Alfredo Vaccaro

A Decentralized and Cooperative Architecture for Optimal Voltage Regulation in Smart Grids

Optimal voltage regulation is one of the main issues to address in a Smart Grid context. In this domain, the application of traditional hierarchical control paradigms has some disadvantages that could hinder their application in Smart Grids where the constant growth of grid complexity and the need for massive pervasion of distribution generation systems require more scalable and more flexible control and regulation paradigms. To try and overcome these challenges, this paper proposes the concept of a decentralized nonhierarchal voltage regulation architecture based on intelligent and cooperative smart entities. This paper intends to bring two main contributions to the existing literature. The first is the definition of a decentralized architecture aimed at computing the actual value of the cost function and its gradient without the need of a central fusion center acquiring and processing all the sensor acquisitions. The second is the proposal of a distributed and cooperative optimization strategy aimed at identifying the optimal asset of the voltage controllers.

Multiple-Energy Carriers: Modeling of Production, Delivery, and Consumption

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.

An Integrated Framework for Smart Microgrids Modeling, Monitoring, Control, Communication, and Verification

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).

Decentralized Economic Dispatch in Smart Grids by Self-Organizing Dynamic Agents

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.

An adaptive local learning-based methodology for voltage regulation in distribution networks with dispersed generation

This paper proposes a computational architecture for the voltage regulation of distribution networks equipped with dispersed generation systems (DGS). The architecture aims to find an effective solution of the optimal regulation problem by combining a conventional nonlinear programming algorithm with an adaptive local learning technique. The rationale for the approach is that a local learning algorithm can rapidly learn on the basis of a limited amount of historical observations the dependency between the current network state and the optimal asset allocation. This approach provides an approximate and fast alternative to an accurate but slow multiobjective optimization procedure. The experimental results obtained by simulating the regulation policy in the case of a medium-voltage network are very promising

An Affine Arithmetic-Based Methodology for Reliable Power Flow Analysis in the Presence of Data Uncertainty

Power flow studies are typically used to determine the steady state or operating conditions of power systems for specified sets of load and generation values, and is one of the most intensely used tools in power engineering. When the input conditions are uncertain, numerous scenarios need to be analyzed to cover the required range of uncertainty. Under such conditions, reliable solution algorithms that incorporate the effect of data uncertainty into the power flow analysis are required. To address this problem, this paper proposes a new solution methodology based on the use of affine arithmetic, which is an enhanced model for self-validated numerical analysis in which the quantities of interest are represented as affine combinations of certain primitive variables representing the sources of uncertainty in the data or approximations made during the computation. The application of this technique to the power flow problem is explained in detail, and several numerical results are presented and discussed, demonstrating the effectiveness of the proposed methodology, especially in comparison to previously proposed interval arithmetics techniques.

Optimisation of energy flow management in hybrid electric vehicles via genetic algorithms

Hybrid electric vehicles powertrain, combining electric motor with an auxiliary power unit, can offer a sensible improvement of the overall vehicle environmental impact achieving at the same time a rational energy employment. The main task of an energy flow management unit is to split the instantaneous vehicle power demand between the internal combustion engine and the electric motor ensuring that the power sources are operated at high efficiency operating points and the related vehicle emissions are minimised. This paper presents an original methodology for the tuning of the characteristic parameters. The proposed methodology identifies, using the genetic algorithm, the value of the energy flow management parameters that minimize the cost function in terms of fuel consumption and emissions. Some interesting simulation results are discussed to prove the validity of the methodology, which contributes to a substantial reduction of the pollutant emissions from hybrid electric vehicles.

Pervasive grid for large-scale power systems contingency analysis

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

A distributed architecture for online power systems security analysis

Phenomena that can compromise power systems operation need to be carefully analyzed in order to evaluate their impact on the security and reliability levels of the electrical networks. The real-time assessment of the systems security and reliability levels, especially under unforeseen contingencies, is known as online power system security analysis. For complex networks this process requires large computational efforts whereas computation times should be less than a few minutes for the information to be useful. To address this problem a distributed architecture based on the Web is proposed. The architecture integrates a network of remotely controlled units distributed in the most critical sections of the electrical network for fields data acquisition and safety check violations, a distributed solution engine for the online analysis of the system security, and a Web-based interface for graphical synoptic and reporting development. The results obtained from an intensive experimentation demonstrate the validity of the architecture and stimulate the enhancement of the solution engine through the use of a computational grid able to dynamically acquire the needed resources.

The role of pervasive and cooperative Sensor Networks in Smart Grids communication

The cornerstone of a Smart Grid is the ability for multiple entities to interact via communication networks. A scalable and pervasive communication infrastructure represents a crucial issue in both structuring and operating smart networks. In addressing this problem this paper figures out the potential role of cooperative Wireless Sensor Networks (WSNs). In detail, it analyses the performance of IEEE 802.15.4 based WSNs in order to establish their suitability for a typical set of monitoring and supervision functionalities required by urban-scale Smart Grids applications. The results obtained show that the application of this technology may be very promising in several Smart Grids applications as far as automation, remote monitoring and supervision are concerned.