CN112132379A - Economic-considered new energy cross-region consumption evaluation method and storage medium - Google Patents

Economic-considered new energy cross-region consumption evaluation method and storage medium Download PDF

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CN112132379A
CN112132379A CN202010766856.5A CN202010766856A CN112132379A CN 112132379 A CN112132379 A CN 112132379A CN 202010766856 A CN202010766856 A CN 202010766856A CN 112132379 A CN112132379 A CN 112132379A
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彭旭
高曼飞
周兆南
高翔
焦永辉
焦朝勇
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Nari Technology Co Ltd
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Abstract

The invention discloses a new energy cross-regional consumption evaluation method considering economy, which is characterized in that a renewable energy consumption model is established by taking the minimum total power generation cost as an objective function, wherein the total power generation cost is calculated by fuel consumption cost, environmental pollution cost, fuel price and carbon dioxide and sulfur dioxide emission allowable price; establishing constraint conditions of a renewable energy consumption model, wherein the constraint conditions comprise system operation constraint and conventional generator set constraint; and solving the renewable energy consumption model to obtain the transmission power of the tie line between the regions and the new energy consumption value of each region. According to the new energy trans-regional consumption model considering the economic cost, under the condition that the constraints of section constraint, inter-provincial links, adjacent provincial consumption margin and the like are met, the trans-regional transmission channel transmission value and the optimal unit output are accurately calculated by taking optimal profit as a target, the economy of the cost of a power generation side is guaranteed while the new energy is consumed in a large range, the utilization rate of the new energy in China is improved, and the new energy is reduced.

Description

Economic-considered new energy cross-region consumption evaluation method and storage medium
Technical Field
The invention belongs to the technical field of new energy consumption, and particularly relates to a new energy trans-regional consumption evaluation method considering economy.
Background
With the rapid development of the current social science, technology and economy, the installation of a renewable energy unit and the consumption capability of renewable energy are widely concerned. However, the output of renewable energy has great randomness and fluctuation, which brings great challenges to system backup and power balance, and causes a great deal of wind and light abandoning phenomena. And the power supply has single structure and insufficient flexibility, and a part of areas have the situation of less load and more energy, so that the trans-regional power exchange is insufficient, new energy cannot be absorbed in a large range, and the problem of new energy absorption is more severe.
Aiming at the problem of difficult cross-regional consumption of new energy, the method for reasonably consuming the new energy by the power grid in a cross-region mode is determined, wind and light abandonment of the power grid can be reduced, the generating efficiency of a unit is improved, the influence of new energy grid connection on the power grid is reduced, and the economic benefit of the power grid is improved. At present, in the aspect of renewable energy consumption, research methods mainly include a typical daily analysis method, a random production simulation method and a time-series production simulation method. The method is characterized in that a time sequence production simulation method is adopted, system load and renewable energy power generation amount are used as time-varying sequences, and the operation state of each generator set is simulated to be closer to the actual situation.
At present, based on a time sequence production simulation method, two new energy consumption models are mainly established. 1) And considering a power grid local consumption model under the constraints of unit operation requirements, unit start and stop, minimum operation mode, heat supply amount and the like. 2) In the operation simulation of the power grid, according to the principle that renewable energy sources are preferentially adjusted when a transmission line is blocked or a peak shaving is blocked, cross-region connecting lines are considered, and larger renewable energy source consumption is obtained. The annual production time series simulation model of large-scale wind power and solar power generation is established by taking factors such as output characteristics, load characteristics, peak load regulation characteristics of a unit, thermoelectric coupling characteristics of a heat supply unit, starting modes and the like of wind power and photovoltaic power generation into comprehensive consideration aiming at the maximum consumption of renewable energy of a power grid as a target.
From the above studies, it can be seen that whether a new energy consumption model of a local power grid or a consumption capability model of a cross-regional renewable energy source is considered, it is desirable to absorb renewable energy sources as much as possible in the existing power grid. However, in some cases, increasing the access capacity of renewable energy sources increases the energy consumption and cost of the overall system. Conversely, the corresponding greenhouse gas and pollutant emissions are not further reduced. Therefore, from the grid operation level, the model targeting economic benefits is closer to the actual production and the actual situation.
Disclosure of Invention
The invention aims to provide a new energy trans-regional consumption model aiming at economic benefits, so that the model is closer to actual production and actual conditions.
In order to achieve the technical purpose, the invention adopts the following technical scheme.
The invention provides a new energy trans-regional consumption evaluation method considering economy, which comprises the following steps:
establishing a renewable energy consumption model by taking the minimum total power generation cost as an objective function, wherein the total power generation cost takes fuel consumption cost, unit start-stop cost, environmental pollution cost, fuel price and carbon dioxide and sulfur dioxide emission permission price into account;
establishing constraint conditions of a renewable energy consumption model, wherein the constraint conditions comprise system operation constraint and conventional generator set constraint;
and solving the renewable energy consumption model to obtain the transmission power of the tie line between the regions and the new energy consumption value of each region.
