Research on energy coordinated control strategy for offshore wind power based on distributed consistency algorithm

Offshore wind power is clean and has a large capacity, making it an important component of the development of new power systems. However, the randomness of offshore wind power affects the stability of the power grid. A coordinated control strategy of offshore wind power and multi-source energy based on a distributed consistency algorithm is proposed for offshore wind farms’ energy coordination management and optimization. By constructing a source network storage load model and analyzing the output and load fluctuations of offshore wind power, energy storage, and power grid, the constraint equation and balance equation of source network load storage power are derived. An adaptive distributed consistency methodology is employed based on a proportional utilization micro growth function to preserve the stability of offshore wind power and the power grid. It can enhance the power distribution of offshore wind power, energy storage, and distribution networks. The outcomes of the simulation show that the suggested approach may quickly equalize power, decrease frequency variations of offshore wind power, and enhance the commercial viability of offshore wind power.


Introduction
Onshore wind power is progressively getting saturated due to the rapid increase of wind power building.Offshore wind power has become a key focus for future new energy development and utilization due to its high wind speed, large power generation capacity, and distance from residential areas [1][2].The fast development of offshore wind power technology, as well as the ongoing expansion in grid connection size, has raised the bar for wind farm energy scheduling and frequency stability [3].
The technical regulations on the integration of wind farms into the power system require that wind farms be equipped with active power control systems that meet the requirements of power grid scheduling to achieve energy regulation [4].At present, the energy regulation of wind farms usually adopts a centralized control mode [5], that is, according to the instructions of the power grid dispatch center, certain energy distribution measures are taken to obtain the target output power of each unit, and wind turbines achieve energy regulation by adjusting the pitch angle or speed.Common active power allocation measures for wind farms include the linear allocation method [6], the nonlinear allocation method [7], and the allocation method based on optimization objectives [8].
In [9], an average power allocation control strategy is adopted, which means that wind turbines are evenly distributed to each unit based on the active power dispatch instructions of the wind farm.This control strategy is suitable for active power scheduling of smaller wind turbine groups by calculating the power of each wind turbine unit.For large wind turbine groups, this algorithm has significant errors and makes it difficult to achieve predetermined power settings.Moreover, some units may operate in an oversaturated state for a long time, affecting the lifespan of the unit.A proportional allocation algorithm based on the installed capacity of wind turbines is proposed in [10].The algorithm calculates the ratio of the rated power of each unit to the total rated power within the wind turbine group and proportionally allocates the total active power to each wind turbine group.It is only applicable to wind turbine groups with small quantities, small land occupation, and significant differences in the rated output power of each unit.In [11], the active power allocation based on the current wind speed of the wind turbine is adopted as the basis for power allocation, which is based on the ratio of the current wind speed gathered by the wind generator to the total captured wind speed of the wind generator community.In [12][13], an allocation method based on the maximum output power of wind turbines is proposed.As the allocation basis for the wind farm's active power setting to each wind turbine, the algorithm employs the ratio of the maximum output power of the wind turbine's current wind speed to the overall maximum output power of the wind farm.
The traditional active power allocation algorithm for wind turbine groups is simple in design and requires less computation, but has a large error.Moreover, in actual wind power operation, for wind turbines with wind speeds approaching the threshold, it is easy to cause the wind turbines to exit operation or operate under overload [14].An energy coordination control strategy for offshore wind power based on a distributed consistency algorithm is proposed.By constructing a source network storage load model, the source network load storage power constraint equation and balance equation are solved.An equal consumption incremental function is established based on a distributed consistency algorithm to optimize the energy distribution of offshore wind power.

Architecture of offshore wind power
Offshore wind power is influenced by various factors such as wind speed and wave loads, resulting in significant fluctuations in output power.To reduce system frequency fluctuations caused by wind turbine power fluctuations, energy storage is supplemented to balance source load power and improve the stability of offshore wind farm operations.The networking architecture is shown in Figure 1.
Figure 1 Architecture of offshore wind power.
Renewable energy sources such as offshore wind power are greatly affected by external factors.To improve the utilization rate of renewable energy, wind power is usually operated at maximum output power, and its control strategy adopts a maximum power tracking algorithm.If the power generated is larger, and the stored energy is full, it is essential to coordinate with the superior power grid for surplus power to preserve voltage stability.If the micro source power is insufficient to satisfy load demand, energy storage, and diesel generators must be coordinated to maintain a balance between source and load power.The control strategy for renewable energy sources is switched between MPPT and droop control based on the relationship between source and load power.The control strategy diagram is shown in Figure 2.

