One of the most promising flexible sources in decarbonized power systems is based on electric vehicles fleets equipped with V2G technology, which can exchange bidirectional power flows with the grid matching the system operator request, and providing valuable ancillary services, as far as frequency and voltage regulation are concerned. Providing these services requires deploying resilient, highly scalable, plug-and-play, and privacy-preserving computing frameworks for charging/discharging orchestration according to the requested active and reactive power profiles at the point of connections. For this purpose, this paper proposes a decentralized and self-organizing scheme based on a Peer-to-Peer architecture, leveraging privacy-preserving through cooperative consensus protocols. The effectiveness of the proposed scheme is demonstrated by both theoretical and experimental studies, which have been obtained on a hardware testbed simulating a variable number of grid-connected vehicles under realistic operation scenarios.

The great diffusion of renewable energy sources is substituting traditional power plants, based on synchronous generators. This is leading to lower levels of inertia in modern power systems, thus reducing their frequency stability. In the context of future low-inertia systems, this paper introduces the novel concept of critical inertia, i.e. the minimum inertia needed at each network bus, in order to ensure the transient stability of the power system, and describes a methodology based on parametric identification for its assessment. The method is tested on a nine-bus power system and results related to different case studies are presented.

This paper outlines the potential role of Fuzzy Logic Control (FLC) for voltage-rise mitigation in power distribution networks in the presence of grid-connected photovoltaic (PV) systems. In particular, after analyzing the main performances of the traditional techniques currently adopted for voltage rise mitigation by reactive power compensation, a decentralized approach based on local fuzzy controllers is proposed to regulate the reactive power injected into the grid by the distributed PV systems. The proposed solution is based on a closed control loop, where the local voltage at the point of common coupling (PCC) is fed at input to the FLC to decide the amount of reactive power generated by the local PV systems. The results obtained on a realistic case study are presented and discussed in order to assess the benefits deriving by the application of the proposed approach.

In this paper, a fuzzy-based paradigm for data compression aimed at reducing the computational burden of data analysis in smart grids (SGs) is proposed. In the SG context, it is challenging achieving an efficient use of the channel communication bandwidth and a reduced need of the storage space for operational data. Thus, we discuss a fuzzy-based mathematical kernel which transforms the data into a new domain where their cardinality can be sensibly reduced, consequently allowing the development of more efficient data analysis algorithms. Detailed numerical results obtained from several test systems are presented and discussed in order to demonstrate the effectiveness of the proposed approach to handle SG operation problems.

Optimal Reactive Power Flow (ORPF) is one of the most fundamental and widely used tools in modern smart grids for improving the power system performance in terms of both operation cost and quality of service. This result is obtained by identifying the optimal set points of the voltage controllers, which improve the bus voltage profiles, minimise the regulation costs, and satisfy the power equipment constraints. In this context, the application of traditional optimisation methods is often complicated by the presence of multiple local minima, which could affect the performance and the convergence of iterative methods. To address this issue the adoption of evolutionary based paradigms has been recognised as one of the most promising enabling methodologies. Armed with such a vision, in this paper a novel solution method based on Search Group Algorithm (SGA) is proposed to solve the ORPF problem. As demonstrated by detailed experimental studies, which have been performed on IEEE 57-bus and 118-bus test power systems, the proposed evolutionary approach outperforms state-of-the-art evolutionary algorithms.

Synchronized sensing offered by wide area measurement systems (WAMSs) enables advanced investigations on modern electrical power systems not performable before their advent. One of them is overhead lines (OHL) temperature monitoring, which can be implemented in WAMS industrial domains thanks to the availability of newly accessible sensing information provided by phasor measurement units (PMUs). To accomplish this task, a proper sensing-based integrated intellectualization is necessary in order to face with the major WAMS issues and challenges. In line with these considerations, this paper proposes an advanced computing framework for real-time conductor temperature monitoring of fully transposed OHLs that is able to overcome the aforementioned challenges and issues. The validity and effectiveness of the conceptualized solution is verified via field tests on an Italian transmission line currently in operation. The obtained outcomes led the Italian Transmission System Operator (TSO) to equip its own Energy Management System (EMS) with the deployed solution.

The advance in the research of Smart Grid methodologies opens the doors toward the conceptualization of new tools aimed at effectively addressing most challenging issues of modern power distribution systems, including the massive pervasion of renewable power generators, the strictest power quality limits, the complex interactions with the energy markets, the raising levels of security and reliability constraints, and the need for maximizing the exploitation of existing electrical infrastructures

The concept of smart microgrid (SMG) is widely recognized as one of the most promising and enabling technologies for smart grids. In this context, the development of integrated and decentralized frameworks for executing complex control and monitoring applications is still in its infancy and needs to be researched. To address this issue, this paper conceptualizes a self-organizing computing framework, based on self-organizing agents, for solving the fundamental control and monitoring problems of an SMG without the need for centralized data acquisition and processing. Simulation results obtained for an 18-bus test network are presented and discussed in order to demonstrate the significance and validity of the proposed new framework.

