A three-mode and three-objective integrated energy system optimization scheduling method

Abstract

The present invention discloses a three-mode, three-objective integrated energy system optimization and scheduling method. The method is based on an integrated energy system of wind, solar, thermal power units and energy storage modules. The method first judges the start-up and shutdown operation of the wind, solar and thermal power units respectively, establishes a wind power generation model and a photovoltaic power generation model, limits different output modes, and then constructs a multi-objective optimization scheduling model based on the wind power generation model and the photovoltaic power generation model. The method performs three-objective optimization scheduling based on the output tracking mode, the stable operation mode and the frequency modulation mode. Finally, the NSGA-II algorithm is used to solve the multi-objective optimization scheduling model to obtain the Pareto optimal frontier solution set. The optimal scheduling scheme of the integrated energy system is obtained by the AHP method. The present invention reduces the impact of the large-scale integration of wind and solar power generation into the power grid by establishing a three-objective optimization scheduling model, and reduces the impact and economic impact of the uncertainty of new energy on the integrated energy system according to scheduling requirements.

Description

Technical Field

[0001] The present invention belongs to the technical field of integrated energy system optimization, and specifically relates to a three-mode three-objective integrated energy system optimization scheduling method. Background Art

[0002] In recent years, global energy problems have become increasingly serious. In order to alleviate the crisis brought about by the depletion of fossil fuels to global development and reduce the pollution caused by the combustion of fossil fuels, my country's power industry is developing rapidly and promoting structural transformation. The development of an integrated energy system has become particularly important.

[0003] Due to the complexity and diversity of integrated energy systems, their energy complementarity and synergy are also very complex. Therefore, the optimal scheduling of such integrated energy systems has become an important research direction in academia and industry.

[0004] Wind power generation and photovoltaic power generation have strong uncertainty and are subject to extreme weather conditions. Existing optimization scheduling models rarely consider the impact of extreme weather on the output of integrated energy systems.

[0005] Current research has rarely established a reasonable and practical multi-objective dispatch model. Furthermore, most dispatch solutions only address one type of problem, lack flexibility, and cannot provide optimized dispatch solutions based on diverse needs. Therefore, there is an urgent need to establish a dispatch model that can cope with extreme weather conditions, accommodate diverse dispatch needs, reduce grid operating costs, and improve the ability to accommodate renewable energy. Summary of the Invention

[0006] In response to the above-mentioned deficiencies in the existing technology, the patent of this invention provides a more targeted three-mode and three-objective integrated energy system optimization scheduling model for wind power generation, photovoltaic power generation, and thermal power generation, so as to reduce the impact of new energy uncertainty on the integrated energy system and select the mode according to the superior scheduling requirements.

[0007] The technical solution adopted by the present invention to solve the technical problem is: a three-mode three-objective integrated energy system optimization scheduling method, based on an integrated energy system including a wind turbine generator set, a photovoltaic generator set, a thermal power generator set and a lead-acid battery energy storage module, comprising the following steps:

[0008] S1, based on the load day-ahead forecast data, determines whether the wind power generation output and photovoltaic power generation output forecasts fall under extreme weather conditions, and then determines the operation status: the wind power generation operation status is determined based on the wind power output day-ahead forecast data, the photovoltaic power generation operation status is determined based on the photovoltaic output day-ahead forecast data, and the thermal power unit start-up and shutdown operation status is determined based on the thermal power unit power generation day-ahead forecast data;

[0009] S2, determine the output composition: determine the output composition of the integrated energy system based on the predicted values ​​of wind power, photovoltaic power generation, thermal power generation units and load, establish a wind power generation model and a photovoltaic power generation model respectively, select the combined output of wind power generation, photovoltaic power generation, thermal power generation units and battery energy storage, the combined output of photovoltaic power generation, thermal power generation units and battery energy storage, or the combined output of wind power generation, photovoltaic power generation and battery energy storage, and establish an integrated energy system output selection model based on wind power generation, photovoltaic power generation and thermal power generation units;

[0010] S3, construct a multi-objective optimization scheduling model based on wind power generation model and photovoltaic power generation model, which includes a first objective function, a second objective function, and a third objective function: the first objective function is constructed from the power generation cost of thermal power units, the start-up and shutdown cost of thermal power units, the wind power generation operation cost, the photovoltaic power generation operation cost, the battery energy storage charging and discharging cost, the battery energy storage life depreciation cost and the demand side response. The first objective function is an economic objective function with the economic efficiency of power grid operation as the goal, wherein the thermal power units need to consider the power generation costs of three situations: conventional operation, deep peak regulation without oil injection, and deep peak regulation with oil injection; taking one day as a scheduling cycle, the wind and solar power abandonment in each scheduling period are superimposed as the wind and solar power abandonment amount of a scheduling cycle to construct the second objective function, and the second objective function is a new energy consumption objective function with the goal of reducing the amount of wind and solar power abandonment; select the output-load deviation objective function, output The fluctuation amplitude objective function or the load voltage q-axis component objective function is used as the third objective function to realize different functions. The output-load deviation objective function is composed of the output tracking objective function and the load tracking objective function. The goal of the output tracking objective function is to minimize the average deviation between the output and the load power of the integrated energy system and to track the change of the load power. The output fluctuation amplitude objective function is composed of the smooth operation objective function and the power smoothing objective function. The goal of the smooth operation objective function is to minimize the output fluctuation of the integrated energy system and reduce the impact on the power grid. The load voltage q-axis component objective function is the frequency modulation objective function, and its goal is to minimize the q-axis component of the grid voltage at the output voltage of the inverter of the integrated energy system; the output-load deviation objective function is selected in the output tracking mode, the output fluctuation amplitude objective function is selected in the smooth operation mode, and the frequency modulation objective function is selected in the frequency modulation mode;