Further, the renewable energy consumption model is represented as follows:
Figure BDA0002615010030000031
wherein C iscoal(PGiT) cost of fuel consumption, Cenv(PGiT) cost of environmental pollution, Q (P)GiT) the start-stop cost of the unit, G the number of the units, T the time, i the ith unit, K the fixed coefficient, D1As a price of fuel, D2The allowable price for discharging carbon dioxide and sulfur dioxide, b (t) the power generation coal consumption rate of the thermal power generating unit, PGi(t) is the output power of the thermal power generating unit, C1Expressed as equivalent CO per coal combustion2Coefficient of emission, Pl tIn order to be the load value,
Figure BDA0002615010030000032
the output value of the jth hydroelectric generating set is obtained,
Figure BDA0002615010030000033
and outputting a force value for the ith thermal power generating unit.
Still further, the constraints of the renewable energy consumption model include the following:
(1) system operation constraints including power balance constraints, system reserve capacity constraints, generator set downward regulation capacity constraints and interval outgoing capacity constraint formulas;
the power balance constraint is
PGi+Pwater+Pp+Pwind-Ptran＝P1 (2)
The system spare capacity is constrained to
S≥Pmaxl×(l％+s％)+Ppre×w％+Pr (3)
The downward regulating capacity of the generator set is restricted as
Gi＝Pi-Pi,min (4)
The interval delivery capacity constraint formula is
D(X,Y)＝min(S(Y-X),R(Y-X))
In the formula, PGiFor thermal power output, PwaterFor the water discharge value, PpFor photovoltaic output value, PwindFor wind power output value, PtranFor delivery of power, P1Is a load value; s is the spare capacity, P, required by the systemmaxlTo predict maximum load,% is percent load reserve, s% is percent accident reserve, PprePredicting power for the new energy, w% demand for reserve capacity for predicted new energy contribution error, PrFor maintenance of reserve capacity, GiFor generator set i turndown capability, PiFor the ith conventional unit output, Pi,minD (X, Y) is X, Y and saving margin for saving X and Y; s (Y-X) is a power exchange margin of a Y power-saving network based on the sensitivity of the X-saving and Y-saving section; r (Y-X) is the clean energy consumption capability of the Y power-saving network, and X is in the direction of input.
(2) Conventional generator set constraints comprise a generator set power constraint thermal power unit climbing constraint and a section constraint;
the unit power constraint is
Pdown≤P≤Pup (5)
The climbing of the thermal power generating unit is restricted as
Pg(t)-Pg(t-1)≤Pg-up (6)
Pg(t-1)-Pg(t)≤Pg-down (7)
The section constraint
S＝min[(L1-P1)/α1，…，(Lj-Pj)/αj，…，(Ln-Pn)/αn] (8)
In the formula, PdownFor minimum technical output of the unit, PupRated output is used; pg-upUpward ramp rate of the unit, Pg-downFor the downward climbing rate, S is the power exchange margin of the power grid based on the section sensitivity; l isjIs the j limit value, P, of the cross sectionjThe power of the section j is used; alpha is alphajThe sensitivity coefficient of the output of the unit in the power grid E to the power of the section j can be determined according to actual operation experience or obtained by calculation, Pg(t)The output of the thermal power generating unit at the t momentg(t-1)The output of the thermal power generating unit at the t-1 moment.
And then further adopting a particle swarm optimization algorithm, simultaneously adding an improved penalty function method to process the power balance constraint and the conventional unit constraint in the model, and solving the renewable energy consumption model, wherein the method specifically comprises the following steps:
the constraint condition is incorporated into the objective function by introducing a penalty factor to form an unconstrained objective function, which is expressed as follows:
Figure BDA0002615010030000051
wherein f (x) is an objective function, Mi(x) For equality constraints, Mj(x) For inequality constraint, F (x) is a new objective function, and M is a penalty coefficient when the constraint condition is satisfied.
The constraint condition of the renewable energy consumption model comprises an equality constraint which is PGi+Pwater+Pp+Pwind-Ptran＝P1When P isGi+Pwater+Pp+Pwind-Ptran≥P1In time, the new energy can not be completely consumed by the conventional unit and the outward delivery, and the value of the new energy is Ppv+Pwind＝P1+Ptran-PGi-Pwater。
PpvThe photovoltaic output is obtained. PGiThe output of the thermal power generating unit is provided;
the iterative process is as follows:
(1) setting the counter k to 0, and selecting the initial iteration point x(0)Penalty factor r(0)；
(2) Constructing an intermediate function according to an interior point method for inequality constraint, and constructing an intermediate function according to an exterior point method for equivalent constraint;
(3) let k be k +1, r (k)＝cr(k-1)；
r(k) For the kth iteration penalty factor, r: (k-1) A penalty factor for the k-1 iteration;
(4) and (4) judging whether the iteration meets the precision requirement or not by the formula (18).
|f(x(k))-F(x(k))|＜,k＝1,2,… (18)
And (4) finishing the iterative process when the precision requirement is met to serve as a final approximate value of the optimal solution, and otherwise, turning to the step (3).