In this paper, we propose a decentralized and self-organizing solution framework aimed at addressing economic dispatch (ED) analysis in a distributed scenario. In particular we will demonstrate that, under some hypotheses, the solution of the ED analysis can be obtained by computing proper weighted averages of the variable of interests. To compute these global quantities we propose the deployment of a network of cooperative dynamic agents solving distributed average consensus problems. Thanks to this decentralized/nonhierarchical paradigm, all the basic operations needed to solve the economic dispatch problem could be easily processed by the agents. Simulation results obtained on the 118 and 300 bus IEEE test networks are presented and discussed in order to prove the effectiveness of the proposed framework.

The massive pervasion of distributed generators in modern smart grids makes hierarchical voltage control paradigms unsuitable. Fuzzy logic and fuzzy agent based architecture is considered to be a promising research approach to address voltage control problem in smart grids. Following the said approach, this paper proposes decentralized and non-hierarchal voltage control architecture based on cooperative fuzzy agents. Similarly to self-organizing biological populations, the voltage control is achieved by the local interaction of fuzzy agents as that of the theory distributed consensus. This allows control agents to decide that when reactive power flow injection in the network is more useful in case of global network conditions. Simulation results obtained on the 30 bus IEEE test network are presented and discussed in order to prove the effectiveness of the proposed solution.

This paper presents a generic framework for the modeling of energy systems comprising multiple-energy carriers, such as electricity, heat, gas, biomass, etc. The modeling framework is based on the so-called energy hub approach. The core idea of the energy hub is the definition of a conversion matrix capable of describing the interactions of production, delivery, and consumption in multiple-energy carrier systems. Based on the energy hub concept a broad spectrum of modeling extensions and applications is presented, such as a multiple-energy carrier optimal power flow, risk management and investment analysis tools, agent-based control schemes for decentralized generation units as well as the possibility to analyze the influence of plug-in hybrid electric vehicles (PHEVs) on future energy systems. The paper is concluded with a section presenting the key benefits of the energy hub modeling framework, followed by a discussion on the main design principles generality, scalability, and modularity as well as a discussion on the possibility to follow top-down or bottom-up modeling strategies.

The microgrid (MG) paradigm is a new concept which is considered as a solution for addressing technical, economical, and environmental issues of modern power systems. The application of MG is the subject of extensive studies and experimental tests. It is recognized that there are a number of technical challenges concerning the operation, monitoring, control, and protection of MGs systems. In this respect, the rapid development of the information and communication technologies (ICTs) has opened the door for feasible and cost-effective solutions allowing more extensive intra- and interutility information exchange, diffusion, and open access to a wide range of real-time information. Consequently, the ICTs could represent a strategic tool in supporting effective MG operation. According to this statement, the paper proposes an advanced framework based on the service-oriented architectures for integrated MG modeling, monitoring, and control. The proposed framework is platform, language, and vendor independent, and thus it is an ideal candidate for an effective integration in existing energy management systems and distribution management systems (EMSs/DMSs).

Optimal control and management of power systems require extensive analyses of phenomena that can compromise their operation in order to evaluate their impact on the security and reliability levels of the electrical networks. For complex networks, this process, known as power systems contingencies analysis, requires large computational efforts, whereas computation times should be less than a few minutes for the information to be useful. Even though many architectures based on conventional parallel and distributed systems have been widely proposed in the literature, they are characterized by low extensibility, reusability, and scalability, and so, they require a sensible hardware upgrade when more computational resources are necessary. This event is not infrequent in power systems where the constant growth of the electrical network complexity and the need for larger security and reliability levels of the plant infrastructures lead to the need of more detailed contingency analysis in shorter times. To address this problem, this paper proposes a pervasive grid approach to define a user-friendly software infrastructure for data acquisition from electrical networks and for data processing in order to simulate possible contingencies in a real electrical network. The grid infrastructure adopts a brokering service, based on an economy-driven model, to satisfy the quality of service constraints specified by the user (i.e., a time deadline to simulate the contingencies). This paper also discusses the deployment of the infrastructure on a network of heterogeneous clusters and PCs to compute the contingency analysis of a realistic electrical network. The experimental results obtained demonstrate the effectiveness of the proposed solution and the potential role of grid computing in supporting intensive computations in power systems

Summary form only given. We dealt with the definition and implementation of a distributed Web-based architecture for the online power system security analysis. The architecture employs field data measurements to dynamically estimate the actual state of an electrical grid and solves, for each possible contingency, the power system state equations to classify critical contingencies that could lead to system vulnerabilities. To identify the critical contingencies a computational engine able to execute the power system state equations is implemented according to a variant of the master/slave pattern. This pattern is defined to easily leverage the hierarchical network topology of resources connected to the Internet. The pattern is implemented with both RMI and ProAactive, while the resulting framework is deployed on a network of heterogeneous clusters and used to compute the contingency analysis of a realistic electrical grid. The experimental results demonstrate that online security analysis can be executed in few minutes even for large network complexity.