Scheduling optimization method for wind-solar-thermal storage integrated energy system

Abstract

The invention discloses a scheduling optimization method for a wind-solar-thermal storage integrated energy system. The scheduling optimization method comprises the steps of 1, establishing a stepped carbon transaction price cost model of basic carbon price fluctuation; 2, introducing the established stepped carbon transaction price cost model, and fusing to establish a low-carbon scheduling model of the wind-solar-thermal storage integrated energy system; and step 3, establishing two robust optimization models for solving the low-carbon scheduling model of the wind-light-fire-storage integrated energy system, and introducing an adjustable robust parameter gamma to adjust the conservative degree of an optimization result in each time period by considering the uncertainty set representation form of the uncertainty of the wind-light basic carbon price so as to solve the low-carbon scheduling model of the wind-light-fire-storage integrated energy system. And solving the two robust optimization models by adopting a column constraint generation algorithm to form a wind-solar-thermal storage comprehensive energy low-carbon scheduling model. According to the invention, an energy storage system is organically combined with wind-solar-thermal power, the fluctuation and uncertainty of new energy output are dealt with, the safe and stable operation of a power system is ensured, the peak regulation pressure of a thermal power generating unit is reduced, and meanwhile, the carbon emission is reduced.

Description

Technical Field

[0001] The present invention relates to a dispatching optimization technology, and in particular to a dispatching optimization method for a wind, solar, thermal and energy storage integrated energy system. Background Art

[0002] As a multi-energy complementary optimization scheduling method, wind, solar, thermal and storage integrated energy scheduling can not only improve the comprehensive utilization efficiency of energy, but also cope with the volatility and uncertainty of energy supply through various means. As an important part of the power system, the energy storage system can not only store energy when there is excess electricity and improve the absorption capacity of renewable energy, but also balance the load by peak shaving and valley filling when there is a power shortage, ensuring the safe and stable operation of the power system. However, there is a lack of scheduling optimization methods for wind, solar, thermal and storage integrated energy systems in the existing technology.

[0003] Common uncertainty optimization methods mainly include random optimization, fuzzy optimization and robust optimization. Random optimization and fuzzy optimization models usually require detailed probability distribution information, which is difficult to obtain accurately in practical applications, and there is a certain gap between probability data and actual conditions. The larger the amount of data, the more accurate the description of the distribution of uncertainty variables, but it will greatly increase the computational complexity. Summary of the invention

[0004] The purpose of the present invention is to overcome the shortcomings of the above-mentioned prior art and provide a dispatching optimization method for a wind, solar, thermal and energy storage integrated energy system, so as to organically combine the energy storage system with wind, solar and thermal power, cope with the volatility and uncertainty of new energy output, and ensure the safe and stable operation of the power system. At the same time, through the regulation of the energy storage system, the randomness of renewable energy such as wind power and photovoltaics can be partially eliminated, reducing the peak-shaving pressure of thermal power units and reducing carbon emissions.

[0005] To achieve the above object, the technical solution of the present invention is:

[0006] A method for optimizing the dispatching of a wind, solar, thermal and energy storage integrated energy system, comprising:

[0007] Step 1: Establish a ladder-type carbon trading price cost model with basic carbon price fluctuations;

[0008] Step 2: Introduce the established ladder-type carbon trading price cost model and integrate it to establish a low-carbon dispatch model for the wind, solar, thermal and energy storage integrated energy system;

[0009] Step 3: Establish a two-stage robust optimization model to solve the low-carbon scheduling model of the wind, solar, thermal and storage integrated energy system, and introduce an adjustable robust parameter Γ in the form of an uncertain set representation of the uncertainty of the basic carbon price of wind and solar power to adjust the conservatism of the optimization results in each time period. The two-stage robust optimization model is solved using a column constraint generation algorithm to form a low-carbon scheduling model of the wind, solar, thermal and storage integrated energy.

