Future industrial systems can be realized using the cyber–physical systems (CPSs) that advocate the coexistence of cyber and physical counterparts in a network structure to perform the system’s functions in a collaborative manner. Multiagent systems share common ground with CPSs and can empower them with a multitude of capabilities in their efforts to achieve complexity management, decentralization, intelligence, modularity, flexibility, robustness, adaptation, and responsiveness. This work surveys and analyzes the current state of the industrial application of agent technology in CPSs, and provides a vision on the way agents can effectively enable emerging CPS challenges.

/pdf/smart-agents-in-industrial-cyber-physical-systems-11yp9o0u5x.pdf

Smart Agents in Industrial Cyber–Physical Systems

In last few years, many countries in the world have shown huge interest in smart grid technology. They are facing many challenges in the process of deployment of this technology at ground level. Hence a planned research is required to meet those challenges within time. This paper provides a detailed description of progress in the field of demand side management, demand response programs, distributed generation, technical issues in the way of their progress and key advantages, which will be received after the final deployment of these programs. Renewable energy resources are also becoming a main part of distributed generation, which provides a solution for environmental problems caused by conventional power plants. Few countries are working on the deployment of the advanced metering system. Along with this, the scope of research in various programs of smart grid technology has been explored.

Smart operations of smart grids integrated with distributed generation: A review

Modern power systems face different challenges such as the ever-increasing electrical energy demand, the massive growth of renewable energy with distributed generations, the large-scale Internet of Things (IoT) devices adaptation, the emerging cyber-physical security threats, and the main goal of maintaining the system’s stability and reliability. These challenges pose extreme pressure on finding advanced technologies and sustainable solutions for secure and reliable operations of the power system. The blockchain is one of the recent technologies that have gained lots of attention in different applications including smart grid for its uniqueness and decentralized nature. In the last few years, this technology grew a momentum specifically with the cryptocurrencies’ industry such as the Bitcoin and Etherium. The Blockchain’s applications in the smart grids could offer many innovative and affordable solutions to some of the challenges that the future and the current smart grids will be facing. This paper reviews different prospects, advantages, approaches, and technical challenges of utilizing the blockchain technology in the smart grid, and presents frameworks for key smart grid blockchain-based applications; more specifically, it is shown that how the blockchain can be used as the smart grid’s cyber-physical layer.

/pdf/blockchain-applications-in-smart-grid-review-and-frameworks-3cbn4kp0tx.pdf

Blockchain Applications in Smart Grid–Review and Frameworks

As a critical indictor in the Battery Management System (BMS), State of Charge (SOC) is closely related to the reliable and safe operation of lithium-ion (Li-ion) batteries. Model-based methods are an effective solution for accurate and robust SOC estimation, the performance of which heavily relies on the battery model. This paper mainly focuses on battery modeling methods, which have the potential to be used in a model-based SOC estimation structure. Battery modeling methods are classified into four categories on the basis of their theoretical foundations, and their expressions and features are detailed. Furthermore, the four battery modeling methods are compared in terms of their pros and cons. Future research directions are also presented. In addition, after optimizing the parameters of the battery models by a Genetic Algorithm (GA), four typical battery models including a combined model, two RC Equivalent Circuit Model (ECM), a Single Particle Model (SPM), and a Support Vector Machine (SVM) battery model are compared in terms of their accuracy and execution time.

/pdf/overview-of-lithium-ion-battery-modeling-methods-for-state-c5znbaf31q.pdf

Overview of Lithium-Ion battery modeling methods for state-of-charge estimation in electrical vehicles

Microgrids are energy systems that aggregate distributed energy resources, loads, and power electronics devices in a stable and balanced way. They rely on energy management systems to schedule optimally the distributed energy resources. Conventionally, many scheduling problems have been solved by using complex algorithms that, even so, do not consider the operation of the distributed energy resources. This paper presents the modeling and design of a modular energy management system and its integration to a grid-connected battery-based microgrid. The scheduling model is a power generation-side strategy, defined as a general mixed-integer linear programming by taking into account two stages for proper charging of the storage units. This model is considered as a deterministic problem that aims to minimize operating costs and promote self-consumption based on 24-hour ahead forecast data. The operation of the microgrid is complemented with a supervisory control stage that compensates any mismatch between the offline scheduling process and the real time microgrid operation. The proposal has been tested experimentally in a hybrid microgrid at the Microgrid Research Laboratory, Aalborg University.