[0011] S4, solve the multi-objective optimization scheduling model considering the battery energy storage charging and discharging life loss and the deep peak regulation of thermal power units: use the NSGA-Ⅱ algorithm to solve the constructed multi-objective optimization scheduling model, and from the obtained Pareto optimal frontier solution set, use the AHP method to make decisions to obtain the optimal scheduling plan for the integrated energy system.

[0012] Furthermore, the step S2 of establishing the wind power generation model in the integrated energy system is as follows: constructing the Weibull two-parameter wind speed probability distribution function where v hub is the wind speed at the hub of the wind turbine, and the distribution parameter Shape parameters Where σ is the standard deviation of wind speed, μ is the average wind speed (m / s) obtained from historical data, and Γ is the gamma function; the wind speed is transformed as follows: where v hub is the wind speed at the hub height of the wind turbine, v t is the wind speed at the height of the wind measurement point (m / s), z hub is the fan hub height, z t is the height of the wind measurement point (m), z0 is the ground roughness;

[0013] Calculate wind power output power P wp :

[0014]

[0015] where v c_in is the cut-in wind speed, v rate is the rated wind speed, v c_out is the cut-out wind speed (m / s), P rate is the rated output of the fan (kW), and α is the ratio of the actual daily average air density to the standard air density.

[0016] Furthermore, the steps of establishing the photovoltaic power generation model in the integrated energy system in step S2 are as follows: first, the relationship between the output current and voltage is established using the power law relationship; second, the short-circuit current I under different light intensities and temperatures is calculated using the technical reference values ​​under standard test conditions. sc , open circuit voltage V oc , Maximum power point current I m , Maximum power point voltage V m Relationship:

[0017]

[0018]

[0019]

[0020]

[0021]

[0022]

[0023]

[0024] Where d1 is the short-circuit current temperature coefficient, d2 is the open-circuit voltage temperature coefficient, f1 is the maximum power point current temperature coefficient, f2 is the maximum power point voltage temperature coefficient, △T is the difference between the test temperature and the standard temperature (℃), I sc-STC Short-circuit current (A) under standard test conditions, V oc-STC Open circuit voltage (V) under standard test conditions, I m-STC is the maximum power point current, V m-STC is the maximum power point current, S is the test light intensity (W / m 2 ), S STC is the standard light intensity, A and B are the calculated coefficients.

[0025] Furthermore, the parameters related to the output selection of the integrated energy system in step S2 include the wind speed forecast data v t , solar radiation intensity forecast data S t , power grid load day-ahead forecast data P in_load , energy storage module capacity E capacity , wind power operation and maintenance cost C w_om , photovoltaic operation and maintenance cost C pv_om , wind power profit C w_pro , photovoltaic profit C pv_pro , daily average wind speed lower limit v t_L , the lower limit of daily average radiation intensity S t_L The output selection judgment step is as follows: based on the day-ahead wind speed forecast data, the day-ahead solar irradiation intensity forecast data, the day-ahead grid load forecast data and the capacity of the energy storage module, with the wind power generation operation and maintenance costs and photovoltaic power generation operation and maintenance as constraints, judge whether the profit of wind power generation is less than its operation and maintenance costs or whether the wind speed of wind power generation is too small; if so, continue to judge whether the profit of wind power generation is less than its operation and maintenance costs or the photovoltaic power generation irradiation intensity is too low, if so, select the thermal power unit output without optimized scheduling, otherwise select photovoltaic power generation, thermal power generation and battery energy storage to jointly output; otherwise, continue to judge whether the profit of photovoltaic power generation is less than its operation and maintenance costs or the photovoltaic power generation irradiation intensity is too low, if so, select wind power generation, thermal power generation and battery energy storage to jointly output, otherwise continue to judge whether the combined output of wind power generation, photovoltaic power generation and thermal power generation can meet the grid load demand throughout the period, if so, select wind power generation, photovoltaic power generation and battery energy storage to jointly output, otherwise select wind power generation, photovoltaic power generation, thermal power generation and battery energy storage to jointly output.

[0026] Furthermore, the economic objective function in step S3 is based on the power generation cost C of the integrated energy system. ec get: Among them C the,i is the power generation cost of the i-th thermal power unit, Si_k is the startup cost of the i-th thermal power unit at time k, u i_k is the start / stop status of the i-th thermal power unit at time k, 1 is on, 0 is off, C w is the operating cost of wind power generation, C pv is the operating cost of photovoltaic power generation, C b is the battery energy storage charging and discharging cost, C bl The cost of battery energy storage life loss;

[0027] The operating cost of thermal power units

[0028] Wind power generation costs Among them C co_w is the operating cost of wind power per unit of electricity generated;

[0029] Photovoltaic power generation costs Among them C co_pv is the operating cost per unit of photovoltaic power generation;

[0030] Battery energy storage maintenance costs Among them C co_b is the unit charging and discharging cost of energy storage;

[0031] Lead-acid battery life loss cost C bl (x d )=L loss C in_bess , where C in_bess is the initial investment cost of the battery energy storage module;

[0032] The formula of frequency modulation objective function is minu gq =min(u g sin(q g -q)), where q is the phase angle of the output voltage of the integrated energy system, q g is the phase angle of the grid load voltage.

