Low-carbon emission scheduling optimization method and device considering multivariate scheduling main body of power system, and terminal equipment

Abstract

The invention discloses a low-carbon emission scheduling optimization method and device considering a multi-element scheduling main body of an electric power system and terminal equipment, and relates to the field of electric power system scheduling, and the method comprises the steps: obtaining equipment parameters of a thermal power generating unit, a wind power station, a photovoltaic power station and battery energy storage, and economic operation parameters of the electric power system; taking carbon emission minimization, power fluctuation ratio minimization and operation benefit maximization as targets, and constructing a low-carbon emission scheduling optimization model; solving the low-carbon emission scheduling optimization model under the constraints of power upper and lower limit constraints, state-of-charge constraints and power balance constraints to obtain the generated power of each thermal power generating unit, each wind power plant station and each photovoltaic power station, and the charging power and the discharging power of each battery energy storage; formulating a dispatching scheme of the power system; and executing scheduling of the power system based on the scheduling scheme of the power system. By implementing the method, the problem that safety and reliability of a power grid are not considered in a low-carbon emission method in the prior art can be solved.

Description

Technical Field

[0001] The present invention relates to the field of power system dispatching, and particularly to a low-carbon emission dispatching optimization method, device and terminal device considering multiple dispatching entities in the power system. Background Art

[0002] According to statistics, the carbon generated in the power industry accounts for about 40% of the total CO2 emissions in the energy industry. Therefore, the power industry is facing severe pressure to reduce carbon emissions. Under the current "dual-carbon" background, many researchers are committed to studying ways to reduce the carbon emissions of the power grid. Since the carbon emissions of new energy power stations are small, to reduce the carbon emissions of the power grid, more power generated by new energy power stations needs to be connected to the grid. However, the connection of new energy power stations will cause fluctuations and trigger power grid safety and reliability problems. However, most of the existing methods for reducing the carbon emissions of the power grid take minimizing carbon emissions and maximizing benefits as the optimization objectives, without considering the safety and reliability problems of the power grid, which makes the operation of the power system have great potential safety hazards. Summary of the Invention

[0003] Embodiments of the present invention provide a low-carbon emission dispatching optimization method, device and terminal device considering multiple dispatching entities in the power system, which can solve the problem that the safety and reliability of the power grid are not considered in the existing low-carbon emission methods.

[0004] An embodiment of the present invention provides a low-carbon emission dispatching optimization method considering multiple dispatching entities in the power system, including:

[0005] Obtain the equipment parameters of thermal power units, wind farms, photovoltaic power stations and battery energy storage, as well as the economic operation parameters of the power system;

[0006] Based on the equipment parameters of thermal power units, wind farms, photovoltaic power stations and battery energy storage, as well as the economic operation parameters of the power system, with the objectives of minimizing carbon emissions, minimizing power volatility and maximizing operation benefits, construct a low-carbon emission dispatching optimization model;

[0007] Apply power upper and lower limit constraints, state of charge constraints and power balance constraints to the low-carbon emission dispatching optimization model. Under the constraints of power upper and lower limit constraints, state of charge constraints and power balance constraints, solve the low-carbon emission dispatching optimization model to obtain the power generation powers of thermal power units, wind farms and photovoltaic power stations, as well as the charging and discharging powers of battery energy storage;

[0008] Based on the power generation powers of thermal power units, wind farms and photovoltaic power stations, as well as the charging and discharging powers of battery energy storage, formulate a dispatching plan for the power system;

[0009] Based on the dispatching plan of the power system, execute the dispatching of the power system.

[0010] Further, the device parameters include: the total number of thermal power units, the total number of wind farms, the total number of photovoltaic power stations, the carbon emission factor per unit of electricity of thermal power units, the initial carbon quota of the power grid, the carbon emission factor per unit of electricity of battery energy storage, the charge-discharge efficiency of battery energy storage, the predicted power generation of wind farms, and the predicted power generation of photovoltaic power stations;

[0011] The economic operation parameters include: the average electricity selling price, the average electricity purchasing price, the power grid load, the unit curtailment cost of wind farms, the unit curtailment cost of photovoltaic power stations, the operating cost correlation coefficient of thermal power units, the hot start cost of thermal power units, and the cost of thermal power units exceeding the hot start part.

[0012] Further, the carbon emission minimization objective function includes:

[0013]

[0014] Among them, when the i-th battery energy storage discharges, F EV,i (t) = -P EV,i (t) * M EV ;

[0015] When the i-th battery energy storage charges, F EV,i (t) = P EV,i (t) * M EV ;

[0016] In the formula, minF1 is the minimum carbon emission, T is the scheduling period, G is the total number of thermal power units, P g,i (t) is the power generation of the i-th thermal power unit in the power grid at time t, e g is the carbon emission factor per unit of electricity of thermal power, E0 is the initial carbon quota of the power grid, I is the total number of battery energy storage, F EV,i (t) is the carbon emission of the i-th battery energy storage charge and discharge in the power grid at time t, P EV,i (t) is the charge and discharge power of the i-th battery energy storage in the power grid at time t, M EV is the carbon emission factor per unit of electricity of battery energy storage.

