A multi-objective optimization of the active and reactive resource scheduling at a distribution level in a smart grid context

In the traditional paradigm, the large power plants supply the reactive power required at a transmission level and the capacitors and transformer tap changer were also used at a distribution level. However, in a near future will be necessary to schedule both active and reactive power at a distribution level, due to the high number of resources connected in distribution levels. This paper proposes a new multi-objective methodology to deal with the optimal resource scheduling considering the distributed generation, electric vehicles and capacitor banks for the joint active and reactive power scheduling. The proposed methodology considers the minimization of the cost (economic perspective) of all distributed resources, and the minimization of the voltage magnitude difference (technical perspective) in all buses. The Pareto front is determined and a fuzzy-based mechanism is applied to present the best compromise solution. The proposed methodology has been tested in the 33-bus distribution network. The case study shows the results of three different scenarios for the economic, technical, and multi-objective perspectives, and the results demonstrated the importance of incorporating the reactive scheduling in the distribution network using the multi-objective perspective to obtain the best compromise solution for the economic and technical perspectives.

Cost allocation model for distribution networks considering high penetration of distributed energy resources

The high penetration of distributed energy resources (DER) in distribution networks and the competitive environment of electricity markets impose the use of new approaches in several domains. The network cost allocation, traditionally used in transmission networks, should be adapted and used in the distribution networks considering the specifications of the connected resources. The main goal is to develop a fairer methodology trying to distribute the distribution network use costs to all players which are using the network in each period. In this paper, a model considering different type of costs (fixed, losses, and congestion costs) is proposed comprising the use of a large set of DER, namely distributed generation (DG), demand response (DR) of direct load control type, energy storage systems (ESS), and electric vehicles with capability of discharging energy to the network, which is known as vehicle-to-grid (V2G). The proposed model includes three distinct phases of operation. The first phase of the model consists in an economic dispatch based on an AC optimal power flow (AC-OPF); in the second phase Kirschen's and Bialek's tracing algorithms are used and compared to evaluate the impact of each resource in the network. Finally, the MW-mile method is used in the third phase of the proposed model. A distribution network of 33 buses with large penetration of DER is used to illustrate the application of the proposed model.

Multi-agent simulation of competitive electricity markets: Autonomous systems cooperation for European market modeling

The electricity market restructuring, and its worldwide evolution into regional and even continental scales, along with the increasing necessity for an adequate integration of renewable energy sources, is resulting in a rising complexity in power systems operation. Several power system simulators have been developed in recent years with the purpose of helping operators, regulators, and involved players to understand and deal with this complex and constantly changing environment. The main contribution of this paper is given by the integration of several electricity market and power system models, respecting to the reality of different countries. This integration is done through the development of an upper ontology which integrates the essential concepts necessary to interpret all the available information. The continuous development of Multi-Agent System for Competitive Electricity Markets platform provides the means for the exemplification of the usefulness of this ontology. A case study using the proposed multi-agent platform is presented, considering a scenario based on real data that simulates the European Electricity Market environment, and comparing its performance using different market mechanisms. The main goal is to demonstrate the advantages that the integration of various market models and simulation platforms have for the study of the electricity markets’ evolution.

A multi-objective model for scheduling of short-term incentive-based demand response programs offered by electricity retailers

In this paper, we formulate the electricity retailers’ short-term decision-making problem in a liberalized retail market as a multi-objective optimization model. Retailers with light physical assets, such as generation and storage units in the distribution network, are considered. Following advances in smart grid technologies, electricity retailers are becoming able to employ incentive-based demand response (DR) programs in addition to their physical assets to effectively manage the risks of market price and load variations. In this model, the DR scheduling is performed simultaneously with the dispatch of generation and storage units. The ultimate goal is to find the optimal values of the hourly financial incentives offered to the end-users. The proposed model considers the capacity obligations imposed on retailers by the grid operator. The profit seeking retailer also has the objective to minimize the peak demand to avoid the high capacity charges in form of grid tariffs or penalties. The non-dominated sorting genetic algorithm II (NSGA-II) is used to solve the multi-objective problem. It is a fast and elitist multi-objective evolutionary algorithm. A case study is solved to illustrate the efficient performance of the proposed methodology. Simulation results show the effectiveness of the model for designing the incentive-based DR programs and indicate the efficiency of NSGA-II in solving the retailers’ multi-objective problem.

