Decentralized Energy Management of Networked Microgrid Based on Alternating-Direction Multiplier Method

With the ever-intensive utilization of distributed generators (DGs) and smart devices, distribution networks are evolving from a hierarchal structure to a distributed structure, which imposes significant challenges to network operators in system dispatch. A distributed energy-management method for a networked microgrid (NM) is proposed to coordinate a large number of DGs for maintaining secure and economic operations in the electricity-market environment. A second-order conic programming model is used to formulate the energy-management problem of an NM. Network decomposition was first carried out, and then a distributed solution for the established optimization model through invoking alternating-direction method of multipliers (ADMM). A modified IEEE 33-bus power system was finally utilized to demonstrate the performance of distributed energy management in an NM

Distant Supervision for Relation Extraction with Linear Attenuation Simulation and Non-IID Relevance Embedding

Distant supervision for relation extraction is an efficient method to reduce labor costs and has been widely used to seek novel relational facts in large corpora, which can be identified as a multi-instance multi-label problem. However, existing distant supervision methods suffer from selecting important words in the sentence and extracting valid sentences in the bag. Towards this end, we propose a novel approach to address these problems in this paper. Firstly, we propose a linear attenuation simulation to reflect the importance of words in the sentence with respect to the distances between entities and words. Secondly, we propose a non-independent and identically distributed (non-IID) relevance embedding to capture the relevance of sentences in the bag. Our method can not only capture complex information of words about hidden relations, but also express the mutual information of instances in the bag. Extensive experiments on a benchmark dataset have well-validated the effectiveness of the proposed method

Energy Cooperation Optimization in Residential Microgrid with Virtual Storage Technology

The power balance of the tie-line is crucial to the stable operation of a community microgrid. This paper presents a power fluctuation smoothing method of the microgrid tie-line based on virtual energy storage technology. Firstly, the structure characteristics and the energy coupling mode of the combined heat and power system is systematically analyzed. Considering the operating characteristics of heat pumps, micro gas turbines, and buildings’ heat storage characteristics, a virtual energy storage model is established. Secondly, the target power of the tie-line is determined with the storage state indexes into consideration. Subsequently, a power allocation strategy which takes into account the correction of equipment state mapping set is proposed to allocate the tie-line power fluctuations to heat pumps, micro gas turbines, and supercapacitors. Simulation results show this method can realize the coupling coordination between heat and power energy and ensure the smoothing effect of the power fluctuations. Meanwhile, the control flexibility of the combined heat and power system can be enhanced, and the microgrid’s operating economy can be improved

Communication‐resilient and convergence‐fast peer‐to‐peer energy trading scheme in a fully decentralized framework

Abstract The wide deployment of distributed energy resources, combined with a more proactive demand‐side management, is boosting the emergence of the peer‐to‐peer market. In the present study, an innovative peer‐to‐peer energy trading model is introduced, enabling a group of price‐setting prosumers to engage in direct negotiations via a straightforward best‐response approach. A Nash equilibrium problem (NEP) is initially formulated and a sufficient condition for the unique solution of the NEP is derived. Afterwards, an asynchronous and convergence‐fast solving method is employed to determine the trading quantity and price. The efficiency and resilience of the presented method are demonstrated through a comprehensive case study

An Early Warning Method of Distribution System Fault Risk Based on Data Mining

Accurate warning information of potential fault risk in the distribution network is essential to the economic operation as well as the rational allocation of maintenance resources. In this paper, we propose a fault risk warning method for a distribution system based on an improved RelieF-Softmax algorithm. Firstly, four categories including 24 fault features of the distribution system are determined through data investigation and preprocessing. Considering the frequency of distribution system faults, and then their consequences, the risk classification method of the distribution system is presented. Secondly, the K-maxmin clustering algorithm is introduced to improve the random sampling process, and then an improved RelieF feature extraction method is proposed to determine the optimal feature subset with the strongest correlation and minimum redundancy. Finally, the loss function of Softmax is improved to cope with the influence of sample imbalance on the prediction accuracy. The optimal feature subset and Softmax classifier are applied to forewarn the fault risk in the distribution system. The 191-feeder power distribution system in south China is employed to demonstrate the effectiveness of the proposed method

Evaluation of Accommodation Capability for Electric Vehicles of a Distribution System Considering Coordinated Charging Strategies

In recent years, the ever-increasing charging demand of electric vehicles (EVs) imposes challenges on both power supply security and reliability in the distribution system. In this paper, an EV accommodation capability evaluation model of a distribution system, with high penetrations of flexible resources, is established. Firstly, according to the actual classifications of EVs and transportation rules, a Monte Carlo simulation is used to simulate the charging behaviors of EVs so as to obtain the relevant parameters of EV charging. Then, a coordinated charging optimization model for various types of EVs is proposed based on the charging characteristics of EVs. The presented model comprises a mixed-integer linear programming problem and a constrained optimization problem which are respectively solved by CPLEX (the Simplex method implemented in the °C programming language) and the particle swarm optimization (PSO) algorithm. Last of all, a real-life distribution system in the coastal areas of China is served for demonstrating the feasibility and efficiency of the proposed approach. Moreover, the impacts of flexible resources, distribution network zoning rules, and EV growth on the EV accommodation capability of a distribution system are also discussed