A Review of Architectures and Concepts for Intelligence in Future Electric Energy Systems <s> B. Necessary Smart Grid Functions and Services <s> This is the first part of a two-part paper that has arisen from the work of the IEEE Power Engineering Society's Multi-Agent Systems (MAS) Working Group. Part I of this paper examines the potential value of MAS technology to the power industry. In terms of contribution, it describes fundamental concepts and approaches within the field of multi-agent systems that are appropriate to power engineering applications. As well as presenting a comprehensive review of the meaningful power engineering applications for which MAS are being investigated, it also defines the technical issues which must be addressed in order to accelerate and facilitate the uptake of the technology within the power and energy sector. Part II of this paper explores the decisions inherent in engineering multi-agent systems for applications in the power and energy sector and offers guidance and recommendations on how MAS can be designed and implemented. <s> BIB001 </s> A Review of Architectures and Concepts for Intelligence in Future Electric Energy Systems <s> B. Necessary Smart Grid Functions and Services <s> In this paper, a generalized formulation for intelligent energy management of a microgrid is proposed using artificial intelligence techniques jointly with linear-programming-based multiobjective optimization. The proposed multiobjective intelligent energy management aims to minimize the operation cost and the environmental impact of a microgrid, taking into account its preoperational variables as future availability of renewable energies and load demand (LD). An artificial neural network ensemble is developed to predict 24-h-ahead photovoltaic generation and 1-h-ahead wind power generation and LD. The proposed machine learning is characterized by enhanced learning model and generalization capability. The efficiency of the microgrid operation strongly depends on the battery scheduling process, which cannot be achieved through conventional optimization formulation. In this paper, a fuzzy logic expert system is used for battery scheduling. The proposed approach can handle uncertainties regarding to the fuzzy environment of the overall microgrid operation and the uncertainty related to the forecasted parameters. The results show considerable minimization on operation cost and emission level compared to literature microgrid energy management approaches based on opportunity charging and Heuristic Flowchart (HF) battery management. <s> BIB002
The management and operation of the future power system and its components-particularly active power distribution grids and microgrids BIB002 -require new and advanced control functions. They have to be integrated in the corresponding automation solutions, intelligent electronic devices, and grid components. A brief overview of the most important functions and services is given by the following list  ,  - BIB001 . • Advanced monitoring and diagnostics: Monitoring and state estimation capability and real-time condition monitoring of components not only in the medium-voltage but also in the low-voltage distribution grids are important future features. Self-diagnostic capabilities are helpful new functions, which are valuable in a power grid with a large amount of DERs, flexible loads, and storage systems. • Optimization/self-optimization capabilities: Fluctuating electricity generation from renewable sources requires the ability to (self-)optimize the operation in medium-and low-voltage distribution grids. In addition, the availability of flexible loads and storage systems add additional degrees of freedom for operating future smart grids. • Automatic grid (topology) reconfiguration: Support of automatic or semiautomatic adjustment of the distribution grid topology due to optimization purposes (e.g., maximum amount of distributed generators and flexible loads) or due to fault management and power system restoration. • Adaptive protection: Automatic or semiautomatic adaption of protection devices (e.g., protection relays and breakers) with respect to the actual power grid conditions (e.g., adaptation of the protection system settings due to the bidirectional power flow caused by DERs). • Distributed power system management: Distributed control with automatic decision finding processes and proactive fault prevention have to be provided for the power system infrastructure operators in medium-and low-voltage grids. • Islanding possibilities/microgrids: Local operation of islands/microgrids can improve the availability of the electricity supply due to failures on higher voltage levels. • Distributed generation/distributed energy resources with ancillary services: Usage of ancillary services provided by DER (e.g., local voltage or frequency control and virtual inertia) improves power grid optimization (e.g., enhancing the hosting capacity of DER in power distribution grids). • Demand response/energy management support: Electric loads and ESS provide additional flexibility in power system operation. Energy management and DR principles and smart functions have to be provided by the used ICT infrastructure. • Advanced forecasting support: Forecasting of (distributed) generation and load profiles for optimized grid operation. • Self-healing: Automatic or semiautomatic restoration of grid operation in case of component/grid faults helps power system and infrastructure operators. • Asset management/condition-dependent power system maintenance: Preventive maintenance according to component/device conditions and remaining lifetime.