Six thinking hats: A novel metalearner for intelligent decision support in electricity markets

The energy sector has suffered a significant restructuring that has increased the complexity in electricity market players' interactions. The complexity that these changes brought requires the creation of decision support tools to facilitate the study and understanding of these markets. The Multiagent Simulator of Competitive Electricity Markets (MASCEM) arose in this context, providing a simulation framework for deregulated electricity markets. The Adaptive Learning strategic Bidding System (ALBidS) is a multiagent system created to provide decision support to market negotiating players. Fully integrated with MASCEM, ALBidS considers several different strategic methodologies based on highly distinct approaches. Six Thinking Hats (STH) is a powerful technique used to look at decisions from different perspectives, forcing the thinker to move outside its usual way of thinking. This paper aims to complement the ALBidS strategies by combining them and taking advantage of their different perspectives through the use of the STH group decision technique. The combination of ALBidS' strategies is performed through the application of a genetic algorithm, resulting in an evolutionary learning approach.

Message-oriented middleware for smart grids

In order to increase the efficiency in the use of energy resources, the electrical grid is slowly evolving into a smart(er) grid that allows users' production and storage of energy, automatic and remote control of appliances, energy exchange between users, and in general optimizations over how the energy is managed and consumed. One of the main innovations of the smart grid is its organization over an energy plane that involves the actual exchange of energy, and a data plane that regards the Information and Communication Technology (ICT) infrastructure used for the management of the grid's data. In the particular case of the data plane, the exchange of large quantities of data can be facilitated by a middleware based on a messaging bus. Existing messaging buses follow different data management paradigms (e.g.: request/response, publish/subscribe, data-oriented messaging) and thus satisfy smart grids' communication requirements at different extents. This work contributes to the state of the art by identifying, in existing standards and architectures, common requirements that impact in the messaging system of a data plane for the smart grid. The paper analyzes existing messaging bus paradigms that can be used as a basis for the ICT infrastructure of a smart grid and discusses how these can satisfy smart grids' requirements.

mRPL: Boosting mobility in the Internet of Things

The 6loWPAN (the light version of IPv6) and RPL (routing protocol for low-power and lossy links) protocols have become de facto standards for the Internet of Things (IoT). In this paper, we show that the two native algorithms that handle changes in network topology – the Trickle and Neighbor Discovery algorithms – behave in a reactive fashion and thus are not prepared for the dynamics inherent to nodes mobility. Many emerging and upcoming IoT application scenarios are expected to impose real-time and reliable mobile data collection, which are not compatible with the long message latency, high packet loss and high overhead exhibited by the native RPL/6loWPAN protocols. To solve this problem, we integrate a proactive hand-off mechanism (dubbed smart-HOP) within RPL, which is very simple, effective and backward compatible with the standard protocol. We show that this add-on halves the packet loss and reduces the hand-off delay dramatically to one tenth of a second, upon nodes’ mobility, with a sub-percent overhead. The smart-HOP algorithm has been implemented and integrated in the Contiki 6LoWPAN/RPL stack (source-code available on-line mrpl: smart-hop within rpl, 2014) and validated through extensive simulation and experimentation.

Deciding Kleene Algebra Terms Equivalence in Coq

This paper presents a mechanically verified implementation of an algorithm for deciding the equivalence of Kleene algebra terms within the Coq proof assistant. The algorithm decides equivalence of two given regular expressions through an iterated process of testing the equivalence of their partial derivatives and does not require the construction of the corresponding automata. Recent theoretical and experimental research provides evidence that this method is, on average, more efficient than the classical methods based on automata. We present some performance tests, comparisons with similar approaches, and also introduce a generalization of the algorithm to decide the equivalence of terms of Kleene algebra with tests. The motivation for the work presented in this paper is that of using the libraries developed as trusted frameworks for carrying out certified program verification.