[0033] Furthermore, when the output range of the thermal power unit is within the maximum output value P max,i and the minimum output value of conventional peak load regulation P a,i The cost of thermal power units is mainly coal consumption cost. Among them, P the,i (t) is the output of thermal power unit i in time t (MW), a i 、b i 、c i is the coal consumption coefficient of thermal power unit i; when the output range of thermal power unit is within the minimum output value of conventional peak regulation P a,i and the minimum output value P for peak load regulation without oil injection b,i The life loss cost of deep peak regulation of thermal power units can be expressed as C2,i (t) = C pur / N f , where C pur is the purchase cost of thermal power units (yuan), N f is the rotor cracking cycle; when the output range of the thermal power unit is within the minimum output value P of the peak load regulation without oil injection b,i and the minimum output value P for oil injection peak regulation c,i The oil cost of the thermal power unit can be expressed as C 3,i (t) = Q oil C oil , where C oil Oil consumption of thermal power units after oil injection (tons), Q oil The oil price for that month (yuan / ton).

[0034] Furthermore, the new energy consumption objective function in step S3 divides one day into 24 scheduling periods, each of which is 1 hour, and takes the output P of 5 thermal power units as the,i Determine the parameters to be optimized: wind power output P w , photovoltaic power output P pv , the charge and discharge power P of the lead-acid battery energy storage module b As the decision variable x d , thermal power units 1 to 5 real-time power P the,i (k),i∈[1,5], the real-time charging and discharging power P of the lead-acid battery energy storage module b (k), wind power generation real-time power P w (k), real-time photovoltaic power generation power P pv (k);

[0035] By formula Calculate the minimum wind and solar curtailment cost C Ab , by reducing the amount of wind and solar power curtailment, the goal of new energy consumption can be achieved, among which C a_w is the unit wind curtailment cost (yuan), C a_pv is the unit cost of abandoned light (yuan), P r_w (k) is the predicted wind power generation value (MW) in time period k, P r_pv (k) is the predicted photovoltaic power generation value (MW) in time period k.

[0036] Furthermore, in the output tracking mode in step S3, the average load power deviation P pd The minimum load following objective function is Among them, P in_load (k) is the grid load dispatch instruction at time k (MW); the average power fluctuation value P in the stable operation mode pf The minimum power smoothing objective function is Among them, Pop (k) is the output value of the wind, solar, thermal and storage energy system at time k (MW), P op_a It is the average value of the output curve of the wind, solar, thermal and storage energy system (MW).

[0037] Furthermore, the following constraints are established in step S3:

[0038] Environmental protection constraints: Pollutant gas emission coefficient λ of thermal power units e satisfy Among them G max is the pollutant gas emission index (kg);

[0039] Lead-acid battery energy storage module state of charge constraint: lead-acid battery energy storage module state of charge SOC(k) at time k and state of charge upper limit State of charge lower limit SOC satisfy The upper and lower limits of the state of charge cannot be 1 and 0. In order for the energy storage system to be used continuously and normally, it is necessary to ensure that there is appropriate power for charging and discharging. The state of charge SOC at the start of scheduling is init and the state of charge SOC at the end time final Same, that is, SOC init =SOC final ;

[0040] Power upper and lower limit constraints: lead-acid battery energy storage module power upper limit Lead-acid battery energy storage module power lower limit P b , Wind power generation power limit and photovoltaic power generation power ceiling satisfy

[0041]

[0042] Thermal power unit ramp rate constraint: |P the,i (k+1)-P the,i (k)|≤P v,i , where P v,i is the maximum ramp rate of the i-th thermal power unit (MW / h);

[0043] Minimum start and stop time constraints for thermal power units: in is the continuous operation time of unit i at time k-1, is the continuous downtime of unit i at time k-1, is the minimum startup time of unit i, is the minimum downtime of unit i (h).

[0044] The beneficial effects of the present invention are: the present invention reduces the impact and economic influence of the uncertainty of new energy on the integrated energy system through the output part judgment module; it can provide more targeted optimization scheduling solutions based on the three different needs of power grid scheduling, thereby realizing the economic operation of the integrated energy system and the consumption of new energy. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] Figure 1 This is a schematic diagram of the integrated energy system optimization scheduling method of the present invention;

[0046] Figure 2 This is a schematic diagram of the output selection principle of the integrated energy system of the present invention;

[0047] Figure 3 This is a structural diagram of the multi-objective optimization scheduling model of the present invention;

[0048] Figure 4 It is a vector diagram of the adjustment process of the present invention. DETAILED DESCRIPTION

[0049] The embodiments of the present invention are further described below with reference to specific examples and drawings.

[0050] Reference Figure 1 As shown, the present invention discloses a three-mode three-objective integrated energy system optimization scheduling method, which is based on an integrated energy system including a wind turbine generator set, a photovoltaic generator set, a thermal power generator set and a lead-acid battery energy storage module, and includes the following steps.

