Energy management strategy of new energy direct current micro-grid water production system

Abstract

The application discloses an energy management strategy of a new energy direct-current micro-grid water production system, which adopts daily operation cost, daily water production and daily abandoned wind and light rate as optimization indexes of the whole system, uses an improved radar chart method to obtain a fitness function, and uses a genetic algorithm to solve an optimal solution of the fitness function under full consideration of specific requirements of water production equipment and influences of converter droop control on unit output. Since a deviation range of a direct-current bus voltage has certain requirements, droop coefficients of the converter droop control have certain limitations, and the droop coefficients influence unit output. The application fully considers influences of control modes of units of the direct-current micro-grid on unit output and special constraint conditions of water production equipment, and realizes optimal operation of water production, water production cost and abandoned wind and light rate.

Description

Technical Field

[0001] This invention belongs to the field of microgrid energy management technology, specifically relating to an energy management strategy for a new energy DC microgrid water production system. Background Technology

[0002] With the establishment of the "dual carbon" target, new energy DC microgrids have been widely used, effectively improving the absorption rate of wind and solar power. Due to the lack of freshwater resources in remote areas such as islands, new energy DC microgrid water production systems have begun to be applied in these areas. The new energy DC microgrid water production system uses photovoltaic arrays and wind turbines connected to the DC microgrid bus via converters to provide clean new energy. Energy storage batteries are used to smooth energy fluctuations, and the load is air-to-water equipment. The converter connected to the energy storage battery adopts a droop control strategy.

[0003] With the increasing complexity of power systems and the instability of power output from wind turbines and photovoltaic arrays in microgrids, there is an urgent need for a system to manage the daily output and power consumption of each unit in a microgrid, thereby achieving various microgrid operation optimization goals, such as minimizing the daily operating cost of the microgrid and increasing the absorption rate of new energy sources, so as to ensure the stable and safe operation of the microgrid according to the strategy. For the water production system of the new energy DC microgrid, increasing the water production capacity of the water production equipment is also one of the main objectives while meeting the above objectives.

[0004] Chinese patent application CN109119983A proposes an energy management method for an electric-hydrogen islanded DC microgrid. This method obtains measurement data by measuring the state of each unit in the DC microgrid's hydrogen production system at the current moment. The top layer of a hierarchical control system calculates the optimal output / input power reference values ​​for the energy storage system and hydrogen production equipment at the current moment based on system economy, stability, and the measured data. Finally, the reference power signal is transmitted to the next layer. This method obtains the optimal solution by setting an energy storage state stability function, a cost function, and a penalty function, and under constraints such as the maximum charge / discharge power of the energy storage unit, the response time of the hydrogen production equipment, and the upper and lower limits of the battery's state of charge (SOC). If the upper layer requires the output power to exceed the limit, the lower layer controls the output power to remain within the finite value. However, this patented technology primarily optimizes system operating costs without considering other optimization objectives such as wind and solar curtailment rates and hydrogen production. Furthermore, the method for finding the optimal solution requires extensive computation. Most importantly, if the output power of each unit exceeds the constraints, the lower-level control output power is kept within the limits, without finding the optimal operating solution under the constraints. Finally, since the converters connecting the wind turbine, photovoltaic array, and energy storage unit to the bus use droop control, the limitation of droop control on the output of each unit is not considered.

[0005] Chinese patent application CN114221369A discloses a high-voltage direct current (HVDC) power supply system and its energy management method based on a photovoltaic-storage DC microgrid. The system comprises a photovoltaic array (connected in parallel with multiple photovoltaic power generation units) and a hybrid energy storage module, forming an HVDC power supply system with eight operating states based on the AC grid, the photovoltaic array converter, and the energy storage module. By monitoring the status of each unit in the photovoltaic-storage DC microgrid, the system manages and controls the converter operating state of each unit or switches the photovoltaic array's operating mode according to these eight operating states to reduce system operating costs and energy consumption, and improve system efficiency and the safety and stability of the power supply system. However, this patented technology only simply guarantees system stability and safety; it does not provide detailed modeling of system operating costs, photovoltaic array curtailment rates, etc., to enable the system to achieve more diversified optimization goals under the premise of stability and safety. Summary of the Invention

[0006] In view of the above, the present invention provides an energy management strategy for a new energy DC microgrid water production system, which fully considers the actual control situation of DC microgrids, can better fit the control mode of new energy DC microgrids, and realizes further optimization of the energy management strategy of new energy DC microgrids.