Impact of sensor nodes scaling and velocity on handover mechanisms for healthcare wireless sensor networks with mobility support

Health promotion in hospital environments can be improved using the most recent information and communication technologies. The Internet connectivity to small sensor nodes carried by patients allows remote access to their bio-signals. To promote these features the healthcare wireless sensor networks (HWSN) are used. In these networks mobility support is a key issue in order to keep patients under realtime monitoring even when they move around. To keep sensors connected to the network, they should change their access points of attachment when patients move to a new coverage area along an infirmary. This process, called handover, is responsible for continuous network connectivity to the sensors. This paper presents a detailed performance evaluation study considering three handover mechanisms for healthcare scenarios (Hand4MAC, RSSI-based, and Backbone-based). The study was performed by simulation using several scenarios with different number of sensors and different moving velocities of sensor nodes. The results show that Hand4MAC is the best solution to guarantee almost continuous connectivity to sensor nodes with less energy consumption.

Re-Use Assessment of Thermoset Composite Wastes as Aggregate and Filler Replacement for Concrete-Polymer Composite Materials: A case study regarding GFRP pultrusion wastes

Glass fibre-reinforced plastics (GFRP), nowadays commonly used in the construction, transportation and automobile sectors, have been considered inherently difficult to recycle due to both the cross-linked nature of thermoset resins, which cannot be remoulded, and the complex composition of the composite itself, which includes glass fibres, polymer matrix and different types of inorganic fillers. Hence, to date, most of the thermoset based GFRP waste is being incinerated or landfilled leading to negative environmental impacts and additional costs to producers and suppliers. With an increasing awareness of environmental matters and the subsequent desire to save resources, recycling would convert an expensive waste disposal into a profitable reusable material. In this study, the effect of the incorporation of mechanically recycled GFRP pultrusion wastes on flexural and compressive behaviour of polyester polymer mortars (PM) was assessed. For this purpose, different contents of GFRP recyclates (0%, 4%, 8% and 12%, w/w), with distinct size grades (coarse fibrous mixture and fine powdered mixture), were incorporated into polyester PM as sand aggregates and filler replacements. The effect of the incorporation of a silane coupling agent was also assessed. Experimental results revealed that GFRP waste filled polymer mortars show improved mechanical behaviour over unmodified polyester based mortars, thus indicating the feasibility of GFRP waste reuse as raw material in concrete-polymer composites.

Nanosized iron based permeable reactive barriers for nitrate removal - Systematic review

It is unquestionable that an effective decision concerning the usage of a certain environmental clean-up technology should be conveniently supported. Significant amount of scientific work focussing on the reduction of nitrate concentration in drinking water by both metallic iron and nanomaterials and their usage in permeable reactive barriers has been worldwide published over the last two decades. This work aims to present in a systematic review of the most relevant research done on the removal of nitrate from groundwater using nanosized iron based permeable reactive barriers. The research was based on scientific papers published between 2004 and June 2014. It was performed using 16 combinations of keywords in 34 databases, according to PRISMA statement guidelines. Independent reviewers validated the selection criteria. From the 4161 records filtered, 45 met the selection criteria and were selected to be included in this review. This study's outcomes show that the permeable reactive barriers are, indeed, a suitable technology for denitrification and with good performance record but the long-term impact of the use of nanosized zero valent iron in this remediation process, in both on the environment and on the human health, is far to be conveniently known. As a consequence, further work is required on this matter, so that nanosized iron based permeable reactive barriers for the removal of nitrate from drinking water can be genuinely considered an eco-efficient technology.

The use of nanoparticles in soil and water remediation processes

Environmental nanoremediation of various contaminants has been reported in several recent studies. In this paper, the state of the art on the use of nanoparticles in soil and groundwater remediation processes is presented. There is a substantive body of evidence on the growing and successful application of nanoremediation for a diversity of soil and groundwater contamination contexts, particularly, for heavy metals, other inorganic contaminants, organic contaminants and emerging contaminants, as pharmaceutical and personal care products. This review confirms the competence of the use of nanoparticles in the remediation of contaminated media and the prevalent use of iron based nanoparticles.