[0017] Further, the power fluctuation minimization objective function includes:

[0018]

[0019] Among them:

[0020]

[0021] P cha,i (t) = P EV,i (t) * η cha,i;

[0022] P dis,i (t) = P EV,i (t) * η dis,i ;

[0023] Where, minF2 is the minimum value of power volatility, P total (t) is the total power of the power grid at time t, P total (t - 1) is the total power of the power grid at time t - 1, Nw is the total number of wind farms, P w,i (t) is the power generation of the ith wind farm in the power grid at time t, Np is the total number of photovoltaic power stations, P pv,i (t) is the power generation of the ith photovoltaic power station in the power grid at time t, ε cha,i is a 0, 1 variable representing the charging state of the ith battery energy storage, P cha,i (t) is the charging power of the ith battery energy storage in the power grid at time t, ε dis,i is a 0, 1 variable representing the discharging state of the ith battery energy storage, P dis,i (t) is the discharging power of the ith battery energy storage in the power grid at time t, η cha,i is the charging efficiency of the ith battery energy storage, η dis,i is the discharging efficiency of the ith battery energy storage.

[0024] Furthermore, the objective function of maximizing operation benefits includes:

[0025]

[0026] Among them, S(t) = (C s (t) - C g (t)) * P load (t);

[0027]

[0028]

[0029] Where, minF3 is the maximum value of operation benefits, S(t) is the electricity selling revenue of the power grid at time t, C(t) is the power generation cost of the power grid at time t, C s (t) is the average electricity selling price of the power grid at time t, C g (t) is the average electricity purchasing price of the power grid at time t, P load (t) is the load of the power grid at time t, C w,i (t) is the curtailment cost of the ith wind farm in the power grid at time t, C pv,i (t) is the curtailment cost of the ith photovoltaic power station in the power grid at time t, The power generation cost of the \(i\)-th thermal power unit in the power grid at time \(t\). The start-up cost of the \(i\)-th thermal power unit in the power grid at time \(t\), \(\delta\) W The unit cost of curtailed wind power. The predicted power generation of the \(i\)-th wind farm in the power grid at time \(t\), \(\delta\) PV The unit cost of curtailed photovoltaic power. The predicted power generation of the \(i\)-th photovoltaic power station in the power grid at time \(t\), \(a\) i , \(b\) i and \(c\) i Are the correlation coefficients of the operating cost of the thermal power unit. The hot start-up cost of the \(i\)-th thermal power unit in the power grid at time \(t\), \(v\) i,t The start-up status of the \(i\)-th thermal power unit in the power grid at time \(t\). The unit start-up status is 1, otherwise it is 0. The cost of the part of the \(i\)-th thermal power unit in the power grid exceeding the hot start-up at time \(t\).

[0030] Furthermore, the power upper and lower limit constraints include:

[0031]

[0032] In the formula, Is the lower limit value of the power generation of the \(i\)-th thermal power unit. Is the upper limit value of the power generation of the \(i\)-th thermal power unit. Is the lower limit value of the power generation of the \(i\)-th wind farm. Is the upper limit value of the power generation of the \(i\)-th wind farm. Is the lower limit value of the power generation of the \(i\)-th photovoltaic power station. Is the upper limit value of the power generation of the \(i\)-th photovoltaic power station. Is the upper limit value of the charging power of the \(i\)-th battery energy storage. Is the upper limit value of the discharging power of the \(i\)-th battery energy storage.

[0033] Furthermore, the state of charge constraints include:

[0034]

[0035] Among them,

[0036] \(\varepsilon\) cha,i +\(\varepsilon\) dis,i \(\leq1\);

[0037] In the formula, Is the minimum value of the state of charge of the battery of the battery energy storage, SOC i \((t)\) is the state of charge of the battery of the \(i\)-th battery energy storage in the power grid at time \(t\). The maximum value of the state of charge of the battery for battery energy storage, SOC i (t - 1) is the state of charge of the i-th battery energy storage in the power grid at time t - 1, Δt is the time step, and E EV,i is the total battery capacity of the i-th battery energy storage.

[0038] Furthermore, the power balance constraint includes:

[0039] P g (t) + P w (t) + P pv (t) + P EV (t) - P loss (t) - P load (t) = 0;

[0040] Among them,

[0041]

[0042] In the formula, P g (t) is the power generation power of all thermal power units at time t, P w (t) is the power generation power of all wind farms at time t, P pv (t) is the power generation power of all photovoltaic power stations at time t, P EV (t) is the charge and discharge power of all battery energy storages at time t, P loss (t) is the power loss of the power grid at time t, P load (t) is the load power of the power grid at time t.