[0010] Compared with the prior art, the present invention has the following beneficial effects:

[0011] The present invention introduces the uncertainty of wind power, photovoltaic output, and carbon price to construct a wind, solar, thermal and storage comprehensive energy low-carbon dispatch model that considers the uncertainty of new energy output and carbon price with a step-type carbon trading mechanism. On the basis of minimizing the operating cost, the model comprehensively considers the carbon emission cost and the absorption capacity of renewable energy, and realizes the minimum cost and carbon emission in the worst scenario, so as to store energy when there is excess electricity, improve the absorption capacity of renewable energy, and balance the load by peak shaving and valley filling when there is a shortage of electricity. In addition, the organic combination of energy storage system and wind, solar and thermal power can effectively cope with the volatility and uncertainty of new energy output to ensure the smooth and safe operation of the power system; through the regulation of the energy storage system, the randomness of renewable energy such as wind power and photovoltaic can be partially eliminated, reducing the peak-shaving pressure of thermal power units and reducing carbon emissions. At the same time, this method is described by an uncertain set. The two-stage robust optimization model splits the uncertainty variables into the main problem and sub-problems. The outer layer determines the start and stop of thermal power units and the wind power photovoltaic output and the charging and discharging plan of the energy storage system. The second stage adjusts the power output of various energy equipment in the worst scenario through flexible regulation to ensure the robustness of the system. This optimization method can effectively reduce fluctuations in operating costs and improve the safety and economy of the system when dealing with output fluctuations of new energy sources such as wind power and photovoltaics. BRIEF DESCRIPTION OF THE DRAWINGS

[0012] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings required for use in the embodiments are briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0013] Figure 1 Complete flow chart of the dispatch optimization method for the wind-solar-thermal-storage integrated energy system provided by the embodiment of the present invention

[0014] Figure 2 A diagram of the scheduling results calculated using the wind, solar, thermal and energy storage integrated energy system scheduling optimization method provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0015] Example:

[0016] The technical solution of the present invention is further described below in conjunction with the accompanying drawings and embodiments.

[0017] See also Figure 1 As shown, the wind-solar-thermal-storage integrated energy system scheduling optimization method provided in this embodiment mainly includes the following steps:

[0018] Step 1: Establish a ladder-type carbon trading price cost model with basic carbon price fluctuations;

[0019] Step 2: Introduce the established ladder-type carbon trading price cost model and integrate it to establish a low-carbon dispatch model for the wind, solar, thermal and energy storage integrated energy system;

[0020] Step 3: Establish a two-stage robust optimization model to solve the low-carbon scheduling model of the wind, solar, thermal and storage integrated energy system, and introduce an adjustable robust parameter Γ in the form of an uncertain set representation of the uncertainty of the basic carbon price of wind and solar power to adjust the conservatism of the optimization results in each time period. The two-stage robust optimization model is solved using a column constraint generation algorithm to form a low-carbon scheduling model of the wind, solar, thermal and storage integrated energy.

[0021] It can be seen that this method introduces the uncertainty of wind power, photovoltaic output, and carbon price with a step-type carbon trading mechanism to construct a wind, solar, thermal and storage integrated energy low-carbon dispatch model that takes into account the uncertainty of new energy output and carbon price. On the basis of minimizing the operating cost, this model comprehensively considers the carbon emission cost and the absorption capacity of renewable energy, and achieves the minimum cost and carbon emission in the worst scenario, thereby storing energy when there is a surplus of electricity, improving the absorption capacity of renewable energy, and balancing the load by peak shaving and valley filling when there is a power shortage. In addition, the organic combination of energy storage system and wind, solar and thermal power can effectively cope with the volatility and uncertainty of new energy output to ensure the smooth and safe operation of the power system; through the regulation of the energy storage system, the randomness of renewable energy such as wind power and photovoltaics can be partially eliminated, reducing the peak-shaving pressure of thermal power units and reducing carbon emissions at the same time. At the same time, this method uses an uncertain set to describe the two-stage robust optimization model, which splits the uncertain variables into the main problem and sub-problems. The outer layer determines the start and stop of thermal power units, wind power and photovoltaic output, and the charging and discharging plan of the energy storage system. The second stage adjusts the power output of various energy equipment in the worst scenario through flexible regulation to ensure the robustness of the system. This optimization method can effectively reduce the fluctuation of operating costs and improve the safety and economy of the system when dealing with the output fluctuation of new energy sources such as wind power and photovoltaics.

[0022] In a specific embodiment, wind and solar energy are used to supplement thermal power supply. When wind energy and solar energy are sufficient, in addition to meeting the electricity load, a wind, solar, thermal and storage integrated energy system is used to absorb more wind and solar energy. When wind and solar energy are underestimated, the wind, solar, thermal and storage integrated energy system is released to meet the electricity load.