[0051] S1, data processing stage (hourly level).

[0052] Based on the load day-ahead forecast data, determine whether the wind power generation output and photovoltaic power generation output forecast belong to extreme weather, and then determine the operation status: determine the wind power generation operation status based on the wind power output day-ahead forecast data; determine the photovoltaic power generation operation status based on the photovoltaic output day-ahead forecast data; determine the start and stop operation status of the thermal power unit based on the thermal power unit power generation day-ahead forecast data.

[0053] S2. Determine the output composition. Based on the predicted values ​​of wind power, photovoltaic power, thermal power units, and load, determine the output composition of the integrated energy system. Establish wind power generation models and photovoltaic power generation models, respectively. Select wind power generation, photovoltaic power generation, thermal power unit generation, and battery energy storage for combined output; photovoltaic power generation, thermal power unit generation, and battery energy storage for combined output; photovoltaic power generation, thermal power unit generation, and battery energy storage for combined output; or wind power generation, photovoltaic power generation, and battery energy storage for combined output. The specific steps for establishing the wind power generation model in the integrated energy system are as follows.

[0054] The output of a fan is mainly related to the wind speed, which can be divided into three situations with the following characteristics: (1) When the wind speed is lower than the cut-in wind speed, the fan will not generate power output due to factors such as internal transmission resistance; (2) When the wind speed is higher than the cut-in wind speed, the output power of the fan increases with the increase of wind speed, but after reaching the rated output power, increasing the wind speed will no longer increase the output power; (3) When the wind speed reaches the cut-out wind speed or even exceeds it, the unit will stop working to avoid safety problems.

[0055] Based on the above problems, the Weibull two-parameter wind speed probability distribution function is adopted, and the solution formula is as follows:

[0056]

[0057] where v hub is the wind speed at the fan hub (m / s), c is the distribution parameter, k is the shape parameter, and the solution formulas for c and k are as follows:

[0058]

[0059]

[0060] Where σ is the standard deviation of wind speed (m / s), μ is the average wind speed (m / s) obtained from historical data, and Γ is the gamma function. Because the actual wind speed measurement height is different from the wind turbine hub height, the wind speed needs to be transformed. The expression is: where v hub is the wind speed at the hub height of the wind turbine (m / s), v t is the wind speed at the height of the wind measurement point (m / s), z hub is the fan hub height (m), z t is the height of the wind measurement point (m), z0 is the ground roughness.

[0061] Wind power output power P wp The calculation formula is as follows:

[0062]

[0063] where v c_in is the cut-in wind speed (m / s), v rate is the rated wind speed (m / s), v c_out is the cut-out wind speed (m / s), P rate is the rated output of the fan (kW), and α is the ratio of the actual daily average air density to the standard air density.

[0064] The specific steps to establish a photovoltaic power generation model in an integrated energy system are as follows.

[0065] This patent uses an exponential model based on a power law function. The process is carried out in two steps: first, the relationship between output current and voltage is established using the power law relationship; second, the technical reference value under standard test conditions is used to calculate the short-circuit current I under different light intensities and temperatures. sc , open circuit voltage V oc , Maximum power point current I m , Maximum power point voltage V m Relationship:

[0066]

[0067]

[0068]

[0069]

[0070]

[0071]

[0072]

[0073] Where d1 is the short-circuit current temperature coefficient, d2 is the open-circuit voltage temperature coefficient, f1 is the maximum power point current temperature coefficient, f2 is the maximum power point voltage temperature coefficient, △T is the difference between the test temperature and the standard temperature (℃), I sc-STC Short-circuit current (A) under standard test conditions, V oc-STC Open circuit voltage (V) under standard test conditions, I m-STC is the maximum power point current (A), V m-STC is the maximum power point current (V), S is the test light intensity (W / m 2 ), S STC is the standard light intensity (W / m 2 ), A and B are the calculated coefficients.

[0074] Compared with wind power generation and photovoltaic power generation, which have uncertain outputs and therefore require modeling and analysis, thermal power generation does not require modeling. Therefore, this step establishes an output selection model for the integrated energy system based on wind power generation, photovoltaic power generation, and thermal power generation.

[0075] Parameters related to the output selection of the integrated energy system include: wind speed day-ahead forecast data v t , solar radiation intensity forecast data S t , power grid load day-ahead forecast data P in_load , energy storage module capacity E capacity , wind power operation and maintenance cost C w_om, photovoltaic operation and maintenance cost C pv_om , wind power profit C w_pro , photovoltaic profit C pv_pro , daily average wind speed lower limit v t_L , the lower limit of daily average radiation intensity S t_L Output selection judgment logic see Figure 2 .

[0076] The weather judgment module is based on the day-ahead forecast data of wind speed, solar radiation intensity, grid load, and energy storage module capacity, and uses the wind power generation operation and maintenance costs and photovoltaic power generation operation and maintenance as constraints to determine whether the wind power generation profit is less than its operation and maintenance costs or whether the wind speed of wind power generation is too low.

[0077] If so, continue to judge whether the profit of wind power generation is less than its operation and maintenance cost or the irradiation intensity of photovoltaic power generation is too low. If so, choose the thermal power unit output without optimizing scheduling. Otherwise, choose photovoltaic power generation, thermal power generation and battery energy storage to jointly output.