[0043] Another embodiment of the present invention provides a low-carbon emission dispatch optimization device considering multiple dispatch entities in the power system, including: a data acquisition module, an optimization model construction module, an optimization model solution module, a dispatch plan formulation module, and an execution dispatch plan module;

[0044] The data acquisition module is used to acquire the equipment parameters of thermal power units, wind farms, photovoltaic power stations, and battery energy storages, as well as the economic operation parameters of the power system;

[0045] The optimization model construction module is used to construct a low-carbon emission dispatch optimization model based on the equipment parameters of thermal power units, wind farms, photovoltaic power stations, and battery energy storages, as well as the economic operation parameters of the power system, with the goals of minimizing carbon emissions, minimizing power volatility, and maximizing operation benefits;

[0046] The optimization model solving module is used to impose power upper and lower limit constraints, state of charge constraints, and power balance constraints on the low-carbon emission scheduling optimization model, and solve the low-carbon emission scheduling optimization model under the constraints of the power upper and lower limit constraints, state of charge constraints, and power balance constraints, so as to obtain the power generation powers of each thermal power unit, wind farm, and photovoltaic power station, as well as the charging power and discharging power of each battery energy storage;

[0047] The scheduling plan formulating module is used to formulate a scheduling plan for the power system based on the power generation powers of each thermal power unit, wind farm, and photovoltaic power station, as well as the charging power and discharging power of each battery energy storage;

[0048] The scheduling plan execution module is used to execute the scheduling of the power system based on the scheduling plan of the power system.

[0049] Another embodiment of the present invention provides a terminal device, including a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor. When the processor executes the computer program, it implements a low-carbon emission scheduling optimization method for considering multiple scheduling entities in a power system as described in any one of the above embodiments.

[0050] By implementing the present invention, the following beneficial effects are achieved:

[0051] The present invention discloses a low-carbon emission scheduling optimization method, device and terminal device considering multiple scheduling entities in a power system. The method includes obtaining the device parameters of thermal power units, wind farms, photovoltaic power stations and battery energy storage, as well as the economic operation parameters of the power system; based on the device parameters of thermal power units, wind farms, photovoltaic power stations and battery energy storage, and the economic operation parameters of the power system, constructing a low-carbon emission scheduling optimization model with the objectives of minimizing carbon emissions, minimizing power volatility and maximizing operation benefits; imposing power upper and lower limit constraints, state of charge constraints and power balance constraints on the low-carbon emission scheduling optimization model, and solving the low-carbon emission scheduling optimization model under the constraints of power upper and lower limit constraints, state of charge constraints and power balance constraints to obtain the power generation powers of each thermal power unit, wind farm and photovoltaic power station, as well as the charging power and discharging power of each battery energy storage; formulating a scheduling plan for the power system based on the power generation powers of each thermal power unit, wind farm and photovoltaic power station, and the charging power and discharging power of each battery energy storage; and executing the scheduling of the power system based on the scheduling plan of the power system. By constructing a low-carbon emission scheduling optimization model with the objectives of minimizing carbon emissions, minimizing power volatility and maximizing operation benefits, and solving the model through constraint conditions to obtain the scheduling plan of the power system, the low-carbon emission scheduling optimization model takes into account the power volatility during the operation of the power system, obtains the scheduling plan under the condition of minimizing power volatility, ensures the safety and reliability of the operation of the power system, and reduces potential safety hazards. Description of the Drawings

[0052] Figure 1 FIG. is a schematic flow chart of a low-carbon emission scheduling optimization method considering multiple scheduling entities in a power system provided by an embodiment of the present invention.

[0053] Figure 2 FIG. is a schematic structural diagram of a low-carbon emission scheduling optimization method considering multiple scheduling entities in a power system provided by an embodiment of the present invention. Detailed Embodiments

[0054] To make the objectives, technical solutions and advantages of the present application clearer, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present application. Apparently, the described embodiments are some, but not all, of the embodiments of the present application. All other embodiments obtained by those of ordinary skill in the art without creative efforts based on the embodiments of the present application belong to the scope of protection of the present application.

[0055] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those of ordinary skill in the technical field to which this application belongs; the terms used herein are for the purpose of describing specific embodiments only and are not intended to limit this application; the terms "including" and "having" and any variations thereof in the specification and claims of this application and the above drawings are intended to cover non-exclusive inclusion.

[0056] In the description of the embodiments of this application, technical terms such as "first" and "second" are only used to distinguish different objects and cannot be understood as indicating or implying relative importance or implicitly specifying the quantity, specific order, or primary-secondary relationship of the indicated technical features. In the description of the embodiments of this application, "a plurality" means more than two unless otherwise specifically defined.

[0057] As Figure 1 shown, to solve the problem that the safety and reliability of the power grid are not considered in the low-carbon emission methods of the existing technology, an embodiment of the present invention provides a low-carbon emission dispatch optimization method considering multiple dispatch entities in the power system, including the following steps:

[0058] Step S1, obtain the equipment parameters of thermal power units, wind farms, photovoltaic power stations, and battery energy storage, as well as the economic operation parameters of the power system;

[0059] In the present invention, obtaining the equipment parameters of thermal power units, wind farms, photovoltaic power stations, and battery energy storage, as well as the economic operation parameters of the power system during the dispatch period, provides a data basis for constructing a low-carbon emission dispatch optimization model in the subsequent steps.

