The promotion of recent critical load securing of power system research has been directed towards centralized commands and control functions. This paper presents a multi-agent based critical load securing in a PV based microgrid. For the trustworthy operation of critical buildings, the reliability, efficiency and security of the power system should be guaranteed. At present, to increase the security and reliability of electricity supply there is a need to design a distributed and autonomous subset of a larger grid or a microgrid. This work also clearly discusses the modelling and simulation of specialized microgrid called an Intelligent Distributed Autonomous Power Systems (IDAPS). The ID-APS microgrid plays a crucial role in constructing power grid that facilitate use of renewable energy technologies. IDAPS microgrid comprising of solar photovoltaic as distributed energy resources, different loads and their control algorithms, has been developed. Several case studies have been simulated to evaluate the operation of the IDAPS microgrid during parallel, islanded mode operation and securing critical loads during emergency.
A large amount of work has been taken place, if we talk about forecasting in the fields of power system. Various reforms in the existing techniques have proved to be helpful in providing guidance to researchers for developing efficient algorithms exhibiting greater accuracy. This paper presents three forecasting models viz. three-daytrained Support Vector Regression model and parameter optimized Support Vector Regression using Genetic Algorithm (SVRGA) and that using Particle Swarm Optimization (SVRPSO). Unlike existing models, these models accomplish accurate forecasting by optimizing the regularized structural risk function. The models make use of previous three days hourly load data for predicting next day hourly load. This paper performs a comparative study between GA and PSO on the grounds of optimization of the hyperparameters of SVR model.
This paper presents a cloud energy storage (CES) architecture for reducing energy costs for residential microgrid users. The former of this article concentrates on identifying an appropriate battery technology from various battery technologies with the aid of a simulation study. The later part addresses the economic feasibility of the storage architecture with three different scenarios namely grid connected energy storage, distributed energy storage (DES) and CES. The performance of the proposed architecture has been evaluated by considering five residential users with suitable battery technology identified from the former part of the study. For the purpose of the analysis, PV and load profiles including seasonal effects and grid price were taken from IIT Mumbai, India and IEX portal, respectively. In addition, this article also examines the impact of increased number of users with CES. The value of this study is that the proposed CES architecture is capable of reducing the cost of electricity experienced by the user by 11.37% as compared to DES. With this, CES operator's revenue can be increased by 6.70% in summer and 16.97% in winter in the case of fixed number of users. Finally, based on the analysis and simulation results, this paper recommends CES with Li-ion battery technology for residential application.
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