[0078] Otherwise, continue to judge whether the profit of photovoltaic power generation is less than its operation and maintenance cost or the irradiation intensity of photovoltaic power generation is too low. If so, choose wind power generation, thermal power generation and battery energy storage to jointly output. Otherwise, continue to judge whether the joint output of wind power generation, photovoltaic power generation and thermal power generation can meet the grid load demand at all times. If so, choose wind power generation, photovoltaic power generation and battery energy storage to jointly output. Otherwise, choose wind power generation, photovoltaic power generation, thermal power generation and battery energy storage to jointly output.

[0079] S3, builds a multi-objective optimization scheduling model based on wind power generation model and photovoltaic power generation model, and performs three-objective optimization scheduling based on output tracking mode, stable operation mode and frequency regulation mode.

[0080] A first objective function is constructed based on the power generation costs of thermal power units, the start-up and shutdown costs of thermal power units, the operating costs of wind power generation, the operating costs of photovoltaic power generation, the charging and discharging costs of battery energy storage, the lifespan depreciation costs of battery energy storage, and demand-side response. This first objective function is an economic objective function aimed at the economic operation of the power grid. A second objective function is constructed based on a scheduling cycle of one day by adding the wind and solar power curtailment in each scheduling period to the total amount of wind and solar power curtailment for that scheduling cycle. This second objective function is a new energy consumption objective function aimed at reducing the amount of wind and solar power curtailment.

[0081] The economic objective function and the new energy consumption objective function are taken as fixed objective functions, and then the third objective function is determined by choosing one of the three.

[0082] According to the mode, the output-load deviation objective function, the output fluctuation amplitude objective function and the load voltage q-axis component objective function are used as the third objective function to realize different functions: the output-load deviation objective function corresponding to the output tracking mode is composed of the output tracking objective function and the load tracking objective function. The goal of the output tracking objective function is to minimize the average deviation between the output and load power of the integrated energy system and to track the changes in load power; the output fluctuation amplitude objective function corresponding to the smooth operation mode is composed of the smooth operation objective function and the power smoothing objective function. The goal of the smooth operation objective function is to minimize the output fluctuation of the integrated energy system and reduce the impact on the power grid; the load voltage q-axis component objective function corresponding to the frequency regulation mode is the frequency regulation objective function, and its goal is to minimize the q-axis component of the grid voltage in the output voltage of the inverter of the integrated energy system.

[0083] Based on the dispatch requirements of higher-level operators, three operating modes were established: output tracking mode, stable operation mode, and frequency regulation mode. Three-mode, three-objective optimization scheduling was implemented: the output-load deviation objective function was selected for output tracking mode, the output fluctuation amplitude objective function was selected for stable operation mode, and the frequency regulation objective function was selected for frequency regulation mode. The output tracking objective was the average deviation between system output and load; the stable operation objective was the system output fluctuation amplitude; and the frequency regulation objective was the grid voltage component of the new energy inverter output voltage.

[0084] Step 3.1: Determine the parameters to be optimized.

[0085] Divide 1 day into 24 dispatching periods, each of which is 1 hour, and take the output P of five thermal power units respectively. the,i , determine the parameters to be optimized: wind power output P w , photovoltaic power output P pv , battery energy storage module charging and discharging power P b is the decision variable x d , as shown in the following table:

[0086]

[0087] Step 3.2, objective function.

[0088] Step 3.2.1, economic objective function.

[0089] The economic objective function mainly considers the power generation cost of the wind, solar, thermal and energy storage integrated energy system:

[0090]

[0091] Among them C the,i is the power generation cost of the i-th thermal power unit (yuan), S i_k is the startup cost of the i-th thermal power unit at time k, u i_kis the start / stop status of the i-th thermal power unit at time k, 1 is on, 0 is off, C w is the operating cost of wind power generation, C pv is the operating cost of photovoltaic power generation, C b is the battery energy storage charging and discharging cost, C bl The cost of battery energy storage life loss.

[0092] The operating costs of thermal power units are as follows.

[0093] When the output range of the thermal power unit is within the maximum output value P max,i and the minimum output value of conventional peak load regulation P a,i The period between the peak load and the peak load is the period of normal operation. The cost of thermal power generation units is mainly coal consumption cost. Among them, P the,i (t) is the output of thermal power unit i in time t (MW), a i 、b i 、c i is the coal consumption coefficient of thermal power unit i.

[0094] When the output range of thermal power units is within the minimum output value of conventional peak load regulation P a,i and the minimum output value P for peak load regulation without oil injection b,i During the period between the two phases, when no oil is added for deep peak regulation, not only coal consumption costs must be considered, but also loss costs. When the unit deviates from normal operating conditions, fatigue and creep losses will occur, shortening the operating life of the thermal power unit.

[0095] The life loss cost of deep peak regulation of thermal power units can be expressed as C 2,i (t) = C pur / N f , where C pur is the purchase cost of thermal power units (yuan), N f The number of rotor cracking cycles.

[0096] When the output range of the thermal power unit is within the minimum output value P of the peak load regulation without oil injection b,i and the minimum output value P for oil injection peak regulation c,i The period between the oil injection and peak regulation is the oil injection stage, and oil injection measures need to be taken to maintain the stable operation of the thermal power unit. The oil injection cost of the thermal power unit can be expressed as C 3,i (t) = Q oil C oil , where C oil Oil consumption of thermal power units after oil injection (tons), Q oil The oil price for that month (yuan / ton).