/pdf/mixed-integer-linear-programming-based-energy-management-1sbza5f17c.pdf

Mixed-Integer-Linear-Programming-Based Energy Management System for Hybrid PV-Wind-Battery Microgrids: Modeling, Design, and Experimental Verification

Renewable energy sources are one key enabler to decrease greenhouse gas emissions and to cope with the anthropogenic climate change. Their intermittent behavior and limited storage capabilities present a new challenge to power system operators to maintain power quality and reliability. Additional technical complexity arises from the large number of small distributed generation units and their allocation within the power system. Market liberalization and changing regulatory framework lead to additional organizational complexity. As a result, the design and operation of the future electric energy system have to be redefined. Sophisticated information and communication architectures, automation concepts, and control approaches are necessary in order to manage the higher complexity of so-called smart grids. This paper provides an overview of the state of the art and recent developments enabling higher intelligence in future smart grids. The integration of renewable sources and storage systems into the power grids is analyzed. Energy management and demand response methods and important automation paradigms and domain standards are also reviewed.

/pdf/a-review-of-architectures-and-concepts-for-intelligence-in-q9us8r0svw.pdf

A Review of Architectures and Concepts for Intelligence in Future Electric Energy Systems

For this contribution a generic and real time capable battery model was implemented within a dedicated Power Hardware-in-the-Loop (PHIL) simulation environment. This was done in order to investigate its implementability and accuracy for PHIL simulations. PHIL simulations offers major benefits to battery inverter tests as reproducibility increases and the preparation time can be reduced significantly. Following a discussion of real time capable battery models, the implemented model is validated with measurement data of a real battery. Finally a PHIL simulation of a battery model is carried out and its applicability is shown.

Power hardware-in-the-loop implementation and verification of a real time capable battery model

For this contribution different generic battery models were implemented in a dedicated simulation environment to investigate their usability for Power Hardware-in-the-loop implementations. Following a discussion of various requirements on these models, their validity is determined by comparison with a measured battery test. The applicability of the models to Power Hardware-in-the-loop applications is evaluated on the basis of its execution time and its deviation of the model from the real battery to achieve values representative of embedding the model in a larger simulation.

Selection and implementation of a generic battery model for PHIL applications

Energy storage systems will play a major role in the decarbonization of future sustainable electric power systems, allowing a high penetration of distributed renewable energy sources and contributing to the distribution network stability and reliability. To accomplish this, a storage system is required to provide multiple services such as self-consumption, grid support, peak-shaving, etc. The simultaneous activation of controllers operation may lead to conflicts, as a consequence the execution of committed services is not guaranteed. This paper presents and discusses a solution to the exposed issue by developing an engineering support approach to semi-automatically detect and handle conflicts for multi-usage storage systems applications. To accomplish that an ontology is developed and exploited by model-driven engineering mechanisms. The proposed approach is evaluated by implementing a use case example, where detection of conflicts is automatically done at an early design stage. Besides this, exploitable source code for conflicts resolution is generated and used during the design and prototype stages of controllers development. Thus, the proposed engineering support enhances the design and development of storage system controllers, especially for multi-usage applications.

Engineering Support for Handling Controller Conflicts in Energy Storage Systems Applications

Battery Energy Storage Systems (BESS) are starting to play an important role in today’s power distribution networks. They provide a manifold of services for fulfilling demands and requests from diverse stakeholders, such as distribution system operators, energy market operators, aggregators but also end-users. Such services are usually provided by corresponding Energy Management Systems (EMS). This paper analyzes the complexity of the EMS development process resulting from an evolving power utility automation.

Rapid Prototyping of Multi-Functional Battery Energy Storage System Applications

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