CN111832898A - Air-conditioning-based multifunctional demand response scheduling method for power system - Google Patents
Air-conditioning-based multifunctional demand response scheduling method for power system Download PDFInfo
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Abstract
The invention discloses a demand response scheduling method for an air-conditioning-based multifunctional power system, and belongs to the field of electrical engineering. The method comprises the following steps: respectively constructing scheduling models of an air conditioner load time shifting function and a standby function; the method comprises the steps that an air conditioner time shifting function and a standby function are used as demand responses of a power system, the running cost of the power system is minimum, and meanwhile, the air conditioner user experience degree is optimal, and a data-driven random unit combination model is constructed; and solving the optimal solution of the objective function to obtain a scheduling result of the demand response of the power system. According to the method, the scheduling models of the air conditioner load time shifting function and the standby function are respectively established, the power load in the power utilization peak period is reduced by using the time shifting function of the air conditioner, the climbing requirement of the system is reduced, the climbing standby of the unit is released by using the standby function of the air conditioner, and finally the flexible climbing capability of the power system is improved under the condition that the optimal experience of the air conditioner user and the minimum operation cost of the power system are ensured.
Description
Technical Field
The invention belongs to the field of electrical engineering, and particularly relates to a demand response scheduling method of an air-conditioning-based multifunctional power system.
Background
With the rapid increase in economy and the rise in global air temperature, the air conditioning load has increased dramatically and has become one of the major drivers of increasing peak demand for electricity. Although wind power is beneficial to relieving energy and environmental crisis, the uncertainty and the inverse peak regulation characteristic of wind power increase the demand of unit combination on the flexible climbing capacity. The increase in wind power and load requires the system to provide more spare capacity during operation. In addition, the ability to ramp up reflecting the flexibility of the system to cope with rapid changes in renewable energy or net load has also attracted widespread attention. When most air conditioning loads are started in the morning rush hour, the load will rise rapidly, resulting in a steep slope of the load curve and a greater need for climbing. Traditionally, the thermal power generating unit on the power generation side is the main source of the climbing capacity. However, the thermal power generating unit is not enough to deal with various changes of net load, and load shedding and wind abandoning phenomena occur. In recent years, demand response has been considered a promising solution due to its high availability and low price. Among all demand response resources, air conditioning load is of great concern due to its considerable number and thermal inertia of the building.
The air conditioning load can use the thermal inertia of the building to store energy, and the power consumption of the air conditioning load has strong correlation with indoor temperature setting and ambient temperature. At present, a thermal battery model of an air conditioner load is researched or established to realize compatibility with a scheduling model, or the thermal battery model is integrated into a robust unit combination problem after the air conditioner load is subjected to fine modeling, so that the operation flexibility is increased, or a load curve is reshaped by utilizing the thermal inertia of the air conditioner load, but the energy utilization difference of the air conditioner load between a starting process and a steady-state process is ignored. The problem of difficult climbing caused by wind power uncertainty and load peak can not be effectively solved.
Disclosure of Invention
In view of the above defects or improvement needs of the prior art, the present invention provides a demand response scheduling method for an electrical power system based on air conditioning multiple functions, which aims to establish a time-shift function and a standby function model of air conditioning loads to participate in demand response of the electrical power system, so as to improve the problem of difficulty in climbing the electrical power system.
In order to achieve the above object, the present invention provides a demand response scheduling method for an electric power system based on air conditioning multifunction, comprising:
s1, according to the energy utilization difference of the air conditioner load between the starting process and the steady state process, constructing a scheduling model of an air conditioner load time shifting function with the aim of reducing load requirements, and constructing a scheduling model of an air conditioner load standby function with the aim of improving the standby capacity of a power system;
s2, constructing a data-driven random unit combination model by taking an air conditioner time shifting function and a standby function as demand responses of a power system and taking the minimum running cost of the power system and the optimal experience degree of an air conditioner user as a target function;
and S3, solving the optimal solution of the objective function to obtain a scheduling result of the demand response of the power system.
Further, the scheduling model of the air conditioner load time shift function is as follows:
the constraint conditions include:
0≤△N′M,k≤△NM,k
wherein L isM,k,tIndicating the load of the kth air conditioner group administered by the Mth air conditioner aggregator in the t-th time period, LM,tRepresenting the total load of all air conditioners governed by the Mth air conditioner aggregator in the t-th time period, delta N'M,kIndicates the number of air conditioners, delta N, participating in time shifting in the k group of air conditioners administered by the Mth air conditioner aggregatorM,kRepresents the total number of the air conditioners of the kth group, s, administered by the Mth air conditioner aggregatorM,t,kRepresents the state variable of the air conditioner starting and stopping, 1 represents the starting of the air conditioner, 0 represents the stopping of the air conditioner,
represents the polymerization power of the stage in which the indoor temperature is decreased from the ambient temperature to the preset temperature when the air conditioner is just turned on,
the aggregation power of the air conditioner at a stage of keeping a steady state by starting and stopping the compressor is shown after the temperature reaches a preset temperature, K represents the number of air conditioner groups governed by an air conditioner aggregator, tKIndicating a first period of time, T, during which the air conditioner is switched onNRepresenting the total number of time segments participating in the scheduling.