[0097] In summary, the operating cost of a thermal power unit can be expressed as a piecewise function as follows:

[0098]

[0099] Wind power generation costs Among them C co_w is the operating cost of wind power per unit of electricity generated.

[0100] Photovoltaic power generation costs Among them C co_pv is the operating cost per unit of photovoltaic power generation.

[0101] The maintenance cost of lead-acid battery energy storage is

[0102] Among them C co_b is the unit charging and discharging cost of energy storage (yuan).

[0103] The life loss cost of lead-acid battery is C bl (x d )=L loss C in_bess , where C in_bess is the initial investment cost of energy storage (yuan).

[0104] Step 3.2.2, new energy consumption objective function.

[0105] The goal of new energy consumption is to reduce the amount of wind and solar power curtailment, which is calculated as the cost of curtailment C Ab Minimum, that is Among them C a_w is the unit wind curtailment cost (yuan), C a_pv is the unit cost of abandoned light (yuan), P r_w (k) is the predicted wind power generation value (MW) in time period k, P r_pv (k) is the predicted photovoltaic power generation value (MW) in time period k.

[0106] Step 3.2.3: Output tracking objective function.

[0107] When the integrated energy system is in output tracking mode, the average load power deviation P pd The minimum load following objective function is Among them, P in_load (k) is the grid load dispatch instruction at time k (MW).

[0108] Step 3.2.4, run the objective function smoothly.

[0109] When the integrated energy system is in stable operation mode, the average power fluctuation P pf The minimum power smoothing objective function is Among them, P op (k) is the output value of the wind, solar, thermal and storage energy system at time k (MW), Pop_a It is the average value of the output curve of the wind, solar, thermal and storage energy system (MW).

[0110] Step 3.2.5, frequency modulation objective function.

[0111] The frequency modulation is performed based on a single phase-locked loop. The vector diagram of the adjustment process is shown in Figure 4 .

[0112] The formula of frequency modulation objective function is minu gq =min(u g sin(q g -q)), where q is the phase angle of the output voltage of the integrated energy system, q g is the phase angle of the grid load voltage.

[0113] The phase angle q of the output voltage of the integrated energy system is obtained by using a phase-locked loop, and the direction of the output voltage u of the integrated energy system is taken as the d-axis to measure the load voltage u of the power grid. g Perform dq transformation, then u gd will fall on the d axis, u gq Perpendicular to the d-axis, it falls on the q-axis. If u is adjusted gq Gradually tends to 0, u g Will gradually approach the d axis, when u gq When u is 0, g and u completely coincide with each other. Therefore, the objective function is the component u of the grid load voltage on the q-axis of the new energy system output voltage. gq .

[0114] Step 3.3, Constraint: k∈θ K , each decision variable should satisfy the following constraints.

[0115] Step 3.3.1, Environmental Constraints: Pollution in the integrated energy system mainly comes from CO2, SO2, and NO emissions from thermal power units. x Polluting gases, emission coefficient λ e As shown in the following table (pollutant emission coefficient).

[0116]

[0117] The environmental constraints are: Among them G max It is the pollutant gas emission index (kg).

[0118] Step 3.3.2, lead-acid battery energy storage module state of charge constraint: Where SOC(k) is the state of charge of the lead-acid battery energy storage module at time k; is the upper limit of state of charge; SOCThe lower limit of the state of charge. Considering the safety of the energy storage system, the upper and lower limits of the state of charge cannot be 1 and 0. In order for the energy storage system to be used continuously and normally, it is necessary to ensure that there is appropriate power for charging and discharging, so that the state of charge SOC init and the state of charge SOC at the end time final Same, that is, SOC init =SOC fina l.

[0119] Step 3.3.3, power upper and lower limit constraints:

[0120]

[0121] in is the upper limit of the lead-acid battery energy storage module power (MW), P b is the lower power limit of the lead-acid battery energy storage module (MW), is the upper limit of wind power (MW), is the upper limit of photovoltaic power (MW).

[0122] Step 3.3.4, thermal power unit ramp rate constraint: |P the,i (k+1)-P the,i (k)|≤P v,i , where P v,i is the maximum ramp rate of the i-th thermal power unit (MW / h).

[0123] Step 3.3.5, minimum start and stop time constraints for thermal power units: in is the continuous operation time of unit i at time k-1, is the continuous downtime of unit i at time k-1, is the minimum startup time of unit i, is the minimum downtime of unit i

[0124] S4, solve the multi-objective optimization scheduling model considering the battery energy storage charging and discharging life loss and the deep peak regulation of thermal power units.

[0125] The constructed multi-objective optimization scheduling model is solved by the NSGA-Ⅱ algorithm. The solving algorithm is a non-dominated sorting genetic algorithm (NSGA-Ⅱ). From the obtained Pareto optimal frontier solution set, the optimal solution for the integrated energy system is obtained based on the Analytic Hierarchy Process (AHP) strategy.

[0126] The above embodiments are merely illustrative of the principles and effects of the present invention, as well as some embodiments of its application. A person skilled in the art may make several modifications and improvements without departing from the inventive concept of the present invention, and all of these modifications and improvements fall within the scope of protection of the present invention.