Further, a self-adaptive inertial weight is introduced into the particle swarm optimization algorithm to determine the particle swarm optimization algorithm, and the particle swarm optimization algorithm specifically comprises the following steps:
sorting the optimizing particles i from good to bad according to the individual optimal positions, wherein the particles i are sorted to the k-th position, and the corresponding good and bad sequence, the inertia weight coefficient, the individual extreme value acceleration coefficient and the global extreme value acceleration coefficient can be adaptively adjusted according to the formulas (10), (11), (12) and (13):
k＝order(i) (10)
ωk＝ωmin+(ωmax-ωmin)×(N-k)/(N-1) (11)
Figure BDA0002615010030000061
Figure BDA0002615010030000062
the updating formula of the improved particle swarm optimization algorithm is as follows:
Figure BDA0002615010030000063
Figure BDA0002615010030000064
1) setting various parameters of a particle swarm optimization algorithm: population size M, iteration times, convergence accuracy and learning factor c1And c2Maximum velocity Vmax(ii) a Randomly initializing speed and position;
2) calculating the fitness of each particle, comparing the fitness with the optimal value of each particle, if the fitness of the particle is smaller than the optimal value of each particle, replacing the optimal value of the previous round with a new fitness function value, and finding out the particle corresponding to the minimum objective function as the global optimal position Gbest；
3) The particle velocity and position are updated by equations (14) and (15), and if V exceeds the upper limit, it is set to VmaxSetting it to V below the lower limitmin；
4) And judging whether the iteration times are finished or the progress requirement is met, if so, jumping out of the loop to output the optimal solution, otherwise, jumping to the step 2) to continue the loop.
The present invention also provides a computer-readable storage medium, in which a computer program is stored, wherein the computer program, when executed by a processor, implements the steps of the method as provided in the above technical solution.
The invention has the beneficial technical effects;
(1) the new energy trans-regional consumption model considering the economic cost accurately calculates the trans-regional transmission channel transmission value and the optimal unit output with the optimal profit as the target under the conditions of meeting the constraints of section, inter-provincial links, adjacent provincial consumption margin and the like, realizes large-scale consumption of new energy, ensures the economical efficiency of the cost of the power generation side, improves the utilization rate of new energy in China and reduces the consumption of new energy;
(2) according to the method, the adaptive inertial weight particle swarm optimization algorithm and the improved penalty function method are introduced to solve the new energy consumption model, so that the model can be solved more quickly and accurately.
The invention determines the access scale of the renewable energy unit through different power grid scales, loads and power structure characteristics, and establishes a renewable energy consumption time series production simulation model based on the minimum total power generation cost. Firstly, establishing a renewable energy consumption model by taking the minimum total power generation cost as an objective function. And then processing the constraint conditions in the regulation of the renewable energy sources through a penalty function method and a feasible processing mechanism. On the basis, a time sequence production simulation method based on the improved particle swarm optimization is used for carrying out time-by-time simulation on the operation of a power grid to obtain an optimal renewable energy consumption value. And finally, taking provincial historical data as an example, the correctness of the evaluation algorithm and the feasibility of the model are verified, and guidance is provided for new energy consumption and power grid dispatching operation in the future.
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FIG. 1 is a schematic diagram of a new energy cross-region absorption principle;
FIG. 2 shows an embodiment of the present invention, X, Y, province, and province, with a margin of consumption;
FIG. 3 is a flow chart of a particle swarm optimization algorithm calculation according to an embodiment of the present invention;
FIG. 4 is a flow chart of penalty function calculation according to an embodiment of the present invention;
fig. 5 is a diagram of a tie line structure between regions in a certain province in accordance with an embodiment of the present invention;
FIG. 6 is a graph of new energy consumption per month for region B throughout the year, in accordance with an embodiment of the present invention;
FIG. 7 is a schematic diagram of the power consumed by new energy having a tie line on a certain day in accordance with an embodiment of the present invention;
FIG. 8 is a flow chart of model solution according to an embodiment of the present invention.
Detailed Description
The present invention will be described in detail below with reference to the accompanying drawings and specific embodiments.
The principle of the existing new energy cross-region consumption is shown in fig. 1, a region enclosed by a load curve and a minimum output curve in the diagram is a new energy consumption space, and when the output of the new energy exceeds the new energy consumption space, namely, when the consumption space is insufficient, the new energy is abandoned. The green part in the diagram is the input and output of the connecting line, namely when new energy is accepted by a single regional power grid, if the adjacent power grids have a space for accepting the new energy, the new energy can be mutually complemented through the connecting line. When the theoretical output of the new energy is smaller than the margin of the province, the margin of the new energy of the adjacent region can be taken away; when the theoretical capacity of the new energy is higher than the consumption margin, the exceeding part can be used for transmitting the consumption of the adjacent region.
The invention discloses a new energy trans-regional consumption model considering economy, aiming at solving the problems that new energy is difficult to consume on site and the cost consumption is too high due to single power supply structure and insufficient flexibility in China, and simulating real-time new energy output and load conditions to perform new energy trans-regional consumption by considering various constraint conditions to obtain an optimal consumption value and ensure the economy of new energy trans-regional transmission. Meanwhile, an improved particle swarm optimization algorithm and an improved penalty function method are provided on the traditional particle swarm optimization algorithm to solve the new energy cross-region consumption evaluation method.
The first embodiment provides a new energy cross-regional consumption evaluation method considering economy, and the specific implementation comprises the following steps:
establishing a new energy cross-region consumption model
And establishing a new energy cross-region consumption evaluation model considering the economy by analyzing factors influencing new energy consumption. The objective function of the new energy cross-region consumption model is specifically shown in formula (1).
