Research on power electronic transformer applied in AC/DC hybrid distribution networks

The AC/DC hybrid distribution network is one of the trends in distribution network development, which poses great challenges to the traditional distribution transformer. In this paper, a new topology suitable for AC/DC hybrid distribution network is put forward according to the demands of power grid, with advantages of accepting DG and DC loads, while clearing DC fault by blocking the clamping double sub-module(CDSM) of input stage. Then, this paper shows the typical structure of AC/DC distribution network that is hand in hand. Based on the new topology, this paper designs the control and modulation strategies of each stage, where the outer loop controller of input stage is emphasized for its twocontrol mode. At last, the rationality of new topology and the validity of control strategies are verified by the steady and dynamic state simulation. At the same time, the simulation results highlight the role of PET in energy regulation.

Hierarchical Control Strategy for Load Regulation Based on Stackelberg Game Theory Considering Randomness

Demand response has been recognized as a valuable functionality of power systems for mitigating power imbalances.This paper proposes a hierarchical control strategy among the distribution system operator(DSO),load aggregators(LAs),and thermostatically controlled loads(TCLs);the strategy includes a scheduling layer and an executive layer to provide load regulation.In the scheduling layer,the DSO(leader)offers compensation price(CP)strategies,and the LAs(followers)respond to CP strategies with available regulation power(ARP)strategies.Profits of the DSO and LAs are modeled according to their behaviors during the load regulation process.Stackelberg game is adopted to capture interactions among the players and leader and to obtain the optimal strategy for each participant to achieve utility.Moreover,considering inevitable random factors in practice,e.g.,renewable generation and behavior of users,two different stochastic models based on sample average approximation(SAA)and parameter modification are formulated with improved scheduling accuracy.In the executive layer,distributed TCLs are triggered based on strategies determined in the scheduling layer.A self-triggering method that does not violate user privacy is presented,where TCLs receive external signals from the LA and independently determine whether to alter their operation statuses.Numerical simulations are performed on the modified IEEE-24 bus system to verify effectiveness of the proposed strategy.

Dynamic-decision-based Real-time Dispatch for Reducing Constraint Violations

This paper proposes a dynamic-decision-based realtime dispatch method to coordinate the economic objective with multiple types of security dispatch objectives while reducing constraint violations in the process of adjusting the system operation point to the optimum.In each decision moment,the following tasks are executed in turn:①locally linearizing the system model at the current operation point with the online model identification by using measurements;②narrowing down the gaps between unsatisfied security requirements and their security thresholds in order of priority;③minimizing the generation cost;④minimizing the security indicators within their security thresholds.Compared with the existing real-time dispatch strategies,the proposed method can adjust the deviations caused by unpredictable power flow fluctuations,avoid dispatch bias caused by model parameter errors,and reduce the constraint violations in the dispatch decision process.The effectiveness of the proposed method is verified with the IEEE 39-bus system.

Data-driven Operation Risk Assessment of Wind-integrated Power Systems via Mixture Models and Importance Sampling

The increasing penetration of highly intermittent wind generation could seriously jeopardize the operation reliability of power systems and increase the risk of electricity outages. To this end, this paper proposes a novel data-driven method for operation risk assessment of wind-integrated power systems. Firstly, a new approach is presented to model the uncertainty of wind power in lead time. The proposed approach employs k-means clustering and mixture models(MMs) to construct time-dependent probability distributions of wind power.The proposed approach can also capture the complicated statistical features of wind power such as multimodality. Then, a nonsequential Monte Carlo simulation(NSMCS) technique is adopted to evaluate the operation risk indices. To improve the computation performance of NSMCS, a cross-entropy based importance sampling(CE-IS) technique is applied. The CE-IS technique is modified to include the proposed model of wind power.The method is validated on a modified IEEE 24-bus reliability test system(RTS) and a modified IEEE 3-area RTS while employing the historical data of wind generation. The simulation results verify the importance of accurate modeling of shortterm uncertainty of wind power for operation risk assessment.Further case studies have been performed to analyze the impact of transmission systems on operation risk indices. The computational performance of the framework is also examined.