Further, the scheduling model of the air conditioner load standby function is as follows:
DM,r,t＝AM,r,tNM,r,t
dM,r,t,s＝AM,r,tnM,r,t,s
the constraint conditions include:
0≤nM,r,t≤NM,r,t
wherein D isM,r,tSpare capacity, N, representing the Mth air conditioner aggregate to adjust the air conditioner temperature to the r-th level comfort level in the t-th time periodM,r,tIndicating the Mth air conditionerThe aggregator adjusts the temperature to the number of air conditioners of the r-th comfort level during the t-th period,
represents the number of all air conditioners managed by the Mth air conditioner aggregator, NRIndicating the comfort level of the air-conditioning user, dM,r,t,sRepresenting wind power uncertainty situation s, the Mth air conditioner aggregates the deployment reserve capacity for adjusting the air conditioner temperature to the r-th level comfort degree in the t-th time period, AM,r,tSpare capacity, n, representing the individual air conditioners within the Mth air conditioner aggregator to adjust air conditioner temperature to the r-th level of comfort during the t-th time periodM,r,t,sRepresenting the quantity of air conditioners d for regulating the temperature to the r-th level comfort level by the Mth air conditioner aggregation quotient in the t-th time period under the wind power uncertainty situation sM,r,t,sAnd (4) under the wind power uncertainty situation s, the Mth air conditioner aggregates the number of air conditioners which adjust the temperature to the r-th level comfort level in the t-th time period.
Further, the objective function of the data-driven random unit combination model is as follows:
wherein N isTIndicates the total amount of time scheduled, NGThe total number of the thermal power generating units is represented,
represents the start-stop cost u of the thermal power generating uniti,tIndicating whether the thermal power generating unit is in an on state, fi minRepresents the minimum cost of force output, Ii,tIndicating the start-stop state of the thermal power unit, NmNumber of segments, P, representing a linearized function of the thermal power costi,t,mIndicating the segmental output of the thermal power generating unit, Ki,mThe cost of the piecewise linearization is represented,
represents the upper spare capacity cost of the thermal power generating unit,
represents the upper spare capacity provided by the thermal power generating unit,
represents the cost of the lower spare capacity of the thermal power generating unit,
indicating upper reserve capacity, N, provided by the thermal power unitMThe number of the load aggregation quotients is shown,
price compensation, L, representing time-shift functionM,tWhich indicates the air conditioning load before the time shift,
indicating the air conditioning load after the time shift,
a compensation price for the standby function is indicated,
representing the amount of cut load under the scene s,
represents the air volume abandoned under the scene s, NSRepresenting the number of scenes, PsRepresenting the probability of each scene, d representing the load node, NDRepresenting the number of load nodes of the AC network, CcurA penalty coefficient of forced load shedding amount is represented, j represents a wind turbine generator node, NJNumber of node where wind turbine is located, CwindAnd representing a wind abandon penalty coefficient.
Further, the constraint condition of the objective function includes a power balance constraint, wherein the load takes into account the time shift function provided by the air conditioner, and the expression is as follows:
wherein, Pi,tIndicating the power output, W, of the thermal power unitj,tIndicating the predicted value of wind turbine output, Ld,tAnd representing the predicted node load value.
Further, the constraint condition of the objective function includes a power rebalance constraint of the wind power scene, the constraint considers a standby function provided by the air conditioner, and the expression is as follows:
wherein,
representing the amount of up-reserve calls in scene s,
represents the next call-to-use amount, W, under scene sj,t,sAnd representing the wind power output value under the scene s.
Further, before executing step S1, the method further includes collecting economic and technical parameters of each element of the power system under study; each element of the power system comprises an alternating current power grid, a tie line, a thermal power generating unit, a wind power generating unit and an air conditioner aggregator participating in demand response.
Further, before executing step S1, the method further includes collecting economic and technical parameters of each element of the power system under study; each element of the power system comprises an alternating current power grid, a tie line, a thermal power generating unit, a wind power generating unit and an air conditioner aggregator participating in demand response.
In general, the above technical solutions contemplated by the present invention can achieve the following advantageous effects compared to the prior art.
Aiming at the problem of insufficient flexible climbing capability of an electric power system caused by load peak and wind power uncertainty, the invention respectively constructs a scheduling model establishing an air conditioner load time shifting function and a standby function based on considering the energy utilization difference of the air conditioner load between the starting process and the steady state process, and is used for participating in the demand response of the electric power system, reducing the electric power load in the power utilization peak period by utilizing the time shifting function of the air conditioner, reducing the climbing requirement of the system, releasing the climbing standby of a unit by utilizing the standby function of the air conditioner, and finally improving the flexible climbing capability of the electric power system under the condition of ensuring the optimal experience of an air conditioner user and the minimum operation cost of the electric power system.