Claims

1. A three-mode, three-objective integrated energy system optimization scheduling method, characterized by: Based on the integrated energy system including wind turbines, photovoltaic generators, thermal power generators and energy storage modules, the following steps are included S1: Determine the operation status of wind power generation based on the day-ahead forecast data of wind power output, determine the operation status of photovoltaic power generation based on the day-ahead forecast data of photovoltaic output, and determine the start-up and shutdown operation status of thermal power generation units based on the day-ahead forecast data of thermal power generation units; S2, respectively establish wind power generation models and photovoltaic power generation models, and select wind power generation, photovoltaic power generation, thermal power generation and battery energy storage to jointly output, photovoltaic power generation, thermal power generation and battery energy storage to jointly output, photovoltaic power generation, thermal power generation and battery energy storage to jointly output, or wind power generation, photovoltaic power generation and battery energy storage to jointly output. The parameters related to the output selection of the integrated energy system include the day-ahead wind speed forecast data v t , solar radiation intensity forecast data S t , power grid load day-ahead forecast data P in_load , energy storage module capacity E capacity , wind power operation and maintenance cost C w_om , photovoltaic operation and maintenance cost C pv_om , wind power profit C w_pro , photovoltaic profit C pv_pro , daily average wind speed lower limit v t_L , the lower limit of daily average radiation intensity S t_L ; The output selection steps are: based on the day-ahead wind speed forecast data, the day-ahead solar irradiation intensity forecast data, the day-ahead grid load forecast data and the capacity of the energy storage module, with the wind power generation operation and maintenance costs and photovoltaic power generation operation and maintenance as constraints, judge whether the profit of wind power generation is less than its operation and maintenance costs or whether the wind speed of wind power generation is too small; if so, continue to judge whether the profit of wind power generation is less than its operation and maintenance costs or the photovoltaic power generation irradiation intensity is too low, if so, select the thermal power unit output without optimization scheduling, otherwise select photovoltaic power generation, thermal power generation and battery energy storage to jointly output; otherwise, continue to judge whether the profit of photovoltaic power generation is less than its operation and maintenance costs or the photovoltaic power generation irradiation intensity is too low, if so, select wind power generation, thermal power generation and battery energy storage to jointly output, otherwise continue to judge whether the combined output of wind power generation, photovoltaic power generation and thermal power generation can meet the grid load demand throughout the period, if so, select wind power generation, photovoltaic power generation and battery energy storage to jointly output, otherwise select wind power generation, photovoltaic power generation, thermal power generation and battery energy storage to jointly output; S3, construct a multi-objective optimization scheduling model based on wind power generation model and photovoltaic power generation model: construct a first objective function from the power generation cost of thermal power units, the start-up and shutdown cost of thermal power units, the operating cost of wind power generation, the operating cost of photovoltaic power generation, the charging and discharging cost of battery energy storage, the life depreciation cost of battery energy storage and the demand-side response. The first objective function is an economic objective function with the economic efficiency of power grid operation as the goal; take one day as a scheduling cycle, superimpose the abandoned wind and solar power in each scheduling period into the abandoned wind and solar power amount of a scheduling cycle to construct a second objective function, and the second objective function is a new energy consumption objective function with the goal of reducing the abandoned wind and solar power amount; select the output-load deviation objective function, the output fluctuation amplitude objective function or the load voltage q-axis component objective function as the third objective Function, the output-load deviation objective function is composed of the output tracking objective function and the load tracking objective function. The goal of the output tracking objective function is to minimize the average deviation between the output and load power of the integrated energy system and to track the change of load power. The output fluctuation amplitude objective function is composed of the smooth operation objective function and the power smoothing objective function. The goal of the smooth operation objective function is to minimize the output fluctuation of the integrated energy system and reduce the impact on the power grid. The load voltage q-axis component objective function is the frequency modulation objective function, the goal of which is to minimize the q-axis component of the grid voltage at the output voltage of the inverter of the integrated energy system; the output-load deviation objective function is selected in the output tracking mode, the output fluctuation amplitude objective function is selected in the smooth operation mode, and the frequency modulation objective function is selected in the frequency modulation mode; S4, the NSGA-Ⅱ algorithm is used to solve the multi-objective optimization scheduling model. From the Pareto optimal frontier solution set obtained, the optimal scheduling plan of the integrated energy system is obtained through the AHP method.

2. A three-mode three-objective integrated energy system optimization scheduling method according to claim 1, characterized in that: The specific steps of establishing the wind power generation model in step S2 are as follows: Constructing Weibull two-parameter wind speed probability distribution function , where v hub is the wind speed at the hub of the wind turbine, and the distribution parameter , shape parameter , σ is the standard deviation of wind speed, μ is the average wind speed, is the gamma function; Transform the wind speed: , where v hub is the wind speed at the hub height of the wind turbine, v t is the wind speed at the height of the wind measurement point, z hub is the fan hub height, z t is the height of the wind measurement point, z0 is the ground roughness; Calculate wind power output: ， where v c_in is the cut-in wind speed, v rate is the rated wind speed, v c_out is the cut-out wind speed, P rate is the rated output of the fan, α It is the ratio of the actual daily average air density to the standard air density.

