Pierluigi Siano

A Multilevel Inverter for Photovoltaic Systems With Fuzzy Logic Control

Converters for photovoltaic (PV) systems usually consist of two stages: a dc/dc booster and a pulsewidth modulated (PWM) inverter. This cascade of converters presents efficiency issues, interactions between its stages, and problems with the maximum power point tracking. Therefore, only part of the produced electrical energy is utilized. In this paper, the authors propose a single-phase H-bridge multilevel converter for PV systems governed by a new integrated fuzzy logic controller (FLC)/modulator. The novelties of the proposed system are the use of a fully FLC (not requiring any optimal PWM switching-angle generator and proportional-integral controller) and the use of an H-bridge power-sharing algorithm. Most of the required signal processing is performed by a mixed-mode field-programmable gate array, resulting in a fully integrated System-on-Chip controller. The general architecture of the system and its main performance in a large spectrum of practical situations are presented and discussed. The proposed system offers improved performance over two-level inverters, particularly at low-medium power.

Evaluating the Impact of Network Investment Deferral on Distributed Generation Expansion

Distributed generation (DG) can offer an alternative planning approach to utilities to satisfy demand growth and distribution network security, planning and management issues. However, an appropriate framework is required to foster the integration of DG within grid network planning, thus avoiding potential inefficiencies in electricity supply infrastructure. In this work, in order to capture the effects of network investment deferral on DG expansion, different regulations for distribution network operators (DNOs) ownership of DG and how they influence the optimal connection of new generation within existing networks are examined. Using a multiyear multiperiod optimal power flow, DNOs preference for the siting and sizing of DG installation are analyzed.

Smart Operation of Wind Turbines and Diesel Generators According to Economic Criteria

This paper proposes an innovative system for Smart Grid (SG) management aiming at minimizing the total costs supported for carrying out the delivery of energy to consumers. These costs include the production costs of distributed generators, the cost of the power provided by the primary substation, and the cost associated with grid power losses. After a brief overview on the main SG aspects, this paper describes the proposed approach that makes use of an optimal power flow algorithm and the active management schemes. The efficiency of the method is verified on a distribution system comprising wind turbines and diesel generators, considering the time-varying characteristics of the load demand and wind power generation.

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

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.

Designing an Adaptive Fuzzy Controller for Maximum Wind Energy Extraction

The wind power production spreading, also aided by the transition from constant to variable speed operation, involves the development of efficient control systems to improve the effectiveness of power production systems. This paper presents a data-driven design methodology able to generate a Takagi-Sugeno-Kang (TSK) fuzzy model for maximum energy extraction from variable speed wind turbines. In order to obtain the TSK model, fuzzy clustering methods for partitioning the input-output space, combined with genetic algorithms, and recursive least-squares optimization methods for model parameter adaptation are used. The implemented TSK fuzzy model, as confirmed by some simulation results on a doubly fed induction generator connected to a power system, exhibits high speed of computation, low memory occupancy, fault tolerance, and learning capability.

Failure Identification in Smart Grids Based on Petri Net Modeling

This paper presents a method to identify and localize failures in smart grids. The method is based on a carefully designed Petri net (PN) that captures the modeling details of the protection system of the distribution network and allows the detection/identification of failures in data transmission and faults in the distribution network by means of simple matrix operations. The design of the PN model is carried out by carefully composing multiple PN models for single protection systems: Such an approach allows the identification of the faults despite possible strong penetration of distributed generation. In order to verify the method, two case studies are discussed. The results highlight that the proposed method can remove a lot of the complexity of the associated data analysis despite the possible presence of malfunctioning protection systems and misinformation due to communication and other errors.

A Review of Agent and Service-Oriented Concepts Applied to Intelligent Energy Systems

The intention of this paper is to provide an overview of using agent and service-oriented technologies in intelligent energy systems. It focuses mainly on ongoing research and development activities related to smart grids. Key challenges as a result of the massive deployment of distributed energy resources are discussed, such as aggregation, supply-demand balancing, electricity markets, as well as fault handling and diagnostics. Concepts and technologies like multiagent systems or service-oriented architectures are able to deal with future requirements supporting a flexible, intelligent, and active power grid management. This work monitors major achievements in the field and provides a brief overview of large-scale smart grid projects using agent and service-oriented principles. In addition, future trends in the digitalization of power grids are discussed covering the deployment of resource constrained devices and appropriate communication protocols. The employment of ontologies ensuring semantic interoperability as well as the improvement of security issues related to smart grids is also discussed.

Distributed Generation Capacity Evaluation Using Combined Genetic Algorithm and OPF

A range of techniques has been proposed to define the optimal locations and capacities of distributed generation (DG) as a means of ensuring that the maximum amount of DG can be connected to existing and future networks. However, there are limitations inherent in these methods, not least in finding the best combination of sites for connecting a predefined number of DGs. Here, a method combining optimal power flow and genetic algorithms aims to meet this requirement. Its use would be in enabling Distribution Network Operators to search a network for the best sites and capacities available to strategically connect a defined number of DGs among a large number of potential combinations. Some applications of the proposed methodology confirmed its effectiveness in sitting and sizing an assigned number of DG units.

An overview on the smart grid concept

Smart Grid is a concept for transforming the electric power grid by using advanced automatic control and communications techniques and other forms of information technology. It integrates innovative tools and technologies from generation, transmission and distribution all the way to consumer appliances and equipment. This concept integrates energy infrastructure, processes, devices, information and markets into a coordinated and collaborative process that allows energy to be generated, distributed and consumed more effectively and efficiently. This paper reviews some researches and studies on Smart Grids (SGs) technology.

Stochastic Optimization of Wind Turbine Power Factor Using Stochastic Model of Wind Power

This paper proposes a stochastic optimization algorithm that aims to minimize the expectation of the system power losses by controlling wind turbine (WT) power factors. This objective of the optimization is subject to the probability constraints of bus voltage and line current requirements. The optimization algorithm utilizes the stochastic models of wind power generation (WPG) and load demand to take into account their stochastic variation. The stochastic model of WPG is developed on the basis of a limited autoregressive integrated moving average (LARIMA) model by introducing a cross-correlation structure to the LARIMA model. The proposed stochastic optimization is carried out on a 69-bus distribution system. Simulation results confirm that, under various combinations of WPG and load demand, the system power losses are considerably reduced with the optimal setting of WT power factor as compared to the case with unity power factor. Furthermore, an economic evaluation is carried out to quantify the value of power loss reduction. It is demonstrated that not only network operators but also WT owners can benefit from the optimal power factor setting, as WT owners can pay a much lower energy transfer fee to the network operators.