[0060] In a preferred embodiment, the equipment parameters include: the total number of thermal power units, the total number of wind farms, the total number of photovoltaic power stations, the carbon emission factor per unit of electricity of thermal power units, the initial carbon quota of the power grid, the carbon emission factor per unit of electricity of battery energy storage, the charge-discharge efficiency of battery energy storage, the predicted power generation of wind farms, and the predicted power generation of photovoltaic power stations;

[0061] The economic operation parameters include: the average electricity selling price, the average electricity purchasing price, the power grid load, the unit curtailment cost of wind farms, the unit curtailment cost of photovoltaic power stations, the operation cost correlation coefficient of thermal power units, the hot start cost of thermal power units, and the cost of thermal power units exceeding the hot start part.

[0062] Step S2, based on the equipment parameters of thermal power units, wind farms, photovoltaic power stations, and battery energy storage, as well as the economic operation parameters of the power system, construct a low-carbon emission dispatch optimization model with the objectives of minimizing carbon emissions, minimizing power volatility, and maximizing operation benefits;

[0063] In a preferred embodiment, the carbon emission minimization objective function includes:

[0064]

[0065] Among them, when the i-th battery energy storage discharges, F EV,i (t) = -P EV,i (t) * M EV ;

[0066] When the i-th battery energy storage charges, F EV,i (t) = P EV,i (t) * M EV ;

[0067] In the formula, minF1 is the minimum carbon emission, T is the scheduling period, G is the total number of thermal power units, P g,i (t) is the power generation power of the i-th thermal power unit in the power grid at time t, e g is the carbon emission factor per unit of thermal power, E0 is the initial carbon quota of the power grid, I is the total number of battery energy storages, F EV,i (t) is the carbon emission of the i-th battery energy storage charging and discharging in the power grid at time t, P EV,i (t) is the charging and discharging power of the i-th battery energy storage in the power grid at time t, M EV is the carbon emission factor per unit of battery energy storage.

[0068] Specifically, the wind farm and the photovoltaic power station belong to new energy power stations, and the carbon emissions of the new energy power stations are regarded as zero. Therefore, in the carbon emission minimization objective function, only the carbon emissions of the thermal power units, the initial carbon quota of the power grid, and the carbon emissions of the battery energy storage charging and discharging need to be considered. Based on the carbon emissions of each thermal power unit, the initial carbon quota of the power grid, and the carbon emissions of each battery energy storage charging and discharging, the carbon emission minimization objective function is constructed.

[0069] In a preferred embodiment, the power fluctuation rate minimization objective function includes:

[0070]

[0071] Among them:

[0072]

[0073] P cha,i (t) = P EV,i (t) * η cha,i ;

[0074] P dis,i (t) = P EV,i (t) * η dis,i ;

[0075] In the formula, minF2 is the minimum value of the power fluctuation rate, and P total (t) is the total power of the power grid at time t, and P total (t - 1) is the total power of the power grid at time t - 1. Nw is the total number of wind farm stations, and P w,i (t) is the power generation of the ith wind farm station in the power grid at time t. Np is the total number of photovoltaic power generation stations, and P pv,i (t) is the power generation of the ith photovoltaic power generation station in the power grid at time t. ε cha,i is a 0,1 variable representing the charging state of the ith battery energy storage. P cha,i (t) is the charging power of the ith battery energy storage in the power grid at time t. ε dis,i is a 0,1 variable representing the discharging state of the ith battery energy storage. P dis,i (t) is the discharging power of the ith battery energy storage in the power grid at time t. η cha,i is the charging efficiency of the ith battery energy storage, and η dis,i is the discharging efficiency of the ith battery energy storage.

[0076] Specifically, by calculating the power generation of all thermal power units, wind farm stations, and photovoltaic power generation stations at each moment of the power grid operation, as well as the charging power and discharging power of all battery energy storages at each moment of the power grid operation, the total power of the power grid at each moment of the power grid operation is obtained, so as to calculate the power fluctuation rate of the power grid operation within the scheduling period and construct an objective function for minimizing the power fluctuation rate.

[0077] Schematically, when the ith battery energy storage in the power grid is in the charging state, ε cha,i is 1, and ε dis,i is 0; when the ith battery energy storage in the power grid is in the discharging state, ε cha,i is 0, and ε dis,i is 1.

[0078] In a preferred embodiment, the objective function for maximizing the operation benefit includes:

[0079]

[0080] Among them, S(t) = (C s (t) - C g (t)) * P load (t);

[0081]

[0082]

[0083] Where, minF3 is the maximum operation benefit, S(t) is the power sales revenue of the power grid at time t, C(t) is the power generation cost of the power grid at time t, C s (t) is the average power sales price of the power grid at time t, C g (t) is the average power purchase price of the power grid at time t, P load (t) is the load of the power grid at time t, C w,i (t) is the curtailment cost of the i-th wind farm in the power grid at time t, C pv,i (t) is the curtailment cost of the i-th photovoltaic power station in the power grid at time t, is the power generation cost of the i-th thermal power unit in the power grid at time t, is the start-up cost of the i-th thermal power unit in the power grid at time t, δ W is the unit curtailment cost of wind, is the predicted power generation of the i-th wind farm in the power grid at time t, δ PV unit curtailment cost of light, is the predicted power generation of the i-th photovoltaic power station in the power grid at time t, a i 、b i and c i are the correlation coefficients of the operation cost of the thermal power unit, is the hot start-up cost of the i-th thermal power unit in the power grid at time t, v i,t is the start-up state of the i-th thermal power unit in the power grid at time t. The start-up state of the unit is 1, otherwise it is 0, is the cost of the part exceeding the hot start-up of the i-th thermal power unit in the power grid at time t.