Figure BDA0002615010030000091
In the formula, Ccoal(PGiT) -cost of fuel consumption, Cenv(PGiT) -cost of environmental pollution, D1-burningPrice of material, Yuan/t, D2-the environmental pollution price is the carbon dioxide and sulphur dioxide emission allowed price, yuan/t; b (t) -the power generation coal consumption rate of the thermal power generating unit, g/kWh, PGi(t) -thermal power unit output power, MW; c1Expressed as equivalent CO per coal combustion2The emission coefficient is set here to 2.87, Pl t-the value of the load,
Figure BDA0002615010030000092
-the output value of the jth hydroelectric generating set,
Figure BDA0002615010030000093
in the power output value of the ith thermal power generating unit, the fixed coefficient 1 is 0.001 in the embodiment, and the fixed coefficient can be set according to practical application in other embodiments.
Constraint conditions
(1) System operational constraints
The power balance is constrained to
PGi+Pwater+Pp+Pwind-Ptran＝P1 (2)
System spare capacity constraint of
S≥Pmaxl×(l％+s％)+Ppre×w％+Pr (3)
The downward regulating capacity of the generator set is restricted as
Gi＝Pi-Pi,min (4)
When inter-provincial outward delivery is considered, the section constraint and the new energy acceptance margin of the accepted province need to be considered, and specifically, as shown in fig. 2, the interval outward delivery capacity constraint formula is
D(X,Y)＝min(S(Y-X),R(Y-X))
In the formula, PfireThermal power output value, Pwater-hydroelectric output value, Pp-photovoltaic output value, Pwind-wind power output value, Ptran-the delivery power, P1-a load value; s-spare capacity required by the system, Pmaxl-predicting the maximum load, l% -Percentage of load reserve set to 5%, s% — percentage of accident reserve set to 2% -5%, Ppre-new energy predicted power, w% — predicted new energy contribution error demand for reserve capacity is set to 5%, P%r-servicing the reserve capacity; pline-maximum value of outgoing line power, D _ (X, Y) -X province and Y province with margin of consumption; s _ (Y-X) — a Y-province power network power exchange margin based on X province delivery Y province cross-section sensitivity; r _ (Y-X) — the clean energy consumption capability of the Y power saving network (X province is imported).
(2) Conventional genset constraints
The power constraint of the unit is
Pdown≤P≤Pup (5)
The climbing of the thermal power generating unit is restricted as
Pg(t)-Pg(t-1)≤Pg-up (6)
Pg(t-1)-Pg(t)≤Pg-down (7)
Section constraint
S＝min[(L1-P1)/α1，…，(Lj-Pj)/αj，…，(Ln-Pn)/αn] (8)
In the formula, PdownMinimum technical output of the unit, Pup-rated output; pg-upUpward ramp rate of the unit, Pg-down-downward ramp rate, S-grid power exchange margin based on section sensitivity; l isj、Pj-section j limit value, power; alpha is alphajThe sensitivity coefficient of the output of the unit in the power grid E to the power of the section j can be determined according to actual operation experience or obtained through calculation.
Second, solving model optimization algorithm
In the model solution of the embodiment, a Particle Swarm Optimization (PSO) algorithm is used, and an improved penalty function method is added to process the power balance constraint and the conventional unit constraint in the model, so that the new energy cross-region absorption evaluation model can be rapidly solved.
The PSO algorithm is an evolutionary algorithm based on group intelligence and provided by simulating the group behaviors of the bird groups. In the particle swarm algorithm, i particles form a population in n-dimensional space, the state of each particle is described by a speed V and a position X, and each particle passes through an individual optimal value (P)best) And global optimum (G)best) As an input, updates its V and X, and the update formula is shown in formula (8) and formula (9).
Figure BDA0002615010030000121
Figure BDA0002615010030000122
In the formula, omega is inertial weight and is used for controlling the degree of the movement of the particles along the original track; c. C1And c2-a learning factor for adjusting the step size of the approach of the particles to the individual optimal position and the population optimal position; r is1And r2-a random number between 0 and 1.
Penalty function
The penalty function method is characterized in that constraint conditions are incorporated into an objective function by introducing penalty factors to form an unconstrained objective function, a mathematical model of constrained optimization is shown as a formula (16), and the objective function after adding the penalty function is shown as a formula (17).
minf(x) (16)
Mj(x)≤0j＝1,2,…,m
Mi(x)＝0i＝1,2,…,l
Figure BDA0002615010030000123
Wherein f (x) -an objective function, Mi(x) -equality constraint, Mj(x) -inequality constraints, F (x) -new objective function, M-unsatisfied with constraint barsPenalty factor when piece.
In the new energy consumption process, a certain proportion of new energy is allowed to be abandoned in the load valley period so as to obtain larger new energy consumption, and the constraint of a power balance equation cannot be strictly met. The penalty function processing equality constraint condition needs to strictly meet the equality condition, and the method improves the penalty function method, and gives a certain margin to the equality constraint, so that the method can allow a certain abandon of new energy. The equation of the present invention is constrained to PGi+Pwater+Pp+Pwind-Ptran＝P1When P isGi+Pwater+Pp+Pwind-Ptran≥P1In time, the new energy can not be completely consumed even in the conventional unit and the outward delivery, and the value of the new energy is Ppv+Pwind＝P1+Ptran-PGi-Pwater. In the second embodiment, on the basis of the first embodiment, in consideration of the fact that the basic PSO algorithm is easy to fall into a local optimum point, which results in a large error of a result, the embodiment improves the basic PSO algorithm to introduce adaptive inertia weight to obtain an improved particle swarm optimization algorithm, that is, particles with different performances adopt different inertia weight coefficients, and particles with stronger performances correspond to larger inertia weight coefficients, so that the particles are responsible for optimizing a more optimal area; the weaker particles correspond to smaller inertial weight coefficients, which quickly converge to a better region for search. The model solution flow chart is shown in fig. 8.
