An Introduction to MultiAgent Systems.

Multiagent Systems is the title of a collection of papers dedicated to surveying specific themes of Multiagent Systems (MAS) and Distributed Artificial Intelligence (DAI). All of them authored by leading researchers of this dynamic multidisciplinary field.

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Multiagent systems: a modern approach to distributed artificial intelligence

In recent years, due to the wide utilization of direct current (DC) power sources, such as solar photovoltaic (PV), fuel cells, different DC loads, high-level integration of different energy storage systems such as batteries, supercapacitors, DC microgrids have been gaining more importance. Furthermore, unlike conventional AC systems, DC microgrids do not have issues such as synchronization, harmonics, reactive power control, and frequency control. However, the incorporation of different distributed generators, such as PV, wind, fuel cell, loads, and energy storage devices in the common DC bus complicates the control of DC bus voltage as well as the power-sharing. In order to ensure the secure and safe operation of DC microgrids, different control techniques, such as centralized, decentralized, distributed, multilevel, and hierarchical control, are presented. The optimal planning of DC microgrids has an impact on operation and control algorithms; thus, coordination among them is required. A detailed review of the planning, operation, and control of DC microgrids is missing in the existing literature. Thus, this article documents developments in the planning, operation, and control of DC microgrids covered in research in the past 15 years. DC microgrid planning, operation, and control challenges and opportunities are discussed. Different planning, control, and operation methods are well documented with their advantages and disadvantages to provide an excellent foundation for industry personnel and researchers. Power-sharing and energy management operation, control, and planning issues are summarized for both grid-connected and islanded DC microgrids. Also, key research areas in DC microgrid planning, operation, and control are identified to adopt cutting-edge technologies. This review explicitly helps readers understand existing developments on DC microgrid planning, operation, and control as well as identify the need for additional research in order to further contribute to the topic.

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DC Microgrid Planning, Operation, and Control: A Comprehensive Review

Review of major approaches to analyze vulnerability in power system

Irregular human behaviors and univariate datasets remain as two main obstacles of data-driven energy consumption predictions for individual households. In this study, a hybrid deep learning model is proposed combining an ensemble long short term memory (LSTM) neural network with the stationary wavelet transform (SWT) technique. The SWT alleviates the volatility and increases the data dimensions, which potentially help improve the LSTM forecasting accuracy. Moreover, the ensemble LSTM neural network further enhances the forecasting performance of the proposed method. Verification experiments were performed based on a real-world household energy consumption dataset collected by the ‘UK-DALE project. The results show that, with a competitive training efficiency, the proposed method outperforms all compared state-of-art methods, including the persistent method, support vector regression (SVR), long short term memory (LSTM) neural network and convolutional neural network combining long short term memory (CNN-LSTM), with different step sizes at 5, 10, 20 and 30 minutes, using three error metrics.

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A Hybrid LSTM Neural Network for Energy Consumption Forecasting of Individual Households

The power system is presently experiencing vital changes: it is advancing from a centralized structure to a decentralized one, primarily because of the enormous advancement of distributed renewable energy sources, so future power system obliges new control strategies. These systems must have the capacity to withstand new requirements, for example, the exceedingly disseminated nature, the irregularity of renewable energy sources and the restricted data transfer capacity for communications. Multi agent systems (MAS) have attributes that meet these prerequisites. A certain degree of distributed or collective intelligence can be accomplished through the connection of these agents with one another, participating or contending to achieve their objectives. This paper introduces outline of the fundamental ideas of MAS and its different platforms. Also, it provides a comprehensive survey on the power system applications in which MAS technique has been applied. For each power system application, technical details are also discussed.

Multi agent system: concepts, platforms and applications in power systems

After environmental crisis, Electric Vehicles (EV) are the best alternative of the Internal Combustion Engine (ICE) vehicle But focusing only on EV is not only sufficient as Electrical Vehicle Charging Station (EVCS) is equally important for deploying these vehicles Since these vehicles are electrically powered there are certain challenges in installing these charging stations Former major problem is grid overloading and load forecasting Latter is charging time and traffic-crowd management at charging stations This paper reviews about the basic terminologies of charging station like charging station types, levels To overcome these challenges, various technologies are discussed along with brief introduction of lithium ion batteries charging strategies and Battery Management System (BMS) Since, Indian Government is concentrating towards environmental friendly ecosystem and as per its mission to reduce carbon emission from the transport sector, deployment of EV's and installation of EVCS is the biggest concern Since, government has reduced the tax on EV's and providing subsidy for installing charging station, this is an easy task Hence, in this paper certain guidelines by the Ministry of Power and Ministry of Housing (Government of India) is discussed which can help an individual to set up a charging station at their end

Electric Vehicle Charging Station Challenges and Opportunities: A Future Perspective

This paper proposes a PV and wind output power generation forecasting agent for a multi-agent-based energy management system (EMS) in a smart microgrid. The microgrid EMS requires both generation forecast and load forecast to provide effective dispatch strategies. The efficiency of the EMS significantly relies on its forecasting accuracy. Firstly, this paper develops an adaptive neuro-fuzzy inference system (ANFIS)-based forecasting model and then utilise it for the development of wind and PV generation forecasting agent for microgrid energy management. ANFIS adopt the self-learning capability from the neural network and linguistic expression function from fuzzy logic inference and stands at the top of both the technologies in performance. The proposed model has been tested using two data sets, i.e., PV historical data and historical wind data. The fuzzy c means clustering (FCM) with hybrid optimisation algorithm-based ANFIS model shows better forecasting accuracy with both PV and wind forecast, therefore, implemented as PV and wind forecasting agent for microgrid EMS.

https://ietresearch.onlinelibrary.wiley.com/doi/pdf/10.1049/joe.2018.9323

FCM Clustering‐ANFIS‐based PV and wind generation forecasting agent for energy management in a smart microgrid

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 IDAPS 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

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Centralized multi-agent implementation for securing critical loads in PV based microgrid

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-day-trained 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 hyper-parameters of SVR model.

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A. Sujil

Papers

Multi agent system: concepts, platforms and applications in power systems

Electric Vehicle Charging Station Challenges and Opportunities: A Future Perspective

FCM Clustering‐ANFIS‐based PV and wind generation forecasting agent for energy management in a smart microgrid

Centralized multi-agent implementation for securing critical loads in PV based microgrid

Comparative Analysis of Intelligently Tuned Support Vector Regression Models for Short Term Load Forecasting in Smart Grid Framework