[0007] An energy management method for a new energy DC microgrid water production system includes the following steps:

[0008] (1) Collect environmental information about the location of the water purification equipment;

[0009] (2) Calculate the daily operating cost, daily water production, and daily wind and solar curtailment rate of the water production system;

[0010] (3) Standardize the daily operating cost, daily water production and daily wind and solar curtailment rate and assign different weights to the three to determine the objective function of energy management of the water production system.

[0011] (4) Set constraints for the optimized scheduling and management of the water production system;

[0012] (5) Based on the above objective function and constraints, the optimal charging and discharging power of the energy storage battery and the optimal operating power of the water treatment equipment at each time period are obtained.

[0013] Furthermore, the environmental information collected in step (1) includes air density, relative humidity, air flow rate of the water treatment equipment, saturated vapor pressure before and after dehumidification of the water treatment equipment, and local atmospheric pressure.

[0014] Furthermore, in step (2), the daily operating cost of the water treatment system is calculated using the following formula:

[0015] C = C w +Cpv +C bat +C gw

[0016]

[0017]

[0018]

[0019]

[0020] Where: C represents the daily operating cost of the water treatment system, C w CRF represents the daily operating cost of the wind turbines in the system. w As the investment recovery factor for wind turbine units, I w M represents the total investment cost of the wind turbine unit. w For the maintenance cost of wind turbine units, F w P is the capacity factor of the wind turbine. w (t) represents the power output of the wind turbine in hour t, C pv CRF represents the daily operating cost of the photovoltaic array in the system. pv I is the investment recovery factor for photovoltaic arrays. pv M represents the total investment cost of the photovoltaic array. pv For the maintenance cost of photovoltaic arrays, F pv P represents the capacity factor of the photovoltaic array. pv (t) represents the power output of the photovoltaic array in hour t, C bat CRF is the daily operating cost of the energy storage batteries in the system. bat As the investment recovery factor for energy storage batteries, I bat M represents the total investment cost of energy storage batteries. bat For the maintenance costs of energy storage batteries, E bat For the capacity of the energy storage battery, T a P represents the annual operating hours of the energy storage battery. batmax P is the rated charge and discharge power of the energy storage battery. bat (t) represents the optimal charge / discharge power of the energy storage battery at hour t, C gw CRF is the daily operating cost of the water purification equipment in the system. gw As the investment recovery factor for water purification equipment, I gw M represents the total investment cost of the water purification equipment. gw For the maintenance costs of water purification equipment, F gw P is the capacity factor of the water purification equipment. gw (t) represents the optimal operating power of the water treatment equipment in hour t.

[0021] Furthermore, in step (2), the daily water production of the water treatment system is calculated using the following formula:

[0022]

[0023] Where: W is the daily water production capacity of the water treatment system, ρ is the air density at the location of the water treatment equipment, and V is the density of the air. m Let X1 and X2 represent the airflow rate of the water purification equipment, X1 and X2 represent the air moisture content before and after dehumidification, respectively, P0 be the rated water purification power of the equipment, and k be a given safety factor. gw (t) represents the optimal operating power of the water treatment equipment in hour t.

[0024] Furthermore, in step (2), the daily wind and solar curtailment rate is calculated using the following formula:

[0025]

[0026] Where: DUMP is the daily wind and solar curtailment rate of the water treatment system, P w (t) and P pv (t) represents the power output of the wind turbine and photovoltaic array in the system at hour t, respectively. cha (t) and P dis (t) represent the charging power and discharging power of the energy storage battery in the system at hour t, respectively. gw (t) represents the optimal operating power of the water purification equipment in the system at hour t.