The method sorts the optimizing particles i from good to bad according to the individual optimal positions, wherein the particles i are sorted to the k-th position, and the corresponding good and bad sequence, the inertia weight coefficient, the individual extreme value acceleration coefficient and the global extreme value acceleration coefficient can be adaptively adjusted according to the formulas (10), (11), (12) and (13):
k＝order(i) (10)
ωk＝ωmin+(ωmax-ωmin)×(N-k)/(N-1) (11)
Figure BDA0002615010030000131
Figure BDA0002615010030000132
at this time, the updating formula of the improved particle swarm optimization algorithm is
Figure BDA0002615010030000133
Figure BDA0002615010030000134
Wherein N is the number of particles, omegakIs the inertia weight coefficient of the particle arranged at k, c1kAnd c2kAre the individual extremum acceleration coefficients and the global extremum acceleration coefficients.
The calculation process of the improved particle swarm optimization algorithm is as follows, as shown in fig. 2:
1) setting various parameters of a particle swarm optimization algorithm: the population size M is generally selected to be 100-200, and the population size M is selected to be 100 in the invention; the iteration times and the convergence precision are used for ensuring that the particles can jump out of the loop and do not fall into the dead loop; learning factor c1And c2Is 1.5; with respect to maximum speed VmaxSetting, if the maximum speed is set too large, the optimal solution can be missed due to direct jumping out, and if the maximum speed is set too small, the search is too slow due to too many iteration times; randomly initializing speed and position;
2) calculating the fitness of each particle, comparing the fitness with the optimal value of each particle, if the fitness of the particle is smaller than the optimal value of each particle, replacing the optimal value of the previous round with a new fitness function value, and finding out the particle corresponding to the minimum objective function as the global optimal position Gbest；
3) The particle velocity and position are updated by equations (14) and (15), and if V exceeds the upper limit, it is set to VmaxSetting it to V below the lower limitmin；
4) And (3) judging whether the iteration times are finished or the progress requirement is met, if so, jumping out of the loop to output the optimal solution, otherwise, jumping to the step (2) to continue the loop.
Penalty function
The penalty function method is characterized in that constraint conditions are incorporated into an objective function by introducing penalty factors to form an unconstrained objective function, a mathematical model of constrained optimization is shown as a formula (16), and the objective function after adding the penalty function is shown as a formula (17).
minf(x) (16)
Mj(x)≤0j＝1,2,…,m
Mi(x)＝0i＝1,2,…,l
Figure BDA0002615010030000141
Wherein f (x) -an objective function, Mi(x) -equality constraint, Mj(x) -inequality constraints, f (x) -new objective functions, M-penalty coefficients when constraint conditions are not satisfied.
In the new energy consumption process, a certain proportion of new energy is allowed to be abandoned in the load valley period so as to obtain larger new energy consumption, and the constraint of a power balance equation cannot be strictly met. The penalty function processing equality constraint condition needs to strictly meet the equality condition, and the method improves the penalty function method, and gives a certain margin to the equality constraint, so that the method can allow a certain abandon of new energy. The equation of the present invention is constrained to PGi+Pwater+Pp+Pwind-Ptran＝P1When P isGi+Pwater+Pp+Pwind-Ptran≥P1In time, the new energy can not be completely consumed even in the conventional unit and the outward delivery, and the value of the new energy is Ppv+Pwind＝P1+Ptran-PGi-Pwater。
The iterative process is as follows:
(1) setting the counter k to 0, and selecting the initial iteration point x(0)Penalty factor r(0)；
(2) Constructing an intermediate function according to an interior point method for inequality constraint, and constructing an intermediate function according to an exterior point method for equivalent constraint;
(3) let k equal k +1, r(k)＝cr(k-1)；
(4) And (4) judging whether the iteration meets the precision requirement or not by the formula (18).
|f(x(k))-F(x(k))|＜,k＝1,2,… (18)
And (4) finishing the iterative process when the precision requirement is met to serve as a final approximate value of the optimal solution, and otherwise, turning to the step (3). The flow chart of the process is shown in fig. 3.
Third, simulation example verification
Taking the power grid shown in fig. 5 as an example, the provincial power grid can be divided into three regions, and the transmission power of the inter-regional tie lines and the new energy consumption values of the regions are solved on the basis of the economic optimization principle, and the installed power generation capacity of the regions is specifically shown in table 1.
TABLE 1 installation situation of each region
Figure BDA0002615010030000161
Firstly, taking a single area B as an example, the area mainly participates in system peak regulation by thermal power generation, the peak regulation capacity of a conventional unit is 30% -40% of the installed capacity of the conventional unit, and the deep peak regulation can reach about 50%. The output of the heat supply unit in the heat supply period is 75-85% of the installed capacity of the heat supply unit. After simulation, the cross-region absorption benefit index is obtained and shown in table 2, the new energy transmission channel utilization ratio is shown in table 3, and the annual monthly new energy absorption value is shown in fig. 6.
