output upper and lower limits and climbing constraint
P i,min chp ≤P i,t chp ≤P i,max chp
U i,t P i,min ≤P i,t ≤U i,t P i,max
-R D,i chp Δt≤P i,t chp -P i,t-1 chp ≤R U,i chp Δt
-R D,i Δt≤P i,t -P i,t-1 ≤R U,i Δt
Wherein: p (P) i,min chp And P i,max chp The lower limit and the upper limit of the electric power of the i-th cogeneration unit are respectively P i,min And P i,max The lower limit and the upper limit of the electric power of the ith conventional unit are respectively R U,i chp And R is D,i chp Respectively represents the upper limit of the ascending and descending slopes of the electric power of the ith cogeneration unit, R U,i And R is D,i Respectively represents the upper limit of the ascending and descending slopes of the electric power of the ith conventional unit.
5) Wind power output constraint
0≤P i,t D,W ≤P i,t D,W0
6) Cogeneration electrothermal coupling constraint
Q i,t chp ＝η chp P i,t chp
Q i,min chp ≤Q i,t chp ≤Q i,max chp
Wherein: η (eta) chp Represents the thermoelectric ratio of the cogeneration unit, and eta is taken herein chp ＝0.75，Q i,min chp and Q i,max chp Respectively the ith stationAnd the lower limit and the upper limit of the heat power of the cogeneration unit.
7) Heating pipe temperature constraint
T SH,g,t ≤T g max
T SH,h,t ≥T h min
Wherein: t (T) g max For the upper limit of the temperature of the water supply pipeline, T h min Is the lower temperature limit of the water supply pipeline.
Step 3.2, rolling the correction model within days: the intra-day correction is a plan 4 h-15 min before the predicted point, and is mainly based on the decision result before the day and the wind power prediction error e obtained by introducing S And the output of the unit, the water supply temperature and the backwater temperature of the heat source and the heat exchange station are adjusted by utilizing the heat storage and release characteristics of the heat supply network.
The total cost of the system is taken as an objective function. The formula is as follows:
min F S ＝min(C NC S +C NG ， s S +C NG ， p S +C loss S )
wherein the method comprises the steps of
Wherein: the superscript S represents the variables in the day-ahead scheduling model; ΔP i,t c h p And DeltaP i,t The electric power adjustment quantity is expressed as a cogeneration unit and a conventional unit.
The constraint conditions include:
1) Electric power balance constraint
0≤|e S1 |≤|e S |
Wherein: p (P) t S,L Expressed as total electric load power at time t in day, P t S,W Expressed as actual scheduling work of wind power in dayRate e S1 Prediction errors compensated for the heat supply network by the stored heat energy.
2) Heat source, heat exchange station water supply and return temperature and heat exchange constraint
Wherein: delta T SH,g,t The temperature of the heating medium of the water supply pipeline at the heat source is regulated by delta T SH,h,t The temperature regulating quantity of the heat medium in the return water pipeline at the heat source is obtained.
Q HE,t ＝ε e ·W·((T HE,g,t +ΔT HE,g,t )-(T HE,h,t +ΔT HE,h,t ))
Wherein: delta T HE,g,t The temperature regulation quantity delta T of the heat medium of the primary side water supply pipeline of the heat exchange station HE,h,t The temperature regulation quantity of the heat medium of the primary side return water pipeline of the heat exchange station is obtained.
3) Heating pipe temperature constraint
T SH,g,t +ΔT SH,g,t ≤T g max
T SH,h,t +ΔT SH,h,t ≥T h min
The unit output correction quantity constraint and the wind power output constraint are the same as the step 3.1.
Step 3.3, real-time correction of the model: the real-time correction plan is formulated 15min before the predicted point, and the wind power prediction error e after the heat supply network compensation in the intra-day scheduling is further compensated by utilizing the energy storage device F . The objective is to minimize the air discarding quantity and the cut load quantity, namely, the maximum charge and discharge quantity of the energy storage device is taken as an objective function formula as follows:
min F F ＝min[e F (P i,t dc -P i,t ch )]
wherein: the superscript F denotes the variables in the day-ahead scheduling model, P ch And P dc Respectively representing the charging power and the discharging power of the electric storage device;
the constraint conditions include:
power storage device restraint
Wherein: u (U) i,t ch And U i,t dc Respectively representing a charging state and a discharging state of an ith power storage device at a moment t, wherein 1 is an operating state, and 0 represents an inactive state; p (P) i,t ch And P i,t dc Respectively representing the charging power and the discharging power of the ith power storage device at the moment t; p (P) i,min ch And P i,max ch Respectively represent a lower limit and an upper limit of charging power, P i,min dc And P i,max dc Respectively represent lower and upper discharge power limits, E i,t ESS Expressed as capacity of the ith power storage device at time t, E i,min ESS And E is i,max ESS Respectively representing the lower limit and the upper limit of the capacity of the ith power storage device, beta ch andβ dc Respectively representing a charging coefficient and a discharging coefficient;
the unit output correction quantity constraint and the wind power output constraint are the same as the step 3.1.