[0027] Furthermore, the expression for the objective function is as follows:

[0028]

[0029]

[0030] Where: l1, l2, and l3 are the standardized results of daily operating cost, daily water production, and daily wind and solar curtailment rate, respectively, and θ i =2πω i , i = 1, 2, 3, ω1, ω2, ω3 are the weights corresponding to daily operating cost, daily water production, and daily wind and solar curtailment rate, respectively.

[0031] Furthermore, the constraints include power balance constraints, water production equipment power constraints, water production equipment airflow velocity constraints, energy storage battery state of charge constraints, and droop coefficient constraints under DC microgrid bus voltage deviation requirements.

[0032] Furthermore, the power balance constraint conditions are as follows:

[0033] P w (t)+Ppv (t)+P dis (t)-P cha (t)-P gw (t)=0

[0034] Where: P w (t) and P pv (t) represents the power output of the wind turbine and photovoltaic array in the system at hour t, respectively. cha (t) and P dis (t) represent the charging power and discharging power of the energy storage battery in the system at hour t, respectively. gw (t) represents the optimal operating power of the water purification equipment in the system at hour t.

[0035] Furthermore, the power constraints of the water purification equipment are as follows:

[0036] P gwmin <P gw (t)<P gwmax

[0037] Where: P gwmin and P gwmax These are the minimum and maximum operating power of the water purification equipment, respectively.

[0038] Furthermore, the airflow velocity constraint conditions in the air duct of the water treatment equipment are as follows:

[0039] V wind <V wmax

[0040] V m <S*V wind

[0041] Where: V wind V is the air velocity in the water purification equipment. m V is the airflow rate through the water purification equipment, S is the cross-sectional area of ​​the air duct of the water purification equipment, and V is the airflow rate through the water purification equipment. wmax This refers to the maximum permissible wind speed in the air duct of the water treatment equipment.

[0042] Furthermore, the state of charge constraints of the energy storage battery are as follows:

[0043] SOC min ≤SOC≤SOC max

[0044] Where: SOC is the state of charge of the energy storage battery. min and SOC max These represent the minimum and maximum states of charge of the energy storage battery, respectively.

[0045] Furthermore, the droop coefficient constraint condition is as follows:

[0046]

[0047] Where: η is the droop coefficient, U max U is the maximum value within the allowable range of the DC microgrid bus voltage. ref P is the reference voltage for droop control of the energy storage battery converter. dc * P is the output power command value of the energy storage battery converter. dcmax This represents the maximum output power of the energy storage battery converter.

[0048] Furthermore, in step (5), a genetic algorithm is used to optimize and solve the objective function.

[0049] The energy management strategy for the new energy DC microgrid water production equipment of this invention fully considers the influence of water production load characteristics and the control mode of each unit converter on the energy management strategy. Taking droop control without communication requirements as an example, it considers the range of droop coefficient values ​​within the allowable range of bus voltage deviation, thus affecting the output of the unit. In addition, considering the cost of water production capacity under different humidity and temperature conditions, this invention can more fully optimize the energy management of the new energy DC microgrid water production system, achieving high renewable energy utilization and minimum cost water production. Attached Figure Description

[0050] Figure 1 This is a schematic diagram of a new energy DC microgrid water production system.

[0051] Figure 2 A simplified structural diagram of a distributed micro-source access DC microgrid model.

[0052] Figure 3 This is a schematic diagram of the adaptive droop control principle of a traditional parallel converter.

[0053] Figure 4 This is a flowchart illustrating the genetic algorithm. Detailed Implementation

[0054] To describe the present invention in more detail, the technical solution of the present invention will be described in detail below with reference to the accompanying drawings and specific embodiments.

[0055] This embodiment connects a wind turbine generator (DG2) and a photovoltaic array (DG1) to a DC microgrid architecture, and uses three sets of 200V, 1Ah lithium-ion batteries as units to mitigate the energy of the DC microgrid. Their initial SOCs are 20%, 40%, and 60%, respectively. The load is a new energy DC microgrid water production system consisting of a 10kW air-to-water generator. Figure 1As shown.