TABLE 2 Cross-regional benefit index based on time sequence production simulation method
Figure BDA0002615010030000162
TABLE 3 concrete utilization ratio of New energy Transmission channel
Figure BDA0002615010030000163
Table 2 mainly shows the comparison of different mathematical models (the objective function model with maximum consumption and the objective function model considering economy) of the region B. When different criteria are adopted, the consumption of new energy and the cost of power supply are different. Compared with the maximum consumption value objective function, the maximum profit is the objective function, the proportion of the abandoned new energy is improved from 14.97% to 16.32%, but the power generation cost is saved by about 40838 ten thousand yuan, which indicates that a part of wind power needs to be abandoned in order to improve the system economy.
And considering the consumption of new energy resources with economic efficiency and complying with the actual operation condition. Fig. 7 is a power diagram of area B with consideration of economic new energy consumption across areas at random one day all the year.
When different targets are adopted, the energy consumption of the new energy is different, and the energy consumption is larger at peak load. According to the model established by the method, under the power balance constraint condition, the new energy accords with the actual condition, the output of the thermal power generating unit is smooth, and the requirement of the actual climbing constraint of the thermal power generating unit is met.
The renewable energy source adjusting method based on the minimum cost adopts time series production simulation, comprehensively considers the factors of the output characteristic, peak regulation sufficiency, load characteristic, thermal power unit output, thermal power unit climbing and the like of renewable energy sources, and develops a more accurate and more detailed annual renewable energy source province power grid energy consumption plan.
The method comprises the steps of firstly, establishing an objective function by taking the minimum total power generation cost as the objective function. The constraints are then solved using the improved penalty function method. And finally, obtaining the optimal consumption value of the renewable energy source under the time sequence production simulation through an improved particle swarm optimization algorithm. Through comparative analysis of the economy of each objective function, the most economical new energy consumption of a power grid enterprise is obtained by taking the minimum total cost as the objective function, and guidance can be provided for new energy consumption and power grid dispatching operation in the future.
The invention determines the access scale of the renewable energy unit through different power grid scales, loads and power structure characteristics, and establishes a renewable energy consumption time series production simulation model based on the minimum total power generation cost. Firstly, establishing a renewable energy consumption model by taking the minimum total power generation cost as an objective function. Constraints in the renewable energy regulation are then treated by improving the penalty function method. On the basis, a time sequence production simulation method based on the improved particle swarm optimization is used for carrying out time-by-time simulation on the operation of a power grid to obtain an optimal renewable energy consumption value. And finally, taking provincial historical data as an example, the correctness of the evaluation algorithm and the feasibility of the model are verified, and guidance is provided for new energy consumption and power grid dispatching operation in the future.
As will be appreciated by one skilled in the art, embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present application is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the application. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
While the present invention has been described with reference to the embodiments shown in the drawings, the present invention is not limited to the embodiments, which are illustrative and not restrictive, and it will be apparent to those skilled in the art that various changes and modifications can be made therein without departing from the spirit and scope of the invention as defined in the appended claims.

Claims (6)

1. The method for evaluating the cross-regional consumption of the new energy in consideration of the economical efficiency is characterized by comprising the following steps:
establishing a renewable energy consumption model by taking the minimum total power generation cost as an objective function, wherein the total power generation cost takes fuel consumption cost, unit start-stop cost, environmental pollution cost, fuel price and carbon dioxide and sulfur dioxide emission permission price into account;
establishing constraint conditions of a renewable energy consumption model, wherein the constraint conditions comprise system operation constraint and conventional generator set constraint;
and solving the renewable energy consumption model to obtain the transmission power of the tie line between the regions and the new energy consumption value of each region.
2. The economic new energy consumption cross-region assessment method according to claim 1, wherein said renewable energy consumption model is represented as follows:
Figure FDA0002615010020000011
wherein C iscoal(PGiT) cost of fuel consumption, Cenv(PGiT) cost of environmental pollution, Q (P)GiT) the start-stop cost of the unit, G the number of the units, T the time, i the ith unit, K the fixed coefficient, D1As a price of fuel, D2The allowable price for discharging carbon dioxide and sulfur dioxide, b (t) the power generation coal consumption rate of the thermal power generating unit, PGi(t) is the output power of the thermal power generating unit, C1Expressed as equivalent CO per coal combustion2Coefficient of emission, Pl tIn order to be the load value,
Figure FDA0002615010020000012
is the output value of the jth hydroelectric generating set, m is the total number of the hydroelectric generating sets,
Figure FDA0002615010020000013
and (4) outputting a force value for the ith thermal power generating unit, wherein n is the total number of the thermal power generating units.