[0084] Specifically, based on the obtained average power sales price, average power purchase price, and power grid load, calculate the power sales revenue of the power grid at each moment; based on the obtained unit curtailment cost of the wind farm, unit curtailment cost of the photovoltaic power station, correlation coefficient of the operation cost of the thermal power unit, hot start-up cost of the thermal power unit, and cost of the part exceeding the hot start-up of the thermal power unit, calculate the costs of each thermal power unit, wind farm, and photovoltaic power station, so as to obtain the power generation cost of the power grid at each moment; according to the power sales revenue of the power grid at each moment and the power generation cost of the power grid at each moment, construct an objective function for maximizing the operation benefit.

[0085] In the present invention, based on the obtained equipment parameters of the thermal power unit, wind farm, photovoltaic power station, and battery energy storage, as well as the economic operation parameters of the power system, with the goals of minimizing carbon emissions, minimizing power volatility, and maximizing operation benefit, a low-carbon emission scheduling optimization model is constructed, comprehensively considering the carbon emissions of the power grid operation, the safety of the power grid operation, and the economic benefits of the power grid operation, comprehensively improving the low-carbon, safety, and economy of the power system.

[0086] Step S3: Apply the power upper and lower limit constraints, state of charge constraints, and power balance constraints to the low-carbon emission dispatch optimization model. Under the constraints of the power upper and lower limit constraints, state of charge constraints, and power balance constraints, solve the low-carbon emission dispatch optimization model to obtain the power generation powers of each thermal power unit, wind farm, and photovoltaic power station, as well as the charging power and discharging power of each battery energy storage;

[0087] In a preferred embodiment, the power upper and lower limit constraints include:

[0088]

[0089] In the formula, is the lower limit value of the power generation power of the i-th thermal power unit, is the upper limit value of the power generation power of the i-th thermal power unit, is the lower limit value of the power generation power of the i-th wind farm, is the upper limit value of the power generation power of the i-th wind farm, is the lower limit value of the power generation power of the i-th photovoltaic power station, is the upper limit value of the power generation power of the i-th photovoltaic power station, is the upper limit value of the charging power of the i-th battery energy storage, is the upper limit value of the discharging power of the i-th battery energy storage.

[0090] Specifically, to ensure the safe operation of the power grid, the power generation powers of thermal power units, wind farms, photovoltaic power stations, as well as the charging power and discharging power of battery energy storage, shall not exceed the corresponding power upper and lower limit values. Therefore, apply the power upper and lower limit constraint conditions to the low-carbon emission dispatch optimization model.

[0091] In a preferred embodiment, the state of charge constraints include:

[0092]

[0093] Among them,

[0094] ε cha,i +ε dis,i ≤1;

[0095] In the formula, is the minimum value of the state of charge of the battery energy storage, SOC i (t) is the state of charge of the i-th battery energy storage in the power grid at time t, is the maximum value of the state of charge of the battery energy storage, SOC i (t - 1) is the state of charge of the i-th battery energy storage in the power grid at time t - 1, Δt is the time step, E EV,iis the total battery capacity of the i-th battery energy storage.

[0096] Specifically, to ensure the safe operation of the battery energy storage, it is necessary to ensure that the state of charge of the battery energy storage does not exceed the minimum and maximum values of the state of charge of the battery energy storage. Therefore, a state of charge constraint condition is imposed on the low-carbon emission scheduling optimization model.

[0097] In a preferred embodiment, the power balance constraint includes:

[0098] P g (t) + P w (t) + P pv (t) + P EV (t) - P loss (t) - P load (t) = 0;

[0099] Wherein,

[0100]

[0101] In the formula, P g (t) is the power generation power of all thermal power plants at time t, P w (t) is the power generation power of all wind farms at time t, P pv (t) is the power generation power of all photovoltaic power stations at time t, P EV (t) is the charge and discharge power of all battery energy storages at time t, P loss (t) is the power loss of the power grid at time t, P load (t) is the load power of the power grid at time t.

[0102] Specifically, during the operation of the power grid, power balance is satisfied. The sum of the power generation powers of all thermal power plants, wind farms, and photovoltaic power stations, and the sum of the charge and discharge powers of all battery energy storages are equal to the sum of the power loss and load power of the power grid. Therefore, a power balance constraint condition is imposed on the low-carbon emission scheduling optimization model.