Drawings
FIG. 1 is a flow chart of a demand response scheduling method for an air-conditioning multifunctional-based power system according to the present invention;
FIG. 2 is a diagram illustrating the number of air conditioner aggregate calls for the time shift function provided by the present invention;
FIG. 3 is an air conditioner invocation capacity for the standby function provided by the present invention;
fig. 4 is a comparison diagram of flexible climbing demands before and after the multifunctional demand response of the air conditioner is considered.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention is described in further detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention. In addition, the technical features involved in the embodiments of the present invention described below may be combined with each other as long as they do not conflict with each other.
As shown in fig. 1, the present invention provides a demand response scheduling method for an air-conditioning multifunctional-based power system, including:
s1, respectively constructing scheduling models of an air conditioner load time shifting function and a standby function;
in particular, before the method of the present invention is executed, it is necessary to collect economic and technical parameters of the elements of the power system under study, including an ac power grid, a tie line, a thermal power generating unit, a wind power generating unit, and an air conditioning aggregator participating in demand response. The economic and technical parameters of each element comprise:
1) number N of load nodes of AC power gridDNode load prediction value LM,tAnd a scene value LM,t,sPenalty coefficient of forced load sheddingcur(ii) a 2) Number of lines N of AC power networkLUpper limit of allowable power flow f of long-term operation of linel max(ii) a 3) Number N of thermal power generating unit nodesGUpper and lower limits of output Pi max/Pi minMaximum climbing rate
Minimum start-stop time of thermal power generating unit
Thermal power generating unit start-stop cost
Minimum force-out cost fi minAnd a segmentation linearization cost Ki,m(ii) a 4) Node number N of wind turbine generatorJOutput predicted value W of wind turbine generatorj,tAnd a scene value Wj,t,sPenalty coefficient of air volume abandon Cwind(ii) a 5) Air conditioner aggregate quotient number NMTime-shiftable air-conditioning aggregate group number K, comfort level R of air-conditioning user, compensation price of time-shifting function and standby function
And
after relevant parameters are collected, a scheduling model of an air conditioner load time shifting function and a standby function is constructed according to the following steps;
s1.1, analyzing the operating characteristics of the air conditioner load;
the embodiment of the invention takes a fixed-frequency air conditioner as an example, the fixed-frequency air conditioner adopts temperature control, and the thermodynamic property of the fixed-frequency air conditioner can be represented by a first-order equivalent parameter model as shown in a formula (1) and a formula (2).
Where eta represents the efficiency of the air conditioner, PACThe rated power of the air conditioner is represented, R represents the equivalent thermal resistance of the air conditioner, and C represents the equivalent thermal capacity of the air conditioner;
equations (1) and (2) describe the model of the indoor temperature as a function of the air conditioning power. The binary variable x represents the air conditioner on (x ═ 1) or off (x ═ 0), and Δ t is the time interval. The operating characteristics of the air conditioner during start-up and steady state are different. When the air conditioner is just started, the indoor temperature is reduced to T from the ambient temperaturemin. Aggregate power of air conditioning units at this stage
And duration τstartCan be expressed as:
wherein,
indicating the rated power, T, of an air conditioner controlled by an aggregator M0Indicating the outdoor temperature.
When the temperature reaches TminThen, the air conditioner keeps the indoor temperature at T by starting and stopping the compressorsetPolymerization power in this stage
And duration τ in on-off stateon，τoffCan be respectively expressed as:
s1.2, constructing a scheduling model of an air conditioner load time shifting function;
in order to reduce the climbing requirement, part of the air conditioners can be started in advance. If Δ N'M,3The air conditioner unit is originally started in the xi +2 time period and is started in the xi +1 time period now, so that the load in the xi +1 time period is increased
And the load in the xi +2 period is reduced
This can be interpreted as the aggregate power during the start-up of the air conditioner being higher than during the steady operation. Therefore, the time shifting of the air conditioner can reduce the climbing demand of the system
The scheduling formula of the air conditioner time shift in the unit combination is as follows:
wherein L isM,k,tIndicating the load of the kth air conditioner group administered by the Mth air conditioner aggregator in the t-th time period, LM,tRepresenting the total load of all air conditioners governed by the Mth air conditioner aggregator in the t-th time period, delta N'M,kIndicates the number of air conditioners, delta N, participating in time shifting in the k group of air conditioners administered by the Mth air conditioner aggregatorM,kRepresents the total number of the air conditioners of the kth group, s, administered by the Mth air conditioner aggregatorM,t,kRepresenting the state variable of the start and stop of the air conditioner, 1 representing the start of the air conditioner, 0 representing the stop of the air conditioner, K representing the number of the air conditioner aggregation quotient groups which can be time-shifted,tKindicating a first period of time, T, during which the air conditioner is switched onNRepresenting the total number of time segments participating in the scheduling.