3. The three-mode three-objective integrated energy system optimization scheduling method according to claim 2 is characterized in that: The specific steps of establishing the photovoltaic power generation model in step S2 are: First establish the relationship between output current and voltage, and then calculate the short-circuit current under different light intensities and temperatures I sc , open circuit voltage V oc , Maximum Power Point Current I m , Maximum Power Point Voltage V m relationship ， ， ， ， ， ， ， in d 1 is the short-circuit current temperature coefficient, d 2 is the open circuit voltage temperature coefficient, f 1 is the maximum power point current temperature coefficient, f 2 is the maximum power point voltage temperature coefficient, ΔT is the difference between the test temperature and the standard temperature, I sc-STC Short-circuit current for standard test conditions, V oc-STC Open circuit voltage under standard test conditions, I m-STC is the maximum power point current, V m-STC is the maximum power point current, s To test the light intensity, S STC is the standard light intensity, A 、 B is the calculated coefficient.

4. The three-mode three-objective integrated energy system optimization scheduling method according to claim 3 is characterized in that: The economic objective function in step S3 is obtained based on the power generation cost of the integrated energy system: , where C the ,i For the i The power generation cost of a thermal power unit, S i_k For the i Taiwan thermal power units k The startup cost at the moment, u i_k For the i The start and stop status of the thermal power unit at time C w C is the operating cost of wind power generation pv is the operating cost of photovoltaic power generation, C b is the charging and discharging cost of battery energy storage, C bl The cost of battery energy storage life loss; The operating cost of thermal power units ; Wind power generation costs , where C co_w is the operating cost of wind power per unit of electricity generated; Photovoltaic power generation costs , where C co_pv is the operating cost per unit of photovoltaic power generation; Energy storage maintenance costs , , where C co_b is the unit charging and discharging cost of energy storage; Energy storage module life depreciation cost , where C in_bess is the initial investment cost of the energy storage module; The formula of frequency modulation objective function is: , where q is the phase angle of the output voltage of the integrated energy system, is the phase angle of the grid load voltage.

5. A three-mode three-objective integrated energy system optimization scheduling method according to claim 4, characterized in that: When the output range of the thermal power unit is within the maximum output value P max,i and the minimum output value of conventional peak load regulation P a,i When the cost of thermal power units is between ,in for t Thermal power units within the time i The output, a i 、b i 、c i For thermal power units i Coal consumption coefficient; when the output range of thermal power unit is within the minimum output value of conventional peak load regulation P a,i and the minimum output value P for peak load regulation without oil injection b,i When the deep peak regulation life loss cost of thermal power units is , where C pur is the purchase cost of the thermal power unit, N f is the rotor cracking cycle; when the output range of the thermal power unit is within the minimum output value P of the peak load regulation without oil injection b,i and the minimum output value P for oil injection peak regulation c,i When the oil cost of the thermal power unit is , where C oil The fuel consumption of the thermal power unit when taking oil injection measures, Q oil The oil price for that month.

6. A three-mode three-objective integrated energy system optimization scheduling method according to claim 5, characterized in that: The new energy consumption objective function in step S3 divides one day into 24 scheduling periods, each of which is 1 hour. The output P of 5 thermal power units is taken as the,i Determine the parameters to be optimized: wind power output P w , photovoltaic power output P pv , energy storage module charging and discharging power P b As the decision variable x d , real-time power of thermal power units 1 to 5 , the real-time charging and discharging power P of the energy storage module b (k), wind power generation real-time power P w (k), real-time photovoltaic power generation power P pv (k); By formula Calculate the minimum wind and solar curtailment cost C Ab , achieving the goal of new energy consumption, among which C a_w is the unit wind curtailment cost, C a_pv is the unit abandoned light cost, P r_w (k) k Wind power generation forecast value for the time period, P r_ pv (k) is the predicted value of photovoltaic power generation in the time period.

7. A three-mode three-objective integrated energy system optimization scheduling method according to claim 6, characterized in that: In step S3, the average load power deviation P in the output tracking mode pd The minimum load following objective function is , where P in_load (k) k The grid load dispatching instruction at the moment; the average power fluctuation P in the stable operation mode pf The minimum power smoothing objective function is , where P op (k) k Wind, solar, thermal and energy storage output value at any moment, P op_a It is the average value of wind, solar, thermal and energy storage output curve.

8. The three-mode three-objective integrated energy system optimization scheduling method according to claim 7 is characterized in that: The following constraints are established in step S3: Environmental protection constraints: Pollutant gas emission coefficient λ of thermal power units e satisfy , where G max It is the pollutant gas emission index; Lead-acid battery energy storage module state of charge constraints: k State of charge of the lead-acid battery energy storage module at all times State of charge upper limit 、Limit of charge state satisfy , where the upper and lower limits of the state of charge are not 1 and 0, and the state of charge at the start of scheduling is State of charge at the end Same, that is ; Power upper and lower limit constraints: lead-acid battery energy storage module power upper limit , the lower power limit of lead-acid battery energy storage module , Wind power generation power limit and photovoltaic power generation power ceiling satisfy ； Thermal power unit ramp rate constraints: ,in For the i The maximum ramp rate of each thermal power unit; Minimum start and stop time constraints for thermal power units: ,in for k -1 time crew i Continuous running time, for k -1 time crew i Continuous downtime, For the crew i Minimum boot time, For the crew i Minimal downtime.

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