[0103] Schematically, an improved moth-flame optimization algorithm based on R domination or multi-objective particle swarm optimization can be used to solve the low-carbon emission scheduling optimization model.

[0104] In the present invention, by imposing power upper and lower limit constraints, state of charge constraints, and power balance constraints on the low-carbon emission dispatch optimization model and solving it, it can ensure that each thermal power unit, wind farm, photovoltaic power station, and battery energy storage device operate within a safe and reasonable range, prevent equipment from being damaged due to overload, low load, or overcharging and discharging, ensure the stable operation of the power system. At the same time, based on these constraints, the generated power, charging and discharging power of each device are obtained by solving, and optimization goals such as minimizing carbon emissions, minimizing power volatility, and maximizing operating benefits can be achieved.

[0105] Step S4: Based on the generated power of each thermal power unit, wind farm, and photovoltaic power station, as well as the charging power and discharging power of each battery energy storage, formulate a dispatch plan for the power system;

[0106] In the present invention, based on the generated power of each thermal power unit, wind farm, and photovoltaic power station obtained by solving in the above steps, as well as the charging power and discharging power of each battery energy storage, the generated power of each thermal power unit, wind farm, and photovoltaic power station, as well as the charging power and discharging power of each battery energy storage are used as a dispatch combination to formulate a dispatch plan for the power system. The dispatch plan can achieve minimizing carbon emissions, minimizing power volatility, and maximizing operating benefits.

[0107] Step S5: Based on the dispatch plan of the power system, execute the dispatch of the power system.

[0108] In the present invention, based on the formulated dispatch plan of the power system, according to the generated power of each thermal power unit, wind farm, and photovoltaic power station in the dispatch plan, as well as the charging power and discharging power of each battery energy storage, corresponding adjustments are made to each device of the power system to execute the dispatch of the power system. After the adjustment, the carbon emissions of the power grid can be reduced, the safe and stable operation of the power grid can be maintained, and at the same time, the revenue of the power grid can be increased.

[0109] Based on the above method item embodiments, the present invention correspondingly provides device item embodiments;

[0110] As Figure 2 shown, it is a schematic structural diagram of a low-carbon emission dispatch optimization device considering multiple dispatch entities in a power system provided by an embodiment of the present invention, including:

[0111] A data acquisition module, an optimization model construction module, an optimization model solving module, a dispatch plan formulation module, and an execution of dispatch plan module;

[0112] The data acquisition module is used to acquire the device parameters of thermal power units, wind farms, photovoltaic power stations, and battery energy storage, as well as the economic operation parameters of the power system;

[0113] The optimization model construction module is used to construct a low-carbon emission dispatch optimization model based on the equipment parameters of thermal power units, wind farms, photovoltaic power stations, and battery energy storage, as well as the economic operation parameters of the power system, with the goals of minimizing carbon emissions, minimizing power volatility, and maximizing operation benefits;

[0114] The optimization model solving module is used to impose power upper and lower limit constraints, state of charge constraints, and power balance constraints on the low-carbon emission dispatch optimization model, and solve the low-carbon emission dispatch optimization model under the constraints of power upper and lower limit constraints, state of charge constraints, and power balance constraints to obtain the power generation powers of each thermal power unit, wind farm, and photovoltaic power station, as well as the charging power and discharging power of each battery energy storage;

[0115] The dispatch plan formulation module is used to formulate a dispatch plan for the power system based on the power generation powers of each thermal power unit, wind farm, and photovoltaic power station, as well as the charging power and discharging power of each battery energy storage;

[0116] The dispatch plan execution module is used to execute the dispatch of the power system based on the dispatch plan of the power system.

[0117] It can be understood that the above device item embodiments correspond to the method item embodiments of the present invention, and can implement a low-carbon emission dispatch optimization method for a power system considering multiple dispatch entities provided by any one of the above method item embodiments of the present invention.

[0118] It should be noted that the device embodiments described above are only illustrative. The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed to multiple network units. Some or all of the modules can be selected according to actual needs to achieve the purpose of the solution of this embodiment. In addition, in the attached drawings of the device embodiments provided by the present invention, the connection relationship between the modules indicates that they have a communication connection, which can be specifically implemented as one or more communication buses or signal lines. Those of ordinary skill in the art can understand and implement without creative efforts.

[0119] Those skilled in the art can clearly understand that for the sake of convenience and brevity, the specific working process of the device described above can refer to the corresponding process in the foregoing method embodiment and will not be repeated here.

[0120] Another preferred embodiment of the present invention provides a terminal device, including a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor. When the processor executes the computer program, it implements a low-carbon emission scheduling optimization method considering multiple scheduling entities in the power system as described in any one of the above embodiments.

[0121] It should be noted that the terminal device mentioned here can be a computing device such as a desktop computer, a notebook, a palm computer, and a cloud server. The terminal device may include, but is not limited to, a processor and a memory. Those skilled in the art can understand that, for example, it may also include input / output devices, network access devices, buses, etc.