The expressions (8) to (10) are combined with the starting and stable operation process of the air conditioner to establish the load curve of the kth air conditioner group governed by the Mth air conditioner aggregator in the tth time period. The on/off state of the air conditioning unit can be determined according to the t time and the t-1 time period to determine the operation stage of the air conditioner. If s isM,t-1,k0 and sM,t,kIf the number of the air conditioning units is 1, the air conditioning unit of the kth group is in a starting stage; on the contrary, if sM,t-1,k1 and sM,t,kAnd (5) the air conditioning unit is in a steady state stage when the air conditioning unit is equal to 1. When the starting time is different, LM,k,tIn contrast, equation (8) indicates that it is possible to calculate the time interval [0, t ]K-1]Air conditioning unit with internal early opening, formula (9) describes the time period [ tK,tK+K-1]The newly-added starting air conditioning unit, the formula (10) refers to the time interval [ t ]K+K,TN]The air conditioning unit is arranged in the stable operation process. Equation (11) represents that the newly turned on air conditioning unit is the sum of the transferable air conditioning units for K periods.
The constraints of the time-shift function scheduling model include:
0≤△N′M,k≤△NM,k(12)
the schedulable air conditioning unit can be divided into K groups according to the starting time. Equations (12) - (14) limit the number of air conditioning units diverted per group. Equation (12) limits the number of air conditioning units transferred per group to not exceed the total number of air conditioning units. Equations (13) and (14) ensure that the number of air conditioning units transferred is not more than the total number of air conditioning units in the previous time period.
S1.3, constructing a scheduling model of an air conditioner load standby function;
the air conditioner user can provide the spare capacity within the comfortable range of the user by adjusting the temperature set value, and in order to stimulate the air conditioner user to participate in demand response, the comfort of the user needs to be ensured when the response capacity is determined:
wherein,
indicating the rated power of the air conditioner aggregator M at time t,
indicating the air conditioner on time at the comfort level r,
indicating an air conditioner on time with a comfort level r.
(15) The first term on the right of the medium equation represents the average aggregate power at a set temperature, and the second term relates to the adjusted temperature for the r-th user comfort level. The more temperature adjustments, the higher the compensation price. The calling model of the air conditioner standby function considering the user temperature adjustment is specifically expressed as follows:
DM,r,t＝AM,r,tNM,r,t(16)
dM,r,t,s＝AM,r,tnM,r,t,s(17)
wherein D isM,r,tSpare capacity representing that the Mth air conditioner aggregates to adjust the air conditioner temperature to the r-th level comfort degree in the t-th time period, AM,r,tSpare capacity, N, representing the individual air conditioners within the Mth air conditioner aggregator to adjust air conditioner temperature to the r-th level of comfort during the t-th time periodM,r,tRepresenting the number of air conditioners that the Mth air conditioner aggregates to adjust the temperature to the r-th level comfort level in the t-th period, dM,r,t,sRepresenting wind power uncertainty situation s, the Mth air conditioner aggregates the deployment reserve capacity for adjusting the air conditioner temperature to the r-th level comfort degree in the t-th time period, and nM,r,t,sDenotes dM,r,t,sRepresenting the quantity of air conditioners for adjusting the temperature to the r-th level comfort degree in the t-th time period by the aggregation of the Mth air conditioners under the wind power uncertainty situation s;
the constraint conditions include:
0≤nM,r,t≤NM,r,t(20)
wherein,
representing the number of all air conditioners governed by the Mth air conditioner aggregator, and R representing the comfort level of an air conditioner user;
(18) limit NM,r,tRange of (1), NM,r,t0 means that no user is scheduled to the r-th level of temperature adjustment,
indicating that all users of the aggregate M are scheduled to the r-th level; (19) the sum of the number of users at each level does not exceed the total number of users
(20) The aggregate M is limited to a spare capacity no less than its deployed spare capacity at each comfort level.
S2, constructing a data-driven random unit combination model by taking an air conditioner time shifting function and a standby function as demand responses of a power system and taking the minimum running cost of the power system and the optimal experience degree of an air conditioner user as a target function;
the objective function of the data-driven stochastic unit combination model is shown as (21). The random optimization comprises two stages, wherein the first stage represents day-ahead scheduling, and the second stage represents day-in scheduling in a corresponding wind power scene. The operation cost of the first stage comprises the starting cost, the fuel cost and the spare capacity cost of the unit, and the compensation cost of the air conditioner time shifting function and the spare function under the basic value scene. The cost of the second stage includes penalty cost for forced load shedding and wind curtailment at the worst wind farm grain distribution probability.
In the formula,
is the decision variable of the first stage,
is the decision variable for the second stage. The decision of the first stage should be robust to wind power scenarios of any probability distribution of the second stage. In the second stage, psAnd the worst distribution probability of the wind power scene in the distribution uncertainty set is represented, and other variables represent the re-scheduling behavior under the worst distribution probability.