The invention adopts a system comprising 8 thermal power generating units, 2 cogeneration units and an electricity storage device. And selecting a sizing flow working mode in the heat supply network working mode. Wind farm data was from the 2019 7-8 operating data published by Elia wind farm in belgium and normalized to 2600MW installed. The electrical storage device parameters are shown in table 1.
Table 1 parameters of the power store
In order to explain the influence of stabilizing wind power prediction errors in multi-time scale scheduling of an electric heating system by utilizing the self heat storage characteristic and the time delay characteristic of a heat supply network, the following 2 cases are set:
1) Case1: the scheduling model consists of a day-ahead, a day-in and a real-time scheduling model. And stabilizing the prediction error by using a day-ahead, day-in and real-time scheduling model according to the prediction error obtained in the first section. And stabilizing wind power prediction errors which cannot be compensated before and during the day by using an energy storage device in the real-time model. The characteristics of the heat supply network are not considered in the model.
2) Case2: the scheduling model consists of a day-ahead, a day-in and a real-time scheduling model. According to the wind power prediction error obtained in the first section, the heat storage and delay characteristics of the heat supply network are considered in a day-ahead and day-in scheduling model, the wind power prediction error is stabilized by using the day-ahead and day-in scheduling model, and finally the wind power prediction error which cannot be compensated in the day-ahead and day-in is stabilized by using an energy storage device in a real-time model.
Simulation results:
the Case1 and Case2 air reject volumes are shown in fig. 2. The Case1 and Case2 system heat output is shown in fig. 3. The Case1 and Case2 energy storage charge and discharge conditions are shown in fig. 4.
As shown in FIG. 2, after the heat storage characteristic and the time delay characteristic of the heat supply network are utilized, the air discarding quantity of the system is reduced from 1030.5MW to 50.3MW, 980.2MW is reduced, and 95.1% is reduced. As shown in FIG. 3, the heat output of the combined heat and power unit in Case1 mainly changes along with the change of the heat load, and the heat balance is kept at all times. And the heat output and the heat load of the thermal cogeneration unit in Case2 are asynchronous. If the heat output of the cogeneration unit in Case2 is obviously larger than the heat supply requirement in 5-15 hours, the cogeneration unit stores the surplus heat energy in the heat supply network at the moment so as to release the heat from the heat supply network in the rest period. And the heat output of the cogeneration unit in the case2 is smaller than the heat supply demand in 16-19h and 21-24h, and the heat energy stored in the heat supply network in the earlier stage is released at the moment, so that the shortage of part of the heat supply demand in the period is made up, and the output of CHP is reduced. As shown in FIG. 4, after the wind power prediction error is greatly stabilized through the heat storage characteristic and the time delay characteristic of the heat network, the rest of the wind power prediction error is stabilized by the energy storage device, the capacity of the energy storage device in Case1 is 2000MW, the capacity of the energy storage device in Case2 is 500MW, the capacity of Case2 is 1500MW less than that of the energy storage device in Case1, and the energy storage cost is greatly reduced.
Table 2 gives the total cost of the system and the reject volume for Case1 and Case 2.
TABLE 2 Total cost of the system
From the simulation results, the heat storage characteristic and the time delay characteristic of the heat supply network are considered in the multi-time scale of the electric heating system, so that the heat output of the combined heat and power generation unit in the system does not need to be balanced with the heat load in real time, the cross-period transfer of heat energy is realized, the coordination complementation of the electric heating system and the heat system in the space-time range is realized, the air discarding quantity and the system operation cost are effectively reduced, the capacity of the energy storage device is greatly reduced, and the energy storage cost is reduced.
The foregoing is merely a preferred embodiment of the present invention and it should be noted that modifications and adaptations to those skilled in the art may be made without departing from the principles of the present invention, which are intended to be comprehended within the scope of the present invention.