Immediate effects of hip mobilization with movement in patients with hip osteoarthritis: A randomised controlled trial

Background Mobilization with movement (MWM) has been shown to reduce pain, increase range of motion (ROM) and physical function in a range of different musculoskeletal disorders. Despite this evidence, there is a lack of studies evaluating the effects of MWM for hip osteoarthritis (OA). Objectives To determine the immediate effects of MWM on pain, ROM and functional performance in patients with hip OA. Design Randomized controlled trial with immediate follow-up. Method Forty consenting patients (mean age 78 ± 6 years; 54% female) satisfied the eligibility criteria. All participants completed the study. Two forms of MWM techniques (n = 20) or a simulated MWM (sham) (n = 20) were applied. Primary outcomes: pain recorded by numerical rating scale (NRS). Secondary outcomes: hip flexion and internal rotation ROM, and physical performance (timed up and go, sit to stand, and 40 m self placed walk test) were assessed before and after the intervention. Results For the MWM group, pain decreased by 2 points on the NRS, hip flexion increased by 12.2°, internal rotation by 4.4°, and functional tests were also improved with clinically relevant effects following the MWM. There were no significant changes in the sham group for any outcome variable. Conclusions Pain, hip flexion ROM and physical performance immediately improved after the application of MWM in elderly patients suffering hip OA. The observed immediate changes were of clinical relevance. Future studies are required to determine the long-term effects of this intervention.

FAS -670A>G genetic polymorphism Is associated with Treatment Resistant Depression

Background Hippocampal neurogenesis has been suggested as a downstream event of antidepressants (AD) mechanism of action and might explain the lag time between AD administration and the therapeutic effect. Despite the widespread use of AD in the context of Major Depressive Disorder (MDD) there are no reliable biomarkers of treatment response phenotypes, and a significant proportion of patients display Treatment Resistant Depression (TRD). Fas/FasL system is one of the best-known death-receptor mediated cell signaling systems and is recognized to regulate cell proliferation and tumor cell growth. Recently this pathway has been described to be involved in neurogenesis and neuroplasticity. Methods Since FAS -670A>G and FASL -844T>C functional polymorphisms never been evaluated in the context of depression and antidepressant therapy, we genotyped FAS -670A>G and FASL -844T>C in a subset of 80 MDD patients to evaluate their role in antidepressant treatment response phenotypes. Results We found that the presence of FAS -670G allele was associated with antidepressant bad prognosis (relapse or TRD: OR=6.200; 95% CI: [1.875–20.499]; p=0.001), and we observed that patients carrying this allele have a higher risk to develop TRD (OR=10.895; 95% CI: [1.362–87.135]; p=0.008).Moreover, multivariate analysis adjusted to potentials confounders showed that patients carrying G allele have higher risk of early relapse (HR=3.827; 95% CI: [1.072–13.659]; p=0.039). FAS mRNA levels were down-regulated among G carriers, whose genotypes were more common in TRD patients. No association was found between FASL-844T>C genetic polymorphism and any treatment phenotypes. Limitations Small sample size. Patients used antidepressants with different mechanisms of action. Conclusion To the best of our knowledge this is the first study to evaluate the role of FAS functional polymorphism in the outcome of antidepressant therapy. This preliminary report associates FAS -670A>G genetic polymorphism with Treatment Resistant Depression and with time to relapse. The current results may possibly be given to the recent recognized role of Fas in neurogenesis and/or neuroplasticity.

Adhesive thickness effects of a ductile adhesive by optical measurement techniques

Adhesive bonding is an excellent alternative to traditional joining techniques such as welding, mechanical fastening or riveting. However, there are many factors that have to be accounted for during joint design to accurately predict the joint strength. One of these is the adhesive layer thickness (tA). Most of the results are for epoxy structural adhesives, tailored to perform best with small values of tA, and these show that the lap joint strength decreases with increase of tA (the optimum joint strength is usually obtained with tA values between 0.1 and 0.2 mm). Recently, polyurethane adhesives were made available in the market, designed to perform with larger tA values, and whose fracture behaviour is still not studied. In this work, the effect of tA on the tensile fracture toughness (View the MathML source) of a bonded joint is studied, considering a novel high strength and ductile polyurethane adhesive for the automotive industry. This work consists on the fracture characterization of the bond by a conventional and the J-integral techniques, which accurately account for root rotation effects. An optical measurement method is used for the evaluation of crack tip opening (δn) and adherends rotation at the crack tip (θo) during the test, supported by a Matlab® sub-routine for the automated extraction of these parameters. As output of this work, fracture data is provided in traction for the selected adhesive, enabling the subsequent strength prediction of bonded joints.