Wherein N isTIndicates the total amount of time scheduled, NGThe total number of the thermal power generating units is represented,
represents the start-stop cost u of the thermal power generating uniti,tIndicating whether the thermal power generating unit is in an on state, fi minRepresents the minimum cost of force output, Ii,tIndicating the start-stop state of the thermal power unit, NmNumber of segments, P, representing a linearized function of the thermal power costi,t,mIndicating the segmental output of the thermal power generating unit, Ki,mThe cost of the piecewise linearization is represented,
represents the upper spare capacity cost of the thermal power generating unit,
represents the upper spare capacity provided by the thermal power generating unit,
represents the cost of the lower spare capacity of the thermal power generating unit,
indicating upper reserve capacity, N, provided by the thermal power unitMThe number of the load aggregation quotients is shown,
price compensation, L, representing time-shift functionM,tWhich indicates the air conditioning load before the time shift,
indicating the air conditioning load after the time shift,
a compensation price for the standby function is indicated,
representing the amount of cut load under the scene s,
represents the air volume abandoned under the scene s, NSRepresenting the number of scenes, PsRepresenting the probability of each scene, d representing the load node, NDRepresenting the number of load nodes of the AC network, CcurA penalty coefficient of forced load shedding amount is represented, j represents a wind turbine generator node, NJNumber of node where wind turbine is located, CwindAnd representing a wind abandon penalty coefficient.
The constraint conditions include:
01. an uncertain set of probability distribution of a wind power scene;
the probability distribution of wind power is difficult to obtain, so a confidence estimation interval D can be established based on historical data, and the possible probability distribution of a wind power scene is covered:
in the formula, NsThe number of scenes extracted from the historical data;
the deduced reference probability of the wind power scene can be obtained by a scene generation method or a kernel function and other methods; θ represents the corresponding tolerance value:
in addition, the probability of the wind power scene should also satisfy the following formula,
02. traditional safety restraints
The first stage represents day-ahead scheduling, with the constraints as follows:
ui,t-vi,t＝Ii,t-Ii,t-1(26)
ui,t+vi,t≤1 (27)
Pi minIi,t≤Pi,t≤Pi maxIi,t(30)
wherein u isi,tIndicating whether the thermal power unit is in an on state, vi,tIndicating whether the thermal power generating unit is in a shutdown state,
indicating the minimum time allowed for the thermal power unit to turn on,
indicating the minimum allowable time for thermal power unit shutdown, Pi minIndicating the minimum output, P, of the thermal power uniti minThe maximum output of the thermal power generating unit is shown,
representing the maximum output of the segmented linearization of the thermal power generating unit;
(25) and a power balance constraint, wherein the load is the load after considering the time shifting function of the air conditioner. (26) - (29) describe the start-stop status and minimum on/off time of the unit. (30) And the unit output constraint. (31) - (32) is a piecewise linearization constraint of the unit output. (33) Is a transmission power constraint for the line.
The second stage corresponds to rescheduling in different wind power scenes, and the constraint conditions are as follows:
wherein,
a sensitivity matrix representing the thermal power unit nodes and the transmission line,
a sensitivity matrix representing the wind turbine nodes and the transmission lines,
a sensitivity matrix representing the load nodes and the transmission lines,
a sensitivity matrix representing the load aggregation quotient node and the transmission line;
(34) for the power rebalance constraint of a wind power scenario, the deployment reserve provided by the air conditioner is considered. (35) - (36) represent curtailment and tangential load constraints, respectively. (37) Representing the transmission power constraints of the line.
03. Coupling constraints for climbing capacity and reserve capacity
The coupling formula for the hill climbing capacity and the backup in the data-driven model is as follows:
Pi minIi,t≤pi,t,s≤Pi maxIi,t(43)
wherein,
represents the climbing amount provided by the thermal power generating unit,
indicating whether the thermal power generating unit provides an upward climb,
represents the downward climbing amount provided by the thermal power generating unit,
indicating whether the thermal power generating unit provides for downhill climbing,
represents the maximum value of the uphill slope which can be provided by the thermal power generating unit,
representing the maximum value of the downward slope which can be provided by the thermal power generating unit;
(38) - (41) belonging to the first stage constraint and (42) - (47) belonging to the second stage constraint. (38) The up-and-down climbing of the unit between two adjacent periods considering the start and stop of the unit is described. (39) And the climbing and the descending can not occur simultaneously. (40) - (41) limits the sum of the reserve capacity and the climbing demand of the first stage to not exceed the flexibility capability of the unit, thereby ensuring that the second stage rebalancing has sufficient climbing reserve. (42) - (43) limit the generator output. (44) And the step (45) is the slope climbing constraint of the unit after the output of the unit is changed in the wind power scene. (46) - (47) limiting the deployment reserve of the crew to not exceed its reserve capacity under each scenario.
And S3, solving the optimal solution of the objective function by a quick solution algorithm based on C & CG (column-and-constraints generation).