Claims (1)
1. The multi-time scale economic dispatching method of the electric heating system considering the heat storage characteristic of the heat supply network is characterized by comprising the following steps of:
step 1: predicting wind power prediction errors by using an LSTM neural network;
step 2: taking the heat storage characteristic and the time delay characteristic of the heat supply network into consideration in a multi-time scale strategy;
step 3: based on the prediction error of the step 1 and the scheduling strategy of the step 2, a multi-time scale scheduling model of the electric heating comprehensive system is established, and a set start-stop and output force and an energy storage charge-discharge plan are formulated;
the step 2 comprises the following specific steps: the scheduling model is divided into 3 time scales, and a day-ahead scheduling model ([ T, t+T)]) Intra-day rolling correction model ([ t.t +16)]) And real-time correction model ([ t, t+1)]) Taking 15min as 1 scheduling period; when the machine is scheduled in the day before, the machine is started and stopped, the primary output of the machine, the water supply temperature and the water return temperature of the heat source and the heat exchange station are carried out; according to the obtained wind power prediction error e during the rolling correction in the day S The heat storage and release characteristics of the heat supply network are utilized to adjust the output of the unit, the water supply temperature and the water return temperature of the heat source and the heat exchange station; because the response speed of the heat supply network is relatively highSlowly, wind power prediction errors cannot be compensated in a real-time scheduling stage, so that the energy storage device is utilized to further compensate the rest wind power prediction errors e on the basis of compensating the wind power prediction errors by the heat supply network during real-time correction F ；
The step 3 of establishing the multi-time scale scheduling model of the electric heating comprehensive system comprises the following steps:
step 3.1 day-ahead scheduling model: the day-ahead scheduling is a scheduling plan 24 hours before the predicted time, and mainly comprises the steps of making a conventional unit start-stop, and supplying water temperature and backwater temperature of the conventional unit, the cogeneration unit, the heat source and the heat exchange station;
the cogeneration unit is set to be in a normally open state, only the running cost is considered, the running cost of the cogeneration unit, the starting-up cost of the conventional unit, the running cost of the conventional unit and the wind discarding cost are taken as objective functions, and the formula is as follows: minF (minF) D ＝min(C NC D +C NG，s D +C NG，p D +C loss D )
Wherein the method comprises the steps of
Wherein: subscripts i and t are unit numbers and period numbers, and subscript D represents variables in a day-ahead scheduling model and F D Expressed as total cost of the system before day, C NC ,C NG,s ,C NG,p And C loss The method comprises the steps of representing a cogeneration unit operation cost function, a conventional unit starting cost function, a conventional unit operation cost function and a waste air cost function; a, a i chp ，b i chp ，c i chp Expressed as the cost coefficient of the ith cogeneration unit, a i ，b i ，c i Expressed as the cost coefficient of the ith conventional unit, CS i,t The method is represented as a starting cost coefficient of an ith thermal power generating unit at a moment t, U i,t The start-stop state of the ith thermal power generating unit at the time t is shown as day-ahead, 1 is started, 0 is stopped, and P i,t chp ,P i,t ,P i,t W0 And P i,t W Respectively representing the output of the ith cogeneration unit, the output of the conventional unit, the predicted active output of wind power and the actual dispatching active output of wind power at the T moment before the day, wherein NC, NG and NW are the numbers of the cogeneration unit, the conventional unit and the wind power plant, and T is the dispatching period;
the constraint conditions include:
1) Electric power balance constraint
Wherein: p (P) t D,L Expressed as the total electrical load power before day;
2) Heat source, heat exchange station water supply and return temperature and heat exchange constraint
Wherein: q (Q) i,t chp The heat power generated by the ith cogeneration unit is represented by W which is the flow heat equivalent value in the heat supply network and T SH,g,t The temperature T of the heat medium of the water supply pipeline at the heat source SH,h,t The temperature of the heat medium is the temperature of the return water pipeline at the heat source;
Q HE,t ＝ε e ·W·(T HE,g,t -T HE,h,t )
wherein: q (Q) HE,t Represented by the thermal power transferred by the heat exchange station epsilon e Is the effective coefficient of the heat exchange station, T HE,g,t The temperature T of the heat medium of the water supply pipeline at the primary side of the heat exchange station HE,h,t The temperature of the heat medium is the temperature of the primary side return water pipeline of the heat exchange station;
3) Pipeline thermal delay and temperature drop constraints
ΔT loss ＝k loss ·(T start (t)-T out (t))
Wherein: delta T loss T is the temperature drop of the pipeline start (T) is the temperature of the head end of the pipeline at the moment of T, T out (t) is the temperature of the external environment at the moment t, k loss Is the temperature loss coefficient;