[0122] The so-called processor may be a Central Processing Unit (CPU), or may also be other general-purpose processors, Digital Signal Processors (DSPs), Application Specific Integrated Circuits (ASICs), Field-Programmable Gate Arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor or the processor may also be any conventional processor, etc. The processor is the control center of the terminal device, connecting various parts of the entire terminal device through various interfaces and lines.

[0123] The memory can be used to store the computer program. The processor realizes various functions of the terminal device by running or executing the computer program stored in the memory and calling the data stored in the memory. The memory may mainly include a program storage area and a data storage area. Among them, the program storage area can store an operating system, application programs required for at least one function, etc.; the data storage area can store data created according to the use of the mobile phone, etc. In addition, the memory may include high-speed random access memory, and may also include non-volatile memory, such as a hard disk, a memory, a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital (SD) card, a Flash Card, at least one magnetic disk storage device, a flash memory device, or other volatile solid-state storage devices.

[0124] The above are the preferred embodiments of the present invention. It should be noted that for those of ordinary skill in the art, without departing from the principle of the present invention, several improvements and modifications can be made, and these improvements and modifications are also regarded as the protection scope of the present invention.

Claims

1. A low-carbon emission dispatch optimization method considering multiple dispatch entities in the power system, characterized in that Including: Obtain the equipment parameters of thermal power units, wind farms, photovoltaic power stations, and battery energy storage, as well as the economic operation parameters of the power system; Based on the equipment parameters of thermal power units, wind farms, photovoltaic power stations, and battery energy storage, as well as the economic operation parameters of the power system, with the goals of minimizing carbon emissions, minimizing power volatility, and maximizing operation benefits, construct a low-carbon emission scheduling optimization model; Apply power upper and lower limit constraints, state of charge constraints, and power balance constraints to the low-carbon emission scheduling optimization model. Under the constraints of power upper and lower limit constraints, state of charge constraints, and power balance constraints, solve the low-carbon emission scheduling optimization model to obtain the power generation powers of each thermal power unit, wind farm, and photovoltaic power station, as well as the charging power and discharging power of each battery energy storage; Based on the power generation powers of each thermal power unit, wind farm, and photovoltaic power station, as well as the charging power and discharging power of each battery energy storage, formulate a scheduling plan for the power system; Based on the scheduling plan of the power system, execute the scheduling of the power system.

2. The low-carbon emission dispatch optimization method considering multiple dispatch entities in the power system according to claim 1, characterized in that, The equipment parameters include: the total number of thermal power units, the total number of wind farms, the total number of photovoltaic power stations, the carbon emission factor per unit of electricity of thermal power units, the initial carbon quota of the power grid, the carbon emission factor per unit of electricity of battery energy storage, the charge and discharge efficiency of battery energy storage, the predicted power generation of wind farms, and the predicted power generation of photovoltaic power stations; The economic operation parameters include: the average selling price of electricity, the average purchase price of electricity, the power grid load, the unit abandoned wind cost of wind farms, the unit abandoned light cost of photovoltaic power stations, the operation cost correlation coefficient of thermal power units, the hot start cost of thermal power units, and the cost of thermal power units exceeding the hot start part.

3. The low-carbon emission dispatch optimization method considering multiple dispatch entities in a power system according to claim 2, wherein The objective function of minimizing carbon emissions includes: where, when the i-th battery energy storage discharges, F EV,i (t) = -P EV,i (t) * M EV ; When the i-th battery energy storage is charging, F EV,i (t) = P EV,i (t) * M EV ; Wherein, minF1 is the minimum carbon emission, T is the scheduling period, G is the total number of thermal power units, P g,i (t) is the power generation of the i-th thermal power unit in the power grid at time t, e g is the carbon emission factor per unit power of thermal power, E0 is the initial carbon quota of the power grid, I is the total number of battery energy storages, F EV,i (t) is the carbon emission of the i-th battery energy storage charging and discharging in the power grid at time t, P EV,i (t) is the charging and discharging power of the i-th battery energy storage in the power grid at time t, M EV is the carbon emission factor per unit power of battery energy storage.

4. The low-carbon emission dispatch optimization method considering multiple dispatch entities in a power system according to claim 3, characterized in that, The objective function of minimizing power volatility includes: Where: P cha,i P(t) = EV,i P(t) * η cha,i ; P dis,i P(t) = EV,i P(t) * η dis,i ; Where, minF2 is the minimum value of power fluctuation rate, and P total (t) is the total power of the power grid at time t, and P total (t - 1) is the total power of the power grid at time t - 1, Nw is the total number of wind farms, and P w,i (t) is the power generation of the i-th wind farm in the power grid at time t, Np is the total number of photovoltaic power stations, and P pv,i (t) is the power generation of the i-th photovoltaic power station in the power grid at time t, and ε cha,i is a 0, 1 variable representing the charging state of the i-th battery energy storage, and P cha,i (t) is the charging power of the i-th battery energy storage in the power grid at time t, and ε dis,i is a 0, 1 variable representing the discharging state of the i-th battery energy storage, and P dis,i (t) is the discharging power of the i-th battery energy storage in the power grid at time t, and η cha,i is the charging efficiency of the i-th battery energy storage, and η dis,i is the discharging efficiency of the i-th battery energy storage.

