Research on Operation Optimization of Energy Storage Power Station and Integrated Energy Microgrid Alliance Based on Stackelberg Game

With the development of renewable energy technologies such as photovoltaics and wind power,it has become a research hotspot to improve the consumption rate of new energy and reduce energy costs through the deployment of energy storage.To solve the problem of the interests of different subjects in the operation of the energy storage power stations(ESS)and the integrated energy multi-microgrid alliance(IEMA),this paper proposes the optimization operation method of the energy storage power station and the IEMA based on the Stackelberg game.In the upper layer,ESS optimizes charging and discharging decisions through a dynamic pricing mechanism.In the lower layer,IEMA optimizes the output of various energy conversion coupled devices within the IEMA,as well as energy interaction and demand response(DR),based on the energy interaction prices provided by ESS.The results demonstrate that the optimization strategy proposed in this paper not only effectively balances the benefits of the IEMA and ESS but also enhances energy consumption rates and reduces IEMA energy costs.

Virtual Synchronous Generator Adaptive Control of Energy Storage Power Station Based on Physical Constraints

The virtual synchronous generator(VSG)can simulate synchronous machine’s operation mechanism in the control link of an energy storage converter,so that an electrochemical energy storage power station has the ability to actively support the power grid,from passive regulation to active support.Since energy storage is an important physical basis for realizing the inertia and damping characteristics in VSG control,energy storage constraints of the physical characteristics on the system control parameters are analyzed to provide a basis for the system parameter tuning.In a classic VSG control,its virtual inertia and damping coefficient remain unchanged.When the grid load changes greatly,the constant control strategy most likely result in the grid frequency deviation beyond the stable operation standard limitations.To solve this problem,a comprehensive control strategy considering electrified wire netting demand and energy storage unit state of charge(SOC)is proposed,and an adaptive optimization method of VSG parameters under different SOC is given.The energy storage battery can maintain a safe working state at any time and be smoothly disconnected,which can effectively improve the output frequency performance of energy storage system.Simulation results further demonstrated the effectiveness of the VSG control theoretical analysis.

Optimal Dispatch for Battery Energy Storage Station in Distribution Network Considering Voltage Distribution Improvement and Peak Load Shifting

Distribution networks are commonly used to demonstrate low-voltage problems.A new method to improve voltage quality is using battery energy storage stations(BESSs),which has a four-quadrant regulating capacity.In this paper,an optimal dispatching model of a distributed BESS considering peak load shifting is proposed to improve the voltage distribution in a distribution network.The objective function is to minimize the power exchange cost between the distribution network and the transmission network and the penalty cost of the voltage deviation.In the process,various constraints are considered,including the node power balance,single/two-way power flow,peak load shifting,line capacity,voltage deviation,photovoltaic station operation,main transformer capacity,and power factor of the distribution network.The big M method is used to linearize the nonlinear variables in the objective function and constraints,and the model is transformed into a mixed-integer linear programming problem,which significantly improves the model accuracy.Simulations are performed using the modified IEEE 33-node system.A typical time period is selected to analyze the node voltage variation,and the results show that the maximum voltage deviation can be reduced from 14.06%to 4.54%.The maximum peak-valley difference of the system can be reduced from 8.83 to 4.23 MW,and the voltage qualification rate can be significantly improved.Moreover,the validity of the proposed model is verified through simulations.

Adaptive Power Control Based on Double-layer Q-learning Algorithm for Multi-parallel Power Conversion Systems in Energy Storage Station

An energy storage station(ESS)usually includes multiple battery systems under parallel operation.In each battery system,a power conversion system(PCS)is used to connect the power system with the battery pack.When allocating the ESS power to multi-parallel PCSs in situations with fluctuating operation,the existing power control methods for parallel PCSs have difficulty in achieving the optimal efficiency during a long-term time period.In addition,existing Q-learning algorithms for adaptive power allocation suffer from the curse of dimensionality.To overcome these challenges,an adaptive power control method based on the double-layer Q-learning algorithm for n parallel PCSs of the ESS is proposed in this paper.First,a selection method for the power allocation coefficient is developed to avoid repeated actions.Then,the outer action space is divided into n+1 power allocation modes according to the power allocation characteristics of the optimal operation efficiency.The inner layer uses an actor neural network to determine the optimal action strategy of power allocations in the non-steady state.Compared with existing power control methods,the proposed method achieves better performance for both static and dynamic operation efficiency optimization.The proposed method optimizes the overall operation efficiency of PCSs effectively under the fluctuating power outputs of the ESS.

Spectrum-Sensing Method for Arc Fault Detection in Direct Current System with Lithium Batteries

We mainly study the detection of arc faults in the direct current (DC) system of lithium battery energystorage power station. Lithium battery DC systems are widely used, but traditional DC protection devices areunable to achieve adequate protection of equipment and circuits. We build an experimental platform based onan energy storage power station with lithium batteries. Then, the data collection of normal current and arc-faultcurrent is completed under multiple conditions, and the waveforms of obvious and weak signals as the arc occursare presented. We analyze the principles and application range of several common spectrum-sensing methods andstudy the feasibility of applying them to the arc detection field. Finally, the covariance absolute value detectionalgorithm is selected, and the average value of the current is filtered out to make the algorithm adapt to the arcdetection field. The result shows that the detection probability in 500 sets of experimental data has reached 98%.