Control strategy of offshore wind turbines
When the power of distributed power sources in the microgrid is insufficient, its output voltage exceeds the standard threshold voltage of mode 1, and the wind turbine enters droop control mode.If the device's power is at its peak, it enters MPPT mode.

Control strategy of energy storage
The control technique of energy storage is critical as an intermediate buffer energy pool for source load power.Energy storage changes the working mode of energy storage by comparing the difference between source and load power: absorption mode or release mode.Therefore, the control strategy for energy storage should be able to achieve the bidirectional flow of power, and the control strategy diagram is shown in Figure 3.In the figure, Lb and Rb represent the inductance and resistance of the power storage unit; Cb is the capacity of the power storage unit; ib and ub are the power storage device's current as well as voltage, respectively; and kb is the droop coefficient.

Algorithm for distributed consistency
The consistency method controls state variables and achieves speedy convergence by transmitting information between intelligent agents.A first-order continuous agent network system may be used to model the energy connectivity microgrid as shown in (1) and ( 2 ( ) According to the characteristic, Equation ( 3) can be expressed as a matrix, as shown in (4):

Energy optimization algorithm
The function equation for offshore wind power generation cost is established based on the function of the incremental rate of equal consumption in the power system, as shown in Equation ( 5).

()
(5) where ai, bi, ci, and di are the power generation cost coefficients for different micro sources, respectively.
The objective function can be obtained from Equation ( 5), as shown in Equation ( 6).
The value of Equation ( 6) is limited by the generator output power, as shown in Equation ( 7): where P is the network power loss; Pex is the interaction power between the wind farm and the higher-level power grid.
The objective optimization function can be constructed by using the objective function and constraint equation, as shown in Equation ( 8): L By solving Equation ( 8), it can be obtained that when the Lagrange factor of each micro source is the same, the minimum cost is obtained.
The optimal energy distribution function is shown in Equation ( 9): The output power of each micro source is found by solving Equation ( 9), as shown in Equation (10):

Simulation analysis
To verify the effectiveness of the proposed strategy, the offshore wind power system shown in Figure 1 is constructed.The simulation parameters are shown in Table 1, and the power generation cost coefficients are shown in Table 2.  Initial working condition: The initial load is fully loaded, and the fan, distributed micro source, and energy storage adopts droop control.They are allocated according to the capacity ratio, which is 2.55 MW, 1.71 MW, 1.84 MW, and 0.4 MW, respectively.After optimizing the proposed technique, the wind generator power, energy storage, and other distributed micro sources are 3 MW, 1.72 MW, 2 MW, and -0.2 MW at 0.5 s, respectively, as shown in Figure 4, and the distribution of micro source power and AC bus voltage are shown in Figure 4 (a) and Figure 4   The simulation results of the incremental cost value and the cost of wind farm power generation are shown in Figure 5, and it can be seen that the recommended technique has improved the output efficiency of distributed power generation units by optimizing the distributed wind turbines and micro source power.The consistent distribution of each power generation unit has been achieved, reducing the cost of wind power generation and ensuring voltage stability.

Conclusion
For the energy coordination management and optimization of offshore wind farms, a constant consumption micro increase rate function based on a distributed consistency algorithm is established, and a distributed consistency algorithm-based coordinated control technique for offshore wind power and multi-source energy is proposed.The theoretical investigation and simulation yield the following results: 1) Distributed micro source power generation is improved and offshore wind power generation costs are reduced.At a load of 6.5 MW, the distributed micro source power generation increases by approximately 10.1%, and the cost of microgrid power generation decreases by approximately 4.7%.
2) The proposed strategy has improved the power generation efficiency of wind farms and reduced voltage fluctuations in wind farms.

Figure 2
Figure 2 Control strategy of offshore wind turbines.

Figure 3
Figure 3 Energy storage Control strategy.

Figure 5
Figure 5 Economic comparison of the wind farm. ).
(2)where xi(t) is the input of nodes vi; xj(t) is the input of nodes vj.

Table 2
Power Generation Cost Coefficients.
(b).Power distribution and AC bus voltage of each micro source before and after optimization.