[0023] In a specific embodiment, in step 1, the established ladder-type carbon trading price cost model is:

[0024]

[0025] Among them, E ais the actual carbon emission of thermal power units, X a is the CO2 emission coefficient of thermal power units, P i，t is the actual output power of the i-th coal-fired unit at time t; assuming that carbon emissions are basically emitted by thermal power units;

[0026]

[0027] Among them, λ is the fluctuating basic carbon emission price; a is the carbon emission price growth coefficient; d is the step interval, E c is the excess carbon emissions, f c The cost of carbon emissions.

[0028] E c =E a -E free (3)

[0029] Among them, E free Free emission allowances.

[0030] The objective function of the low-carbon dispatch model of the wind-solar-thermal-storage integrated energy system is:

[0031] min C Z (x) = w1·C total (x)+w2·E a (x) (5)

[0032] Among them, w1 and w2 are economic and low-carbon weight coefficients, C Z (x) is the cost of the outer integrated energy system scheduling plan, x is the low-carbon economic scheduling plan; E a (x) is the actual carbon emissions;

[0033] C total (x) = C ho (x)+C fe (x)+C gu (x)+C sto (x)+C qi (x)+f c (6)

[0034] Among them, C ho (x) is the cost of thermal power unit; C fe (x) is the wind power operation cost; C gu (x) is the photovoltaic operation cost; C sto (x) is the energy storage operation cost; C qi (x) is the cost of curtailing wind and solar power.

[0035] Specifically, the costs of each part are as follows:

[0036] The cost of thermal power generation units includes:

[0037]

[0038] Among them, N TH is the number of thermal power units, C coal is the coal price; a i , b i , c i is the coal combustion coefficient of thermal power unit (i); P i，t is the power generation of thermal power unit (i) at time t on a certain day; Δt is the scheduling span step; C T，i is the start-up and shutdown cost of the thermal power unit at time t. i，t is the start / stop status of the thermal power unit at time t [0 is shutdown, 1 is startup]; C1 is the basic peak-shaving cost.

[0039] C2=βS C，i / (2N f (P)) (8)

[0041] Among them, β is the loss coefficient of the thermal power unit; S C，i is the purchase cost of thermal power unit i; N f (P) is the number of rotor cracking cycles; C2 is the peak load regulation cost when no oil is added.

[0042] C3=S oil Q oil，i (9)

[0043] Among them, S oil is the current oil price; Q oil，i is the oil injection amount for unit i.

[0044] The cost of thermal power generation unit is:

[0045]

[0046] The operating cost of wind, solar and energy storage is:

[0047]

[0048] Among them, P WT,t , P PV,t are the power generation of wind and solar units respectively; C s , C PV , C WT are the operating cost coefficients of energy storage equipment and wind and solar power generation equipment respectively; η S is the charge and discharge efficiency; and are respectively the discharge and charge amounts of the energy storage device at time t.

[0049] Cost of curtailing wind and solar power

[0050]

[0051] Among them, C cu,wind

[0052] In a specific embodiment, the constraints of the low-carbon scheduling model of the wind-solar-thermal-storage integrated energy system include:

[0053] Power balance constraints:

[0054]

[0055] Among them, U S,t is the energy storage charging and discharging state at time t; P LOAD,t represents the power load demand at time t;

[0056] Thermal power unit constraints

[0057]

[0058] in, are the maximum up and down ramp rates of thermal power unit i respectively;

[0059] Energy storage system constraints

[0060]

[0061] in, They are the upper limit of energy storage discharge power and charging power respectively;

[0062] Energy storage state of charge constraints:

[0063]

[0064] Among them, P s (0) is the initial amount of energy storage; is the amount of electricity stored in the energy storage system at time t; They are respectively the upper and lower limits of the energy storage system’s charge state; is the rated capacity of the energy storage;

[0065] Energy storage system charging incentive constraints:

[0066] E sy (t) = P WT,t +P PV,t -D(t)

[0067]

[0068] Where D(t) is the load demand at time t, E sy (t) is the remaining wind and solar load at time t.