3. The economic new energy trans-regional consumption assessment method according to claim 2, wherein the constraints of said renewable energy consumption model include the following:
(1) system operation constraints including power balance constraints, system reserve capacity constraints, generator set downward regulation capacity constraints and interval outgoing capacity constraints;
the power balance constraint is
PGi+Pwater+Ppv+Pwind-Ptran＝P1 (2)
The system spare capacity is constrained to
S≥Pmax1×(l％+s％)+Ppre×w％+Pr (3)
The downward regulating capacity of the generator set is restricted as
Gi＝Pi-Pi，min (4)
The interval delivery capacity constraint is
D(X，Y)＝min(S(Y-X)，R(Y-X)) (5)
In the formula, PGiFor thermal power output, PwaterFor the water discharge value, PpvFor photovoltaic output value, PwindFor wind power output value, PtranFor delivery of power, P1Is a load value; s is the spare capacity, P, required by the systemmax1To predict maximum load,% is percent load reserve, s% is percent accident reserve, PprePredicting power for the new energy, w% demand for reserve capacity for predicted new energy contribution error, PrFor maintenance of reserve capacity, GiFor generator set i turndown capability, PiFor the ith conventional unit output, Pi，minD (X, Y) is X, Y and saving margin for saving X and Y; s (Y-X) is a power exchange margin of a Y power-saving network based on the sensitivity of the X-saving and Y-saving section; r (Y-X) is the clean energy consumption capability of the Y power-saving network, and X is saved in the input direction;
(2) the conventional generator set constraint comprises a generator set power constraint, a thermal power unit climbing constraint and a section constraint;
the unit power constraint is
Pdown≤P≤Pup (5)
The climbing of the thermal power generating unit is restricted as
Pg(t)-Pg(t-1)≤Pg-up (6)
Pg(t-1)-Pg(t)≤Pg-down (7)
The section constraint is
S＝min[(L1-P1)/α1，...，(Lj-Pj)/αj，...，(Ln-Pn)/αn] (8)
In the formula, PdownIs the minimum output of the unit, P is the output of the unit, PupRated output is used; pg-upUpward ramp rate of the unit, Pg-downFor downward climbing rate, S is based on section agentPower grid power exchange margin of sensitivity; l isjIs the limit of the section j, PjThe power of the section j is used; alpha is alphajSensitivity coefficient, P, of the output of the unit in the grid E to the power of the section jg(t)The output of the thermal power generating unit at the t momentg(t-1)The power output of the thermal power generating unit at the t-1 moment, and n is the total number of the sections.
4. The economic new energy cross-regional consumption evaluation method according to claim 3, wherein the renewable energy consumption model is solved by adopting a particle swarm optimization algorithm and adding an improved penalty function method to process the power balance constraint and the conventional unit constraint in the model, and specifically comprises the following steps:
the constraint condition is incorporated into the objective function by introducing a penalty factor to form an unconstrained objective function, which is expressed as follows:
Figure FDA0002615010020000031
wherein f (x) is an objective function, Mi(x) For equality constraints, Mj(x) For inequality constraint, F (x) is a new objective function, and M is a penalty coefficient when a constraint condition is met;
the constraint condition of the renewable energy consumption model comprises an equality constraint which is PGi+Pwater+Pp+Pwind-Ptran＝P1When P isGi+Pwater+Pp+Pwind-Ptran≥P1In time, the new energy can not be completely consumed by the conventional unit and the outward delivery, and the value of the new energy is Ppv+Pwind＝P1+Ptran-PGi-Pwater；PpvPhotovoltaic output is obtained; pGiThe output of the thermal power generating unit is provided;
the iterative process is as follows:
(1) setting the counter k to 0, and selecting the initial iteration point x(0)Penalty factor r(0)；
(2) Constructing an intermediate function according to an interior point method for inequality constraint, and constructing an intermediate function according to an exterior point method for equivalent constraint;
(3) let k equal k +1, r(k)＝cr(k-1)；
r(k)For the kth iteration penalty factor, r(k-1)A penalty factor for the k-1 iteration;
(4) judging whether the iteration meets the precision requirement through an equation (18);
|f(x(k))-F(x(k))|＜，k＝1，2，…； (18)
and (4) finishing the iterative process when the precision requirement is met to serve as a final approximate value of the optimal solution, and otherwise, turning to the step (3).
5. The economic new energy trans-regional consumption evaluation method according to claim 4, wherein an adaptive inertial weight is introduced into the particle swarm optimization algorithm to determine a particle swarm optimization algorithm, and the particle swarm optimization algorithm comprises the following specific steps:
sorting the optimizing particles i from good to bad according to the individual optimal positions, wherein the particles i are sorted to the k-th position, and the corresponding good and bad sequence, the inertia weight coefficient, the individual extreme value acceleration coefficient and the global extreme value acceleration coefficient can be adaptively adjusted according to the formulas (10), (11), (12) and (13):
k＝order(i) (10)
ωk＝ωmin+(ωmax-ωmin)×(N-k)/(N-1) (11)
Figure FDA0002615010020000051
Figure FDA0002615010020000052
the updating formula of the improved particle swarm optimization algorithm is as follows:
Figure FDA0002615010020000053
Figure FDA0002615010020000054
1) setting various parameters of a particle swarm optimization algorithm: population size M, iteration times, convergence accuracy and learning factor c1And c2Maximum velocity Vmax(ii) a Randomly initializing speed and position;
2) calculating the fitness of each particle, comparing the fitness with the optimal value of each particle, if the fitness of the particle is smaller than the optimal value of each particle, replacing the optimal value of the previous round with a new fitness function value, and finding out the particle corresponding to the minimum objective function as the global optimal position Gbest；
3) The particle velocity and position are updated by equations (14) and (15), and if V exceeds the upper limit, it is set to VmaxSetting it to V below the lower limitmin；
4) And judging whether the iteration times are finished or the progress requirement is met, if so, jumping out of the loop to output the optimal solution, otherwise, jumping to the step 2) to continue the loop.