[0056] The energy management strategy of the new energy DC microgrid water production system of the present invention includes the following steps:

[0057] (1) First, design the daily operating cost C, daily water production W, and daily wind and solar curtailment rate DUMP of the system;

[0058] The daily operating cost C of the new energy DC microgrid water production system can be calculated by the following formula:

[0059] C = C w +C pv +C bat +C gw

[0060] Wind turbine power generation cost C w Cost of photovoltaic array C pv Energy storage cost C bat Cost of water purification equipment C gw They are as follows:

[0061]

[0062]

[0063] Where: P a It is the average annual output power of the wind turbine, P wt-rate This is the rated power of the wind turbine.

[0064]

[0065] Among them: I pv F is the total investment cost of the photovoltaic array. pv M is the capacity factor of the photovoltaic array. pv It is the maintenance cost of the photovoltaic array, P pv This is the actual output of the photovoltaic array.

[0066]

[0067] Where: P batmax It is the rated charge and discharge power of the energy storage battery, T a It is the annual operating hours of the energy storage battery, E bat It refers to the capacity of the energy storage battery.

[0068]

[0069] Among them: I gw It is the total investment cost of the water purification equipment, F gw M is the capacity factor of the water purification equipment. gw It's the maintenance cost of the water purification equipment, CRFgw It is the investment recovery factor for water purification equipment.

[0070] The formula for calculating the return on investment factor (CRF) is as follows. The CRF can be used to convert the initial investment cost into an annualized cost.

[0071]

[0072] Where: n i R represents the lifespan of each component, and R represents the depreciation rate.

[0073] Calculate the water production capacity of the water purification equipment using the following formula:

[0074]

[0075] Where: the unit of air density ρ is kg / m³ 3 Under standard conditions, it is usually taken as 1.29 kg / m³. 3 airflow V m The unit is m 3 / h, X1 represents the air moisture content before dehumidification, X2 represents the air moisture content after dehumidification, the unit is g / kg dry air, k represents the safety factor (taken as 1.2), P0 is the rated water production capacity of the water purification equipment, P gw (t) represents the optimal operating power of the water treatment equipment in hour t.

[0076] The air moisture content X is calculated using the following formula:

[0077]

[0078] in: P represents the relative humidity of moist air. s P is the saturated vapor pressure at humid air temperature. b The pressure is the local atmospheric pressure, expressed in MPa.

[0079] The wind and solar curtailment rate is calculated using the following formula:

[0080]

[0081] Where: P w (t) and P pv (t) represents the power generated by the wind turbine and photovoltaic array in hour t under the current wind and solar conditions, P cha (t) and P dis (t) represents the charging power and discharging power of the energy storage battery in hour t, respectively.

[0082] (2) The above-mentioned daily operating cost C, daily water production W, and daily wind and solar curtailment rate DUMP are standardized. The standardized calculation is as follows:

[0083]

[0084]

[0085]

[0086] The optimization objectives of the three factors—daily operating cost C, daily water production W, and daily wind and solar curtailment rate DUMP—are weighted according to their relative importance:

[0087] C>W>DUMP

[0088] The importance is quantified using the following formula:

[0089]

[0090] Where: r k 1 represents X k-1 and X k Equally important, with r k The increase in X indicates k-1 Compared to X k It is becoming increasingly important.

[0091] The fitness function F of the system is determined using the radar chart method:

[0092]

[0093]

[0094] Where: l1, l2, and l3 are the standardized results of daily operating cost, daily water production, and daily wind and solar curtailment rate, respectively, and θ i =2πω i , i = 1, 2, 3, ω1, ω2, ω3 are the weights corresponding to daily operating cost, daily water production, and daily wind and solar curtailment rate, respectively.

[0095] (3) Set constraints for the optimization scheduling model of the new energy water production system, including power balance constraints, power constraints of water production equipment, air velocity constraints of air ducts of water production equipment, and droop coefficient constraints under the voltage deviation requirements of DC microgrid bus.

[0096] The power constraints of the new energy water production system include the following:

[0097] P w (t)+P pv (t)+P dis (t)-P cha (t)-P gw (t)=0

[0098] The power constraints of the water purification equipment are as follows:

[0099] P gwmin <P gw (t)<P gwmax

[0100] Where: P gwmin and P gwmax These are the minimum and maximum operating power of the water purification equipment, respectively.