[0069] In a specific embodiment, the uncertainty of the basic carbon price of wind and solar power includes:

[0070]

[0071] And an adjustable robust optimization parameter Γ is introduced to adjust the conservatism of the optimization results at each moment;

[0072]

[0073] in, They are the upper and lower limits of the photovoltaic unit output respectively; The upper and lower limits of wind turbine output; max , min They are the upper and lower limits of the real-time basic carbon price respectively;

[0074] A min-max-min two-stage robust optimization model is established for the uncertainty of renewable energy output and basic carbon price:

[0075]

[0076] Among them, C Z (x) is the operating cost of the outer integrated energy system; x = [U i,t ,U S,t ] T , u is the set of predicted y values; A, B, C, D, G, H, J are coefficient matrices; h, e, f, g are constant column vectors

[0077] The column constraint generation algorithm is used to solve the two-stage robust optimization model algorithm, and the outer and inner problems are solved iteratively, including:

[0078] (1) Set the initial value and randomly give a set of uncertainty parameters u as the initial situation. The initial upper and lower bounds are both +∞, the number of iterations k = 1, and the convergence value ε is a small positive number.

[0079] (2) Solve the main problem in the worst scenario and obtain the optimal solution (x k ,θ k ,y1,y2,...,y k ), and update the lower bound LB=max{LB,θ k}.

[0080] (3) The optimal solution of the main problem x k Substitute into the subproblem and solve the subproblem to obtain the optimal solution And the optimal objective function value And update the upper bound value

[0081] (4) Determine whether the result converges. If |UB-LB|≤ε, stop the iteration and output the optimal solution. Otherwise, add constraints.

[0082]

[0083] And update the number of iteration steps k=k+1, and return to step (2).

[0084] Thus, by adopting the above-mentioned solution method, the difficulty of solution is greatly reduced.

[0085] like Figure 2 As shown, this is a scheduling result diagram calculated using the wind, solar, thermal and energy storage integrated energy system scheduling optimization method provided in this embodiment.

[0086] The above embodiments are only for illustrating the technical concept and features of the present invention, and their purpose is to enable ordinary technicians in the field to understand the content of the present invention and implement it accordingly, and they cannot be used to limit the protection scope of the present invention. Any equivalent changes or modifications made based on the essence of the content of the present invention should be included in the protection scope of the present invention.

Claims

1. A method for optimizing the dispatching of a wind, solar, thermal and energy storage integrated energy system, characterized in that: include: Step 1: Establish a ladder-type carbon trading price cost model with basic carbon price fluctuations; Step 2: Introduce the established ladder-type carbon trading price cost model and integrate it to establish a low-carbon dispatch model for the wind, solar, thermal and energy storage integrated energy system; Step 3: Establish a two-stage robust optimization model to solve the low-carbon scheduling model of the wind, solar, thermal and storage integrated energy system, and introduce an adjustable robust parameter Γ in the form of an uncertain set representation of the uncertainty of the basic carbon price of wind and solar power to adjust the conservatism of the optimization results in each time period. The two-stage robust optimization model is solved using a column constraint generation algorithm to form a low-carbon scheduling model of the wind, solar, thermal and storage integrated energy.

2. The method for optimizing the dispatching of the wind, solar, thermal and energy storage integrated energy system according to claim 1 is characterized in that: Use wind and solar energy to supplement thermal power supply. When wind energy and solar energy are sufficient, in addition to meeting the electricity load, use the wind, solar, thermal and storage integrated energy system to absorb more wind and solar energy. When wind and solar energy are underestimated, release the wind, solar, thermal and storage integrated energy system to meet the electricity load.

3. The method for optimizing the dispatching of a wind, solar, thermal and energy storage integrated energy system according to claim 1, characterized in that: In step 1, the established ladder-type carbon trading price cost model is: Among them, E a is the actual carbon emission of thermal power units, X a is the CO2 emission coefficient of thermal power units, P i,t is the actual output power of the i-th coal-fired unit at time t; Assume that carbon emissions are mainly emitted by thermal power units; Among them, λ is the fluctuating basic carbon emission price; a is the carbon emission price growth coefficient; d is the step interval, E c is the excess carbon emissions, f c for the cost of carbon emissions; AND c =And a -AND free (3) Among them, E free Free emission allowances.