01. Problem reconstruction; it should be noted that the above model contains non-linear terms, such as Δ N 'in (13) and (14)'M, ksM,t,k. To make the optimization easy to handle, the non-linear term is replaced with an auxiliary variable, i.e. θM,t,k＝△N′M,ksM,t,k. In addition, an equivalent linear constraint is introduced to limit θM,t,kSpecifically, as follows,
0≤θM,t,k≤Q·sM,t,k(48)
△N′M,k-Q·(1-sM,t,k)≤θM,t,k≤△N′M,k+Q·(1-sM,t,k) (49)
(48) ensure when sM,t,kWhen equal to 0, thetaM,t,k0; (49) ensure when sM,t,kWhen 1, θM,t,k＝△N′M,k。
Non-linear term
Similar equivalent transformations are also made.
02. The model proposed in step 2 can be summarized as follows:
wherein X is the decision vector of the first stage; y is the deterministic decision vector for the second stage;
representing a wind power scene; p is the uncertain decision variable of the second stage. A and B are constant matrices; e. b and h are constant vectors. F (X) represents an objective function of the first stage;
representing the objective function of the second stage. (50.a) is a constraint set of the first stage, including constraints (8) - (14), (25) - (33) and (38) - (41). (50.b) is the set of constraints for the second stage, including constraints (15) - (20), (34) - (37) and (42) - (47). (50.c) is the probability distribution for (22) - (24). Since the optimization problem of (50) cannot be solved directly, it can be solved by C&The CG method decomposes the original problem into a main problem and a sub problem, as follows.
03. Major problems
The main problem usually replaces the objective function of the second stage with an auxiliary variable η:
in addition to the first stage constraint of (51.a), MP iteration adds new constraints under the worst probability distributions of (51.b) and (51.c), representing a feasible cut face and an optimal cut face, respectively. However, due to the separability of the distribution probability P and the rescheduling decision Y, the constraint added in (51.b) is in a fixed scenario
Each iteration is the same next. Thus, the scenario constraints can be co-optimized with the day-ahead scheduling of the first stage, with each iteration only requiring the addition of the optimal cut plane, as shown at (52).
Selected probabilities as in constraints (52.c)
The initial value of (c). (52) The MP of (1) is the relaxation of the original problem, and can provide a lower bound for the original problem. Mathematically, it is a standard linear programming problem that can be solved directly.
04. Sub-problems
Notably, Y and P are separable for both the constraint and the objective function. Therefore, the second stage max-min problem can be decomposed without using dual transformation. The problem of reconstruction is shown in (53).
(53) The optimization of (a) can be achieved in two steps: first, the constraint condition (53.a) is considered to solve the inner-layer minimum problem under each situation, and the optimal solution of the internal optimization is expressed as
Then, will
Solving the outer-layer maximum problem as a known parameter yields the worst distribution probability. Since scene variables and scene constraints can be integrated into the MP, the following SPs can be established
(54) The SP of (a) aims to find the worst probability distribution and provide an upper bound for the original problem. Mathematically, the proposed algorithm avoids the dual transform of internal optimization by exploiting the separable nature of the inner and outer variables, making the algorithm easier to process.
05. Solving process
Step 1: the lower bound LB ═ infinity and the upper bound UB ═ infinity were set. The iteration number k is 0 and the threshold value e is-4。
Step 2: solving (52) the MP to obtain an optimal solution
And
i.e. the optimum target
Then, the lower bound of the original problem is updated to LB ═ Lk。
Step 3: fixing
Solving (54) the SP to obtain an optimal solution
I.e. the optimum target
Thus, the upper bound of the original problem is updated to
Step 4: if UB-LB is less than or equal to e, return
As the final solution and terminates the calculation. Otherwise, a constraint is added in the MP (52. d).
Step 5: update k to k +1 and return to Step 2.
Embodiments of the present invention employ an improved IEEE-118 node system diagram for simulation. The system comprises 54 thermal power generating units, and the total capacity is 7220 MW. The wind power plant is located at node 59 and has a capacity of 3000 MW. The wind curtailment and the load shedding penalty are respectively set to 300$/MW · h and 3500$/MW · h. Assume that the participating AC capacity is 30% of the total load. Since the rapid ramp-up of the load occurs mainly during the period 07:30-09:30, it is assumed that the air conditioning load during this period can be started in advance, thereby changing the load profile. The compensation price of the air conditioner time shift function is set to 115$/MW h. The spare function quotation requirement of each user comfort level is higher than the spare price of the thermal power generating unit.
The scheduling scheme of the multifunctional air conditioner demand response for improving the flexible climbing capability of the electric power system is obtained by solving through commercial software GUROBI based on an MATLAB platform, the calling results of the time shifting function and the standby function are respectively shown in FIG. 2 and FIG. 3, fig. 4 compares the climbing demand of the system if the demand response provided by the air conditioning load aggregator considering the two functions, table 1 compares the calculation results and time of the proposed fast solving algorithm and the conventional solving algorithm for the data driven unit combination model considering and not considering the multifunctional demand response of the air conditioner, and it can be seen from fig. 4 and table 1 that the multifunctional demand response model of the air conditioning load can effectively reduce the climbing demand of the power system and improve the flexibility of the system, at the same time, by comparing the traditional algorithm with the solving result of the rapid solving algorithm provided by the invention, the rapid solving algorithm can ensure the global optimal solution and simultaneously improve the calculation efficiency.