[0101] Airflow V w Constraints: Since water vapor in the air condenses into liquid water in the evaporator of the water purification equipment, the wind speed V flowing through the water purification equipment is affected. wind The following restrictions apply:

[0102] V wind <V wmax

[0103] Therefore, the air flow rate V in the water purification equipment m The restrictions are as follows:

[0104] V m <S*V wind

[0105] Where: V m V is the airflow rate through the water purification equipment, S is the cross-sectional area of ​​the air duct of the water purification equipment, and V is the airflow rate through the water purification equipment. wind The air velocity in the water purification equipment.

[0106] Constraints on the state of charge and number of charge / discharge cycles of energy storage batteries:

[0107] SOC min ≤SOC≤SOC max

[0108] In this embodiment, V wmax =3m / s, S=1m 2 SOC min =0.2, SOC max =0.8.

[0109] Sag coefficient constraint:

[0110] Assume the DC bus voltage deviation range is [-εU ref ,+εU ref ], where ε is the allowable voltage deviation range, which varies slightly depending on the voltage level, then the DC voltage U dc satisfy:

[0111] U min =(1-ε)U ref ≤U dc ≤(1+ε)Uref =U max

[0112] Among them: U min and U max These are the minimum and maximum values, respectively, within the allowable range of bus voltage. ref This is the reference voltage for the converter's droop control.

[0113]

[0114] The transformation yields the following formula:

[0115]

[0116] The power range of the converter to be solved is:

[0117]

[0118] Combining the converter's droop control characteristics, we have:

[0119]

[0120] Assume U ref =U n Then when The droop coefficient ranges from [0, ∞).

[0121] when The selection range for the droop factor is as follows:

[0122]

[0123] Among them: U pcc U is the DC bus common point voltage. dc I is the output voltage of the converter. dc P is the output current of the converter. dc R is the output power of the converter. line P is the line impedance. dcmax U is the maximum output power of the converter. n This is the rated voltage of the DC bus.

[0124] The relationship between the converter output power and the droop control coefficient is as follows:

[0125]

[0126] Figure 2 and Figure 3 A simplified structure of a distributed unit connected to a DC microgrid using droop control is demonstrated, along with the adaptive droop control principle. The rated DC bus voltage and the allowable deviation range of the bus voltage are as follows:

[0127]

[0128] Therefore, U min =651V, U max =749V.

[0129] (4) Based on the fitness function F and various constraints obtained in steps (2) and (3), adopt the following... Figure 4 The genetic algorithm shown seeks the optimal fitness function value F, that is:

[0130]

[0131] Where: g≤0 represents the constraint condition of the new energy DC water production system, the genetic algorithm sets the evolution iteration variable G, the maximum number of generations G=100, and randomly generates N p = 100 initial individuals.

[0132] Finally, the optimal charge / discharge power P of the energy storage battery under the current optimization objective condition is obtained. bat Optimal operating power P of water purification equipment gw .

[0133] The above description of the embodiments is provided to enable those skilled in the art to understand and apply the present invention. Those skilled in the art can readily make various modifications to the above embodiments and apply the general principles described herein to other embodiments without creative effort. Therefore, the present invention is not limited to the above embodiments, and any improvements and modifications made to the present invention by those skilled in the art based on the disclosure thereof should be within the scope of protection of the present invention.