6. A computer-readable storage medium, in which a computer program is stored which, when being executed by a processor, carries out the steps of the method according to any one of claims 1 to 5.
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Cited By (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN113128750A (en) * 2021-03-16 2021-07-16 贵州电网有限责任公司 Water-fire-electricity generator set maintenance plan optimization decomposition method considering clean energy consumption
CN113162124A (en) * 2021-03-22 2021-07-23 中国电力科学研究院有限公司 New energy concentrated grid-connected node output optimization method, device, equipment and medium
CN114285090A (en) * 2021-12-22 2022-04-05 国网江苏省电力有限公司盐城供电分公司 New energy limit consumption capability evaluation method based on single station-partition-whole network
CN114418198A (en) * 2021-12-30 2022-04-29 国网辽宁省电力有限公司电力科学研究院 Segmented function type calculation method for punishment cost of abandoned new energy

Citations (8)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN102496968A (en) * 2011-12-20 2012-06-13 国电南瑞科技股份有限公司 Generation plan optimizing method in intermittent energy and conventional energy coordinated dispatching mode
CN106786799A (en) * 2017-01-03 2017-05-31 国电南瑞科技股份有限公司 A kind of DC link power step elelctrochemical power generation plan optimization method
CN107221947A (en) * 2017-08-07 2017-09-29 国电南瑞科技股份有限公司 The extra-high voltage direct-current Active Power Optimization Dispatching method of meter and sending end new energy and receiving end load correlation
CN107508328A (en) * 2017-04-08 2017-12-22 东北电力大学 Consider the association system energy optimizing method of wind electricity digestion
CN109245183A (en) * 2018-05-21 2019-01-18 国网河南省电力公司安阳供电公司 A kind of honourable permeability area power grid peak regulating method of height based on load control system
CN109449971A (en) * 2018-10-29 2019-03-08 国网甘肃省电力公司 A kind of multiple target electric power system source lotus interaction Optimization Scheduling of new energy consumption
CN109888830A (en) * 2019-03-20 2019-06-14 国网能源研究院有限公司 A kind of stored energy capacitance Optimal Configuration Method adapting to extensive new-energy grid-connected consumption
CN110930188A (en) * 2019-11-19 2020-03-27 西安交通大学 New energy and thermal power bundling transaction pricing method considering resource complementary characteristics

Patent Citations (8)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN102496968A (en) * 2011-12-20 2012-06-13 国电南瑞科技股份有限公司 Generation plan optimizing method in intermittent energy and conventional energy coordinated dispatching mode
CN106786799A (en) * 2017-01-03 2017-05-31 国电南瑞科技股份有限公司 A kind of DC link power step elelctrochemical power generation plan optimization method
CN107508328A (en) * 2017-04-08 2017-12-22 东北电力大学 Consider the association system energy optimizing method of wind electricity digestion
CN107221947A (en) * 2017-08-07 2017-09-29 国电南瑞科技股份有限公司 The extra-high voltage direct-current Active Power Optimization Dispatching method of meter and sending end new energy and receiving end load correlation
CN109245183A (en) * 2018-05-21 2019-01-18 国网河南省电力公司安阳供电公司 A kind of honourable permeability area power grid peak regulating method of height based on load control system
CN109449971A (en) * 2018-10-29 2019-03-08 国网甘肃省电力公司 A kind of multiple target electric power system source lotus interaction Optimization Scheduling of new energy consumption
CN109888830A (en) * 2019-03-20 2019-06-14 国网能源研究院有限公司 A kind of stored energy capacitance Optimal Configuration Method adapting to extensive new-energy grid-connected consumption
CN110930188A (en) * 2019-11-19 2020-03-27 西安交通大学 New energy and thermal power bundling transaction pricing method considering resource complementary characteristics

Non-Patent Citations (2)

* Cited by examiner, † Cited by third party
Title
柴大鹏: "低碳背景下大规模风电并网消纳决策模型及方法研究", 《中国博士学位论文全文数据库 工程科技Ⅱ辑》 *
田露露: "提高新能源消纳能力的主动配电网能量管理策略研究", 《中国优秀硕士学位论文全文数据库 工程科技Ⅱ辑》 *

Cited By (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN113128750A (en) * 2021-03-16 2021-07-16 贵州电网有限责任公司 Water-fire-electricity generator set maintenance plan optimization decomposition method considering clean energy consumption
CN113162124A (en) * 2021-03-22 2021-07-23 中国电力科学研究院有限公司 New energy concentrated grid-connected node output optimization method, device, equipment and medium
CN113162124B (en) * 2021-03-22 2022-06-14 中国电力科学研究院有限公司 New energy concentrated grid-connected node output optimization method, device, equipment and medium
CN114285090A (en) * 2021-12-22 2022-04-05 国网江苏省电力有限公司盐城供电分公司 New energy limit consumption capability evaluation method based on single station-partition-whole network
CN114418198A (en) * 2021-12-30 2022-04-29 国网辽宁省电力有限公司电力科学研究院 Segmented function type calculation method for punishment cost of abandoned new energy

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