Multi-microgrid Energy Management Systems: Architecture, Communication, and Scheduling Strategies

The increasing penetration of various distributed and renewable energy resources at the consumption premises,along with the advanced metering,control and communication technologies,promotes a transition on the structure of traditional distribution systems towards cyber-physical multi-microgrids(MMGs).The networked MMG system is an interconnected cluster of distributed generators,energy storage as well as controllable loads in a distribution system.And its operation complexity can be decomposed to decrease the burdens of communi-cation and control with a decentralized framework.Consequently,the multi-microgrid energy management system(MIVIGEIV1S)plays a significant role in improving energy efficiency,power quality and reliability of distribution systems,especially in enhancing system resiliency during contingencies.A comprehensive overview on typical functionalities and architectures of MMGEMS is illustrated.Then,the emerging communication technologies for information monitoring and interaction among MMG clusters are surveyed.Furthermore,various energy scheduling and control strategies of MMGs for interactive energy trading,multi-energy management,and resilient operations are thoroughly analyzed and investigated.Lastly,some challenges with great importance in the future research are presented.

Coordinated Robust PID-based Damping Control of Permanent Magnet Synchronous Generators for Low-frequency Oscillations Considering Power System Operational Uncertainties

In recent years, with the growth of wind energy resources,the capability of wind farms to damp low-frequency oscillations(LFOs) has provided a notable advantage for the stabilityenhancement of the modern power grid. Meanwhile, owingto variations in the power system operating point (OP), thedamping characteristics of LFOs may be affected adversely. Inthis respect, this paper presents a coordinated robust proportional-integral-derivative (PID) based damping control approachfor permanent magnet synchronous generators (PMSGs)to effectively stabilize LFOs, while considering power system operationaluncertainties in the form of a polytopic model constructedby linearizing the power system under a given set ofOPs. The proposed approach works by modulating the DC-linkvoltage control loop of the grid-side converter (GSC) via a supplementaryPID controller, which is synthesized by transformingthe design problem into H-infinity static output feedback(SOF) control methodology. The solution of H-infinity SOF controlproblem involves satisfying linear matrix inequality (LMI)constraints based on the parameter-dependent Lyapunov functionto ensure asymptotic stability such that the minimal H-infinityperformance objective is simultaneously accomplished forthe entire polytope. The coordinated damping controllers forthe multiple wind farms are then designed sequentially by usingthe proposed approach. Eigenvalue analysis confirms the improveddamping characteristics of the closed-loop system forseveral representative OPs. Afterward, the simulation results, includingthe performance comparison with existing approaches,validate the higher robustness of the proposed approach for awide range of operating scenarios.

Small-signal Stability Criteria in Power Electronics-dominated Power Systems:A Comparative Review

To tackle emerging power system small-signal stability problems such as wideband oscillations induced by the large-scale integration of renewable energy and power electronics,it is crucial to review and compare existing small-signal stability analysis methods.On this basis,guidance can be provided on determining suitable analysis methods to solve relevant small-signal stability problems in power electronics-dominated power systems(PEDPSs).Various mature methods have been developed to analyze the small-signal stability of PEDPSs,including eigenvalue-based methods,Routh stability criterion,Nyquist/Bode plot based methods,passivity-based methods,positive-net-damping method,lumped impedance-based methods,bifurcation-based methods,etc.In this paper,the application conditions,advantages,and limitations of these criteria in identifying oscillation frequencies and stability margins are reviewed and compared to reveal and explain connections and discrepancies among them.Especially,efforts are devoted to mathematically proving the equivalence between these small-signal stability criteria.Finally,the performance of these criteria is demonstrated and compared in a 4-machine 2-area power system with a wind farm and an IEEE 39-bus power system with 3 wind farms.

Connection Between Damping Torque Analysis and Energy Flow Analysis in Damping Performance Evaluation for Electromechanical Oscillations in Power Systems

The damping performance evaluation for electromechanical oscillations in power systems is crucial for the stable operation of modern power systems.In this paper,the connection between two commonly-used damping performance evaluation methods,i.e.,the damping torque analysis(DTA)and energy flow analysis(EFA),are systematically examined and revealed for the better understanding of the oscillatory damping mechanism.First,a concept of the aggregated damping torque coefficient is proposed and derived based on DTA of multi-machine power systems,which can characterize the integration effect of the damping contribution from the whole power system.Then,the pre-processing of measurements at the terminal of a local generator is conducted for EFA,and a concept of the frequency-decomposed energy attenuation coefficient is defined to screen the damping contribution with respect to the interested frequency.On this basis,the frequency spectrum analysis of the energy attenuation coefficient is employed to rigorously prove that the results of DTA and EFA are essentially equivalent,which is valid for arbitrary types of synchronous generator models in multi-machine power systems.Additionally,the consistency between the aggregated damping torque coefficient and frequency-decomposed energy attenuation coefficient is further verified by the numerical calculation in case studies.The relationship between the proposed coefficients and the eigenvalue(or damping ratio)is finally revealed,which consolidates the application of the proposed concepts in the damping performance evaluation.

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.

Probabilistic Residential Load Forecasting with Sequence-to-sequence Adversarial Domain Adaptation Networks

Lately,the power demand of consumers is increasing in distribution networks,while renewable power generation keeps penetrating into the distribution networks.Insufficient data make it hard to accurately predict the new residential load or newly built apartments with volatile and changing time-series characteristics in terms of frequency and magnitude.Hence,this paper proposes a short-term probabilistic residential load forecasting scheme based on transfer learning and deep learning techniques.First,we formulate the short-term probabilistic residential load forecasting problem.Then,we propose a sequence-to-sequence(Seq2Seq)adversarial domain adaptation network and its joint training strategy to transfer generic features from the source domain(with massive consumption records of regular loads)to the target domain(with limited observations of new residential loads)and simultaneously minimize the domain difference and forecasting errors when solving the forecasting problem.For implementation,the dominant techniques or elements are used as the submodules of the Seq2Seq adversarial domain adaptation network,including the Seq2Seq recurrent neural networks(RNNs)composed of a long short-term memory(LSTM)encoder and an LSTM decoder,and quantile loss.Finally,this study conducts the case studies via multiple evaluation indices,comparative methods of classic machine learning and advanced deep learning,and various available data of the new residentical loads and other regular loads.The experimental results validate the effectiveness and stability of the proposed scheme.