Table 1 comparison of results of solving algorithm
It will be understood by those skilled in the art that the foregoing is only a preferred embodiment of the present invention, and is not intended to limit the invention, and that any modification, equivalent replacement, or improvement made within the spirit and principle of the present invention should be included in the scope of the present invention.
Claims (7)
1.A demand response scheduling method for an electric power system based on air conditioning multifunction is characterized by comprising the following steps:
s1, according to the energy utilization difference of the air conditioner load between the starting process and the steady state process, constructing a scheduling model of an air conditioner load time shifting function with the aim of reducing load requirements, and constructing a scheduling model of an air conditioner load standby function with the aim of improving the standby capacity of a power system;
s2, constructing a data-driven random unit combination model by taking an air conditioner time shifting function and a standby function as demand responses of a power system and taking the minimum running cost of the power system and the optimal experience degree of an air conditioner user as a target function;
and S3, solving the optimal solution of the objective function to obtain a scheduling result of the demand response of the power system.
2. The method as claimed in claim 1, wherein the scheduling model of the air-conditioning load time-shifting function is as follows:
the constraint conditions include:
0≤△N′M,k≤△NM,k
wherein L isM,k,tIndicating the load of the kth air conditioner group administered by the Mth air conditioner aggregator in the t-th time period, LM,tRepresenting the total load of all air conditioners governed by the Mth air conditioner aggregator in the t-th time period, delta N'M,kIndicates the number of air conditioners, delta N, participating in time shifting in the k group of air conditioners administered by the Mth air conditioner aggregatorM,kRepresents the total number of the air conditioners of the kth group, s, administered by the Mth air conditioner aggregatorM,t,kRepresents the state variable of the air conditioner starting and stopping, 1 represents the starting of the air conditioner, 0 represents the stopping of the air conditioner,
express as air conditionerPolymerization power in the stage that the indoor temperature is reduced from the ambient temperature to the preset temperature just after starting,
the aggregation power of the air conditioner at a stage of keeping a steady state by starting and stopping the compressor is shown after the temperature reaches a preset temperature, K represents the number of air conditioner groups governed by an air conditioner aggregator, tKIndicating a first period of time, T, during which the air conditioner is switched onNRepresenting the total number of time segments participating in the scheduling.
3. The multifunctional air-conditioning-based power system demand response scheduling method of claim 1, wherein the scheduling model of the air-conditioning load standby function is as follows:
DM,r,t＝AM,r,tNM,r,t
dM,r,t,s＝AM,r,tnM,r,t,s
the constraint conditions include:
0≤nM,r,t≤NM,r,t
wherein D isM,r,tSpare capacity, N, representing the Mth air conditioner aggregate to adjust the air conditioner temperature to the r-th level comfort level in the t-th time periodM,r,tRepresents the number of air conditioners that the mth air conditioner aggregator adjusts the temperature to the nth level comfort during the t-th period,
represents the number of all air conditioners managed by the Mth air conditioner aggregator, NRIndicating the comfort level of the air-conditioning user, dM,r,t,sRepresenting wind power uncertainty situation s, adjusting air conditioner temperature to r-level comfort level by Mth air conditioner aggregation in t-th time periodDeployment of reserve capacity, AM,r,tSpare capacity, n, representing the individual air conditioners within the Mth air conditioner aggregator to adjust air conditioner temperature to the r-th level of comfort during the t-th time periodM,r,t,sRepresenting the quantity of air conditioners d for regulating the temperature to the r-th level comfort level by the Mth air conditioner aggregation quotient in the t-th time period under the wind power uncertainty situation sM,r,t,sAnd (4) under the wind power uncertainty situation s, the Mth air conditioner aggregates the number of air conditioners which adjust the temperature to the r-th level comfort level in the t-th time period.
4. The multifunctional air-conditioning-based power system demand response scheduling method of claim 1, wherein an objective function of the data-driven stochastic unit combination model is as follows:
wherein N isTIndicates the total amount of time scheduled, NGThe total number of the thermal power generating units is represented,
represents the start-stop cost u of the thermal power generating uniti,tIndicating whether the thermal power generating unit is in an on state, fi minRepresents the minimum cost of force output, Ii,tIndicating the start-stop state of the thermal power unit, NmNumber of segments, P, representing a linearized function of the thermal power costi,t,mIndicating the segmental output of the thermal power generating unit, Ki,mThe cost of the piecewise linearization is represented,
represents the upper spare capacity cost of the thermal power generating unit,
represents the upper spare capacity provided by the thermal power generating unit,
indicating lower reserve of thermal power generating unitThe cost of the capacity is that of the capacity,
indicating upper reserve capacity, N, provided by the thermal power unitMThe number of the load aggregation quotients is shown,
price compensation, L, representing time-shift functionM,tWhich indicates the air conditioning load before the time shift,
indicating the air conditioning load after the time shift,
a compensation price for the standby function is indicated,
representing the amount of cut load under the scene s,
represents the air volume abandoned under the scene s, NSRepresenting the number of scenes, PsRepresenting the probability of each scene, d representing the load node, NDRepresenting the number of load nodes of the AC network, CcurA penalty coefficient of forced load shedding amount is represented, j represents a wind turbine generator node, NJNumber of node where wind turbine is located, CwindAnd representing a wind abandon penalty coefficient.
