Smart Microgrids: Overview and Outlook

The idea of changing our energy system from a hierarchical design into a set of nearly independent microgrids becomes feasible with the availability of small renewable energy generators. The smart microgrid concept comes with several challenges in research and engineering targeting load balancing, pricing, consumer integration and home automation. In this paper we first provide an overview on these challenges and present approaches that target the problems identified. While there exist promising algorithms for the particular field, we see a missing integration which specifically targets smart microgrids. Therefore, we propose an architecture that integrates the presented approaches and defines interfaces between the identified components such as generators, storage, smart and \dq{dumb} devices.Comment: presented at the GI Informatik 2012, Braunschweig Germany, Smart Grid Worksho

Practical applications of multi-agent systems in electric power systems

The transformation of energy networks from passive to active systems requires the embedding of intelligence within the network. One suitable approach to integrating distributed intelligent systems is multi-agent systems technology, where components of functionality run as autonomous agents capable of interaction through messaging. This provides loose coupling between components that can benefit the complex systems envisioned for the smart grid. This paper reviews the key milestones of demonstrated agent systems in the power industry and considers which aspects of agent design must still be addressed for widespread application of agent technology to occur

Transactive Energy and Flexibility Provision in Multi-microgrids using Stackelberg Game

Aggregating the demand side flexibility is essential to complementing the inflexible and variable renewable energy supply in achieving low carbon energy systems. Sources of demand side flexibility, e.g., dispatchable generators, storages, and flexible loads, can be structured in a form of microgrids and collectively provided to utility grids through transactive energy in local energy markets. This paper proposes a framework of local energy markets to manage the transactive energy and facilitate the flexibility provision. The distribution system operator aims to achieve local energy balance by scheduling the operation of multi-microgrids and determining the imbalance prices. Multiple microgrid traders aim to maximise profits for their prosumers through dispatching flexibility sources and participating in localised energy trading. The decision making and interactions between a distribution system operator and multiple microgrid traders are formulated as the Stackelberg game-theoretic problem. Case studies using the IEEE 69-bus distribution system demonstrate the effectiveness of the developed model in terms of facilitating the local energy balance and reducing the dependency on the utility grids

Computational intelligence approaches for energy load forecasting in smart energy management grids: state of the art, future challenges, and research directions and Research Directions

Energy management systems are designed to monitor, optimize, and control the smart grid energy market. Demand-side management, considered as an essential part of the energy management system, can enable utility market operators to make better management decisions for energy trading between consumers and the operator. In this system, a priori knowledge about the energy load pattern can help reshape the load and cut the energy demand curve, thus allowing a better management and distribution of the energy in smart grid energy systems. Designing a computationally intelligent load forecasting (ILF) system is often a primary goal of energy demand management. This study explores the state of the art of computationally intelligent (i.e., machine learning) methods that are applied in load forecasting in terms of their classification and evaluation for sustainable operation of the overall energy management system. More than 50 research papers related to the subject identified in existing literature are classified into two categories: namely the single and the hybrid computational intelligence (CI)-based load forecasting technique. The advantages and disadvantages of each individual techniques also discussed to encapsulate them into the perspective into the energy management research. The identified methods have been further investigated by a qualitative analysis based on the accuracy of the prediction, which confirms the dominance of hybrid forecasting methods, which are often applied as metaheurstic algorithms considering the different optimization techniques over single model approaches. Based on extensive surveys, the review paper predicts a continuous future expansion of such literature on different CI approaches and their optimizations with both heuristic and metaheuristic methods used for energy load forecasting and their potential utilization in real-time smart energy management grids to address future challenges in energy demand managemen

Artificial Intelligence and Machine Learning Approaches to Energy Demand-Side Response: A Systematic Review

Recent years have seen an increasing interest in Demand Response (DR) as a means to provide flexibility, and hence improve the reliability of energy systems in a cost-effective way. Yet, the high complexity of the tasks associated with DR, combined with their use of large-scale data and the frequent need for near real-time de-cisions, means that Artificial Intelligence (AI) and Machine Learning (ML) — a branch of AI — have recently emerged as key technologies for enabling demand-side response. AI methods can be used to tackle various challenges, ranging from selecting the optimal set of consumers to respond, learning their attributes and pref-erences, dynamic pricing, scheduling and control of devices, learning how to incentivise participants in the DR schemes and how to reward them in a fair and economically efficient way. This work provides an overview of AI methods utilised for DR applications, based on a systematic review of over 160 papers, 40 companies and commercial initiatives, and 21 large-scale projects. The papers are classified with regards to both the AI/ML algorithm(s) used and the application area in energy DR. Next, commercial initiatives are presented (including both start-ups and established companies) and large-scale innovation projects, where AI methods have been used for energy DR. The paper concludes with a discussion of advantages and potential limitations of reviewed AI techniques for different DR tasks, and outlines directions for future research in this fast-growing area

Dynamic, Stochastic, Computational, and Scalable Technologies for Smart Grids

The smart electric power grid will evolve into a very complex adaptive system under semi-autonomous distributed control. its spatial and temporal complexity, non-convexity, non-linearity, non-stationarity, variability and uncertainties exceed the characteristics found in today\u27s traditional power system. the distributed integration of intermittent sources of energy and plug-in electric vehicles to a smart grid further adds complexity and challenges to its modeling, control and optimization. Innovative technologies are needed to handle the growing complexity of the smart grid and stochastic bidirectional optimal power flows, to maximize the penetration of renewable energy, and to provide maximum utilization of available energy storage, especially plugin electric vehicles. Smart grids will need to be monitored continuously to maintain stability, reliability and efficiency under normal and abnormal operating conditions and disturbances. a combination of capabilities for system state prediction, dynamic stochastic power flow, system optimization, and solution checking will be necessary. the optimization and control systems for a smart-grid environment will require a computational systems thinking machine to handle the uncertainties and variability that exist. the importance and contributions of the computational intelligence field for developing the dynamic, stochastic, computational, and scalable technologies needed for sensemaking, situational awareness, control and optimization in smart grids are presented in this paper. © 2011 IEEE

A transition from manual to Intelligent Automated power system operation -A Indicative Review

This paper reviews the transition of the power system operation from the traditional manual mode of power system operations to the level where automation using Internet of Things (IOT) and intelligence using Artificial Intelligence (AI) is implemented. To make the review paper brief only indicative papers are chosen to cover multiple power system operation based implementation. Care is taken there is lesser repeatation of similar technology or application be reviewed. The indicative review is to take only a representative literature to bypass scrutinizing multiple literatures with similar objectives and methods. A brief review of the slow transition from the traditional to the intelligent automated way of carrying out power system operations like the energy audit, load forecasting, fault detection, power quality control, smart grid technology, islanding detection, energy management etc is discussed .The Mechanical Engineering Perspective on the basis of applications would be noticed in the paper although the energy management and power delivery concepts are electrical