4. The method for optimizing the dispatching of a wind, solar, thermal and energy storage integrated energy system according to claim 1, characterized in that: The objective function of the low-carbon dispatch model of the wind-solar-thermal-storage integrated energy system is: min C Z (x)=w1·C total (x)+w2·E a (x) (5) Among them, w1 and w2 are economic and low-carbon weight coefficients, C Z (x) is the cost of the outer integrated energy system scheduling plan, x is the low-carbon economic scheduling plan; E a (x) is the actual carbon emissions; C total (x)=C ho (x)+C fe (x)+C gu (x)+C sto (x)+C qi (x)+f c (6) Among them, C ho (x) is the cost of thermal power unit; C fe (x) is the wind power operation cost; C gu (x) is the photovoltaic operation cost; C sto (x) is the energy storage operation cost; C qi (x) is the cost of curtailing wind and solar power.

5. The method for optimizing the dispatching of the wind, solar, thermal and energy storage integrated energy system according to claim 4, characterized in that: The cost of thermal power units includes: Among them, N TH is the number of thermal power units, C coal is the coal price; a i , b i , c i is the coal combustion coefficient of thermal power unit (i); P i,t is the power generation of thermal power unit (i) at time t on a certain day; Δt is the scheduling span step; C T,i is the start-up and shutdown cost of the thermal power unit at time t; U i,t is the start / stop status of the thermal power unit at time t [0 for shutdown, 1 for startup]; C1 is the basic peak load regulation cost; C2=βS C,i / (2N f (P))(8) Among them, β is the loss coefficient of the thermal power unit; S C,i is the purchase cost of thermal power unit i; N f (P) is the number of rotor cracking cycles; C2 is the peak load regulation cost when no oil is added; C3=S oil ·Q oil,i (9) Among them, S oil is the current oil price; Q oil,i is the oil injection amount for unit i; The cost of thermal power generation unit is:

6. The method for optimizing the dispatching of a wind, solar, thermal and energy storage integrated energy system according to claim 4, characterized in that: The wind, solar and storage operation costs are: Among them, P WT,t , P PV,t are the power generation of wind and solar units respectively; C s , C PV , C WT are the operating cost coefficients of energy storage equipment and wind and solar power generation equipment respectively; η S is the charge and discharge efficiency; and are respectively the discharge and charge amounts of the energy storage device at time t.

7. The method for optimizing the dispatching of a wind, solar, thermal and energy storage integrated energy system according to claim 2 or 4, characterized in that: The constraints of the low-carbon dispatch model of the wind-solar-thermal-storage integrated energy system include: Power balance constraints: Among them, U S,t is the energy storage charging and discharging state at time t; P LOAD,t represents the power load demand at time t; Thermal power unit constraints in, are the maximum up and down ramp rates of thermal power unit i respectively; Energy storage system constraints in, They are the upper limit of energy storage discharge power and charging power respectively; Energy storage state of charge constraints: Among them, P s (0) is the initial amount of energy storage; is the amount of electricity stored in the energy storage system at time t; They are respectively the upper and lower limits of the energy storage system’s charge state; is the rated capacity of the energy storage; Energy storage system charging incentive constraints: E sy (t)=P WT,t +P PV,t -D(t) Where D(t) is the load demand at time t, E sy (t) is the remaining wind and solar load at time t.

8. The method for optimizing the dispatching of a wind, solar, thermal and energy storage integrated energy system according to claim 1, characterized in that: The uncertainties of the basic carbon price of wind and solar power include: And in step 3, an adjustable robust optimization parameter Γ is introduced to adjust the conservatism of the optimization results at each moment to improve the robustness of the two-stage robust optimization model; in, They are the upper and lower limits of the photovoltaic unit output respectively; is the upper and lower limits of wind turbine output; max , min They are the upper and lower limits of the real-time basic carbon price respectively; A min-max-min two-stage robust optimization model is established to address the uncertainty of renewable energy output and basic carbon price.

9. The method for optimizing the dispatching of a wind, solar, thermal and energy storage integrated energy system according to claim 8, characterized in that: The min-max-min two-stage robust optimization model is: Among them, C Z (x) is the operating cost of the outer integrated energy system; x = [U i,t ,U S,t ] T , u is the set of predicted y values; A, B, C, D, G, H, J are coefficient matrices; h, e, f, g are constant column vectors.

10. The method for optimizing the dispatch of a wind, solar, thermal and energy storage integrated energy system according to claim 9, characterized in that: The column constraint generation algorithm is used to solve the min-max-min two-stage robust optimization model, and the outer layer and inner layer problems are iteratively solved.

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