5. The low-carbon emission dispatch optimization method considering multiple dispatch entities in a power system according to claim 4, characterized in that, The objective function of maximizing operation benefits includes: where S(t) = (C s (t) - C g (t)) * P load (t); Where, minF3 is the maximum operation benefit, S(t) is the power sales revenue of the power grid at time t, C(t) is the power generation cost of the power grid at time t, C s (t) is the average power sales price of the power grid at time t, C g (t) is the average power purchase price of the power grid at time t, P load (t) is the load of the power grid at time t, C w,i (t) is the curtailment cost of the i-th wind farm in the power grid at time t, C pv,i (t) is the curtailment cost of the i-th photovoltaic power station in the power grid at time t, is the power generation cost of the i-th thermal power unit in the power grid at time t, is the start-up cost of the i-th thermal power unit in the power grid at time t, ε W is the unit curtailment cost of wind, is the predicted power generation of the i-th wind farm in the power grid at time t, δ PV unit curtailment cost of light, is the predicted power generation of the i-th photovoltaic power station in the power grid at time t, a i 、b i and c i are the correlation coefficients of the operation cost of the thermal power unit, is the hot start-up cost of the i-th thermal power unit in the power grid at time t, v i,t is the start-up status of the i-th thermal power unit in the power grid at time t. The start-up status of the unit is 1, otherwise it is 0, is the cost of the part exceeding the hot start-up of the i-th thermal power unit in the power grid at time t.

6. The low-carbon emission dispatch optimization method for taking into account multiple dispatch entities in a power system as claimed in claim 5, wherein The power upper and lower limit constraints include: Wherein, is the lower limit value of the power generation of the i-th thermal power unit, is the upper limit value of the power generation of the i-th thermal power unit, is the lower limit value of the power generation of the i-th wind farm, is the upper limit value of the power generation of the i-th wind farm, is the lower limit value of the power generation of the i-th photovoltaic power station, is the upper limit value of the power generation of the i-th photovoltaic power station, is the upper limit value of the charging power of the i-th battery energy storage, is the upper limit value of the discharging power of the i-th battery energy storage.

7. The low-carbon emission dispatching optimization method considering multiple dispatching entities of a power system according to claim 6, characterized in that, The state of charge constraints include: Among them, ε cha,i +ε dis,i ≤ 1; Wherein, is the minimum value of the state of charge of the battery energy storage, SOC i (t) is the state of charge of the i-th battery energy storage in the power grid at time t, is the maximum value of the state of charge of the battery energy storage, SOC i (t - 1) is the state of charge of the i-th battery energy storage in the power grid at time t - 1, and Δt is the time step, and E EV,i is the total battery capacity of the i-th battery energy storage.

8. The low-carbon emission dispatch optimization method considering multiple dispatch entities in a power system according to claim 7, wherein The power balance constraints include: P g (t) + P w (t) + P pv (t) + P EV (t) - P loss (t) - P load (t) = 0; Among them, where P g (t) is the power generation of all thermal power units at time t, P w (t) is the power generation of all wind farms at time t, P pv (t) is the power generation of all photovoltaic power stations at time t, P EV (t) is the charge and discharge power of all battery energy storages at time t, P loss (t) is the power loss of the power grid at time t, P load (t) is the load power of the power grid at time t.

9. A low-carbon emission dispatch optimization device considering multiple dispatch entities in a power system, characterized in that, Including: A data acquisition module, an optimization model construction module, an optimization model solution module, a scheduling plan formulation module, and an execution scheduling plan module; The data acquisition module is used to obtain the equipment parameters of thermal power units, wind farms, photovoltaic power stations, and battery energy storage, as well as the economic operation parameters of the power system; The optimization model construction module is used to construct a low-carbon emission scheduling optimization model based on the equipment parameters of thermal power units, wind farms, photovoltaic power stations, and battery energy storage, as well as the economic operation parameters of the power system, with the goals of minimizing carbon emissions, minimizing power volatility, and maximizing operation benefits; The optimization model solution module is used to apply power upper and lower limit constraints, state of charge constraints, and power balance constraints to the low-carbon emission scheduling optimization model. Under the constraints of power upper and lower limit constraints, state of charge constraints, and power balance constraints, solve the low-carbon emission scheduling optimization model to obtain the power generation powers of each thermal power unit, wind farm, and photovoltaic power station, as well as the charging power and discharging power of each battery energy storage; The scheduling plan formulation module is used to formulate a scheduling plan for the power system based on the power generation powers of each thermal power unit, wind farm, and photovoltaic power generation station, as well as the charging power and discharging power of each battery energy storage. The scheduling plan execution module is used to execute the scheduling of the power system based on the scheduling plan of the power system.

10. A terminal device, characterized in that, It includes a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor. When the processor executes the computer program, it implements a low-carbon emission scheduling optimization method for a power system considering multiple scheduling entities as described in any one of claims 1 to 8.

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