Claims

1. An energy management method of a new energy direct current micro-grid water production system, comprising the following steps: (1) collecting environmental information of a location of a water production device; (2) calculating daily operation cost, daily water production and daily wind and light rejection rate of the water production system, wherein: a calculation expression of the daily operation cost is as follows: C=C w +C pv +C bat +C gw Where: C represents the daily operating cost of the water treatment system, C w CRF represents the daily operating cost of the wind turbines in the system. w As the investment recovery factor for wind turbine units, I w M represents the total investment cost of the wind turbine unit. w For the maintenance cost of wind turbine units, F w P is the capacity factor of the wind turbine. w (t) represents the power output of the wind turbine in hour t, C pv CRF represents the daily operating cost of the photovoltaic array in the system. pv I is the investment recovery factor for photovoltaic arrays. pv M represents the total investment cost of the photovoltaic array. pv For the maintenance cost of photovoltaic arrays, F pv P represents the capacity factor of the photovoltaic array. pv (t) represents the power output of the photovoltaic array in hour t, C bat CRF is the daily operating cost of the energy storage batteries in the system. bat As the investment recovery factor for energy storage batteries, I bat M represents the total investment cost of energy storage batteries. bat For the maintenance costs of energy storage batteries, E bat For the capacity of the energy storage battery, T a P represents the annual operating hours of the energy storage battery. batmax P is the rated charge and discharge power of the energy storage battery. bat (t) represents the optimal charge / discharge power of the energy storage battery in hour t, C gw CRF is the daily operating cost of the water purification equipment in the system. gw As the investment recovery factor for water purification equipment, I gw M represents the total investment cost of the water purification equipment. gw For the maintenance costs of water purification equipment, F gw P is the capacity factor of the water purification equipment. gw (t) represents the optimal operating power of the water purification equipment in hour t; a calculation expression of the daily water production is as follows: Wherein: W is the daily water production of the water production system, p is the air density at the location of the water production equipment, V m is the air flow of the water production equipment, X1 and X2 represent the water content of the air before and after dehumidification of the water production equipment, P0 is the rated water production power of the water production equipment, and k is a given safety factor; a calculation expression of the daily wind and light rejection rate is as follows: wherein: DUMP is the daily dump rate of the water production system, P cha (t) and P dis (t) represent the charging power and discharging power of the energy storage battery in the system at the tth hour, respectively; (3) performing standardization processing on the daily operation cost, the daily water production and the daily wind and light rejection rate and giving different weights to the three, so as to determine a target function expression of energy management of the water production system as follows: Wherein: l1, l2, l3 are the results of daily operation cost, daily water production, daily wind and light rejection rate after standardization processing, respectively, and θ i = 2πω i , i = 1, 2, 3, ω1, ω2, ω3 are the weights corresponding to daily operation cost, daily water production, daily wind and light rejection rate, respectively, and S and L are intermediate variables; (4) setting constraint conditions of optimized scheduling management of the water production system, including a power balance constraint condition, a water production device power constraint condition, a water production device air duct air flow rate constraint condition, a storage battery state of charge constraint condition and a droop coefficient constraint condition under a bus voltage deviation requirement of the direct current micro-grid; the power balance constraint condition is as follows: P w (t)+P pv (t)+P dis (t)-P cha (t)-P gw (t) = 0 the water production device power constraint condition is as follows: P gwmin <P gw (t) <P gwmax wherein: P gwmin and P gwmax are the minimum and maximum operating power of the water production plant, respectively; the water production device air duct air flow rate constraint condition is as follows: V wind <V wmax V m <A*V wind where: V wind is the air flow rate in the water production plant, A is the cross-sectional area of the air duct of the water production plant, V wmax is the maximum permissible air speed in the air duct of the water production plant; the storage battery state of charge constraint condition is as follows: SOC min ≤ SOC ≤ SOC max wherein: SOC is the state of charge of the energy storage battery, SOC min and SOC max are the minimum and maximum state of charge of the energy storage battery, respectively. the droop coefficient constraint condition is as follows: wherein: η is a droop coefficient, U max is the maximum value within the allowable range of the DC microgrid bus voltage, U ref is the reference voltage of the energy storage battery converter droop control, P dc * is the output power command value of the energy storage battery converter, P dcmax is the maximum output power of the energy storage battery converter; (5) performing optimized solving according to the target function and the constraint conditions, so as to obtain optimal charging and discharging power of the storage battery and optimal operation power of the water production device in each period.

2. The energy management method of claim 1, wherein: The environmental information collected in the step (1) includes air density, air relative humidity, air flow rate of the water production device, saturated vapor pressure before and after dehumidification of the water production device and local atmospheric pressure.

3. The energy management method of claim 1, wherein: In the step (5), a genetic algorithm is used to perform optimized solving on the target function.

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