T end (t)＝T start (t-T delay )-ΔT loss
wherein: t (T) end (T) is the temperature of the end of the pipeline at the moment T, T delay A thermal delay time for the heating conduit;
4) Unit output upper and lower limits and climbing constraint
P i,min chp ≤P i,t chp ≤P i,max chp
U i,t P i,min ≤P i,t ≤U i,t P i,max
-R D,i chp Δt≤P i,t chp -P i,t-1 chp ≤R U,i chp Δt
-R D,i Δt≤P i,t -P i,t-1 ≤R U,i Δt
Wherein: p (P) i,min chp And P i,max chp The lower limit and the upper limit of the electric power of the i-th cogeneration unit are respectively P i,min And P i,max The lower limit and the upper limit of the electric power of the ith conventional unit are respectively R U,i chp And R is D,i chp Respectively represents the upper limit of the ascending and descending slopes of the electric power of the ith cogeneration unit, R U,i And R is D,i Respectively representing the upper limit of the ascending and descending slopes of the electric power of the ith conventional unit; Δt is a scheduling period interval;
5) Wind power output constraint
0≤P i,t D,W ≤P i,t D,W0
6) Cogeneration electrothermal coupling constraint
Q i,t chp ＝η chp P i,t chp
Q i,min chp ≤Q i,t chp ≤Q i,max chp
Wherein: η (eta) chp Represents the thermoelectric ratio of the cogeneration unit, and eta is taken herein chp ＝0.75，Q i,min chp And Q i,max chp The lower limit and the upper limit of the heat power of the i-th cogeneration unit are respectively;
7) Heating pipe temperature constraint
T SH,g,t ≤T g max
T SH,h,t ≥T h min
Wherein: t (T) g max For the upper limit of the temperature of the water supply pipeline, T h min Is the lower limit of the temperature of the water return pipeline;
step 3.2, rolling the correction model within days: the intra-day correction is a plan 4 h-15 min before the predicted point, and is mainly based on the decision result before the day and the wind power prediction error e obtained by introducing S The heat storage and release characteristics of the heat supply network are utilized to adjust the output of the unit, the water supply temperature and the water return temperature of the heat source and the heat exchange station;
taking the total cost of the system as an objective function, the formula is as follows:
minF S ＝min(C NC S +C NG，s S +C NG，p S +C loss S )
wherein the method comprises the steps of
Wherein: the superscript S denotes the variables in the day-ahead scheduling model, ΔP i,t chp And DeltaP i,t Electric power adjustment amounts expressed as a cogeneration unit and a conventional unit;
the constraint conditions include:
1) Electric power balance constraint
0≤|e S1 |≤|e S |
Wherein: p (P) t S,L Expressed as total electric load power at time t in day, P t S,W Expressed as actual dispatch power of wind power in the day e S1 A prediction error compensated for the heat supply network by the stored heat energy;
2) Heat source, heat exchange station water supply and return temperature and heat exchange constraint
Wherein: delta T SH,g,t The temperature of the heating medium of the water supply pipeline at the heat source is regulated by delta T SH,h,t The temperature regulating quantity of the heat medium of the return water pipeline at the heat source is used; q (Q) HE,t ＝ε e ·W·((T HE,g,t +ΔT HE,g,t )-(T HE,h,t +ΔT HE,h,t ))
Wherein: delta T HE,g,t The temperature regulation quantity delta T of the heat medium of the primary side water supply pipeline of the heat exchange station HE,h,t The temperature adjustment quantity of the heat medium of the primary side return water pipeline of the heat exchange station is obtained;
3) Heating pipe temperature constraint
T SH,g,t +ΔT SH,g,t ≤T g max
T SH,h,t +ΔT SH,h,t ≥T h min
The unit output correction quantity constraint and the wind power output constraint are the same as those in step 3.1;
step 3.3, real-time correction of the model: the real-time correction plan is formulated 15min before the predicted point, and the wind power prediction error e after the heat supply network compensation in the intra-day scheduling is further compensated by utilizing the energy storage device F The objective is to take the minimum air discarding quantity and the minimum cut load quantity, namely the maximum charge and discharge quantity of the energy storage device as an objective function formula as follows:
minF F ＝min[e F (P i,t dc -P i,t ch )]
wherein: the superscript F denotes the variables in the day-ahead scheduling model, P ch And P dc Respectively representing the charging power and the discharging power of the electric storage device;
the constraint conditions include:
power storage device restraint
Wherein: u (U) i,t ch And U i,t dc Respectively representing a charging state and a discharging state of an ith power storage device at a moment t, wherein 1 is an operating state, and 0 represents an inactive state; p (P) i,t ch And P i,t dc Respectively representing the charging power and the discharging power of the ith power storage device at the moment t; p (P) i,min ch And P i,max ch Respectively represent a lower limit and an upper limit of charging power, P i,min dc And P i,max dc Respectively represent lower and upper discharge power limits, E i,t ESS Expressed as capacity of the ith power storage device at time t, E i,min ESS And E is i,max ESS Respectively representing the lower limit and the upper limit of the capacity of the ith power storage device, beta ch And beta dc Respectively representing a charging coefficient and a discharging coefficient;
the unit output correction quantity constraint and the wind power output constraint are the same as the step 3.1.
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