5. The multifunctional air-conditioning-based power system demand response scheduling method of claim 4, wherein the constraint condition of the objective function comprises a power balance constraint, wherein the load takes into account the time-shifting function provided by the air conditioner, and the expression is as follows:
wherein, Pi,tIndicating the power output, W, of the thermal power unitj,tIndicating the predicted value of wind turbine output, Ld,tAnd representing the predicted node load value.
6. The multifunctional power system demand response scheduling method based on air conditioners as claimed in claim 4, wherein the constraint condition of the objective function comprises a power rebalancing constraint of a wind power scene, the constraint takes into account a standby function provided by the air conditioners, and the expression is as follows:
wherein,
representing the amount of up-reserve calls in scene s,
represents the next call-to-use amount, W, under scene sj,t,sAnd representing the wind power output value under the scene s.
7. The air-conditioning multifunctional based power system demand response scheduling method as claimed in any one of claims 1 to 6, wherein the method further comprises, before executing step S1, collecting economic and technical parameters of each element of the power system under study; each element of the power system comprises an alternating current power grid, a tie line, a thermal power generating unit, a wind power generating unit and an air conditioner aggregator participating in demand response.
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113011655A (en) * | 2021-03-22 | 2021-06-22 | 昆明理工大学 | Two-stage random planning-based hybrid energy storage system planning method for community multi-energy system |
| CN115187091A (en) * | 2022-07-18 | 2022-10-14 | 东南大学溧阳研究院 | Method for evaluating and controlling online response capability of air conditioner load |
Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106655200A (en) * | 2016-09-13 | 2017-05-10 | 浙江大学 | Method for calculating electric power system operation reserve response quantity provided by air-conditioner aggregation |
| CN109523096A (en) * | 2018-12-28 | 2019-03-26 | 广东电网有限责任公司 | Interruptible load makees the gas electric system optimization dispatching method of Reserve Ancillary Service |
| CN109752953A (en) * | 2018-10-08 | 2019-05-14 | 国网天津市电力公司电力科学研究院 | A kind of building energy supplying system model prediction regulation method of integrated electric refrigerating machine |
| CN110189056A (en) * | 2019-06-05 | 2019-08-30 | 广东电网有限责任公司 | A kind of method of electric power system dispatching, system and equipment |
| CN110197312A (en) * | 2019-06-28 | 2019-09-03 | 东南大学 | A kind of user class integrated energy system Optimization Scheduling based on Multiple Time Scales |
| CN110535185A (en) * | 2019-08-09 | 2019-12-03 | 东北电力大学 | A kind of optimizing operation method of multi-source containing photo-thermal power station considering demand response |
-
2020
- 2020-06-11 CN CN202010526990.8A patent/CN111832898A/en active Pending
Patent Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106655200A (en) * | 2016-09-13 | 2017-05-10 | 浙江大学 | Method for calculating electric power system operation reserve response quantity provided by air-conditioner aggregation |
| CN109752953A (en) * | 2018-10-08 | 2019-05-14 | 国网天津市电力公司电力科学研究院 | A kind of building energy supplying system model prediction regulation method of integrated electric refrigerating machine |
| CN109523096A (en) * | 2018-12-28 | 2019-03-26 | 广东电网有限责任公司 | Interruptible load makees the gas electric system optimization dispatching method of Reserve Ancillary Service |
| CN110189056A (en) * | 2019-06-05 | 2019-08-30 | 广东电网有限责任公司 | A kind of method of electric power system dispatching, system and equipment |
| CN110197312A (en) * | 2019-06-28 | 2019-09-03 | 东南大学 | A kind of user class integrated energy system Optimization Scheduling based on Multiple Time Scales |
| CN110535185A (en) * | 2019-08-09 | 2019-12-03 | 东北电力大学 | A kind of optimizing operation method of multi-source containing photo-thermal power station considering demand response |
Non-Patent Citations (1)
| Title |
|---|
| 沈琛云;王明俭;李晓明;: "基于风-光-蓄-火联合发电***的多目标优化调度", 电网与清洁能源, no. 11, 25 November 2019 (2019-11-25), pages 78 - 86 * |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113011655A (en) * | 2021-03-22 | 2021-06-22 | 昆明理工大学 | Two-stage random planning-based hybrid energy storage system planning method for community multi-energy system |
| CN115187091A (en) * | 2022-07-18 | 2022-10-14 | 东南大学溧阳研究院 | Method for evaluating and controlling online response capability of air conditioner load |
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