Combined heat and power and consumption optimization in a SCADA-based system

The operation of power systems in a Smart Grid (SG) context brings new opportunities to consumers as active players, in order to fully reach the SG advantages. In this context, concepts as smart homes or smart buildings are promising approaches to perform the optimization of the consumption, while reducing the electricity costs. This paper proposes an intelligent methodology to support the consumption optimization of an industrial consumer, which has a Combined Heat and Power (CHP) facility. A SCADA (Supervisory Control and Data Acquisition) system developed by the authors is used to support the implementation of the proposed methodology. An optimization algorithm implemented in the system in order to perform the determination of the optimal consumption and CHP levels in each instant, according to the Demand Response (DR) opportunities. The paper includes a case study with several scenarios of consumption and heat demand in the context of a DR event which specifies a maximum demand level for the consumer.

Dynamic power- and failure-aware cloud resources allocation for Sets of independent tasks

Cloud computing is increasingly being adopted in different scenarios, like social networking, business applications, scientific experiments, etc. Relying in virtualization technology, the construction of these computing environments targets improvements in the infrastructure, such as power-efficiency and fulfillment of users’ SLA specifications. The methodology usually applied is packing all the virtual machines on the proper physical servers. However, failure occurrences in these networked computing systems can induce substantial negative impact on system performance, deviating the system from ours initial objectives. In this work, we propose adapted algorithms to dynamically map virtual machines to physical hosts, in order to improve cloud infrastructure power-efficiency, with low impact on users’ required performance. Our decision making algorithms leverage proactive fault-tolerance techniques to deal with systems failures, allied with virtual machine technology to share nodes resources in an accurately and controlled manner. The results indicate that our algorithms perform better targeting power-efficiency and SLA fulfillment, in face of cloud infrastructure failures.

Quality-of-service aware routing for static and mobile IPv6-based low-power and lossy sensor networks using RPL

The Internet of Things (IoT) has emerged as a paradigm over the last few years as a result of the tight integration of the computing and the physical world. The requirement of remote sensing makes low-power wireless sensor networks one of the key enabling technologies of IoT. These networks encompass several challenges, especially in communication and networking, due to their inherent constraints of low-power features, deployment in harsh and lossy environments, and limited computing and storage resources. The IPv6 Routing Protocol for Low Power and Lossy Networks (RPL) [1] was proposed by the IETF ROLL (Routing Over Low-power Lossy links) working group and is currently adopted as an IETF standard in the RFC 6550 since March 2012. Although RPL greatly satisfied the requirements of low-power and lossy sensor networks, several issues remain open for improvement and specification, in particular with respect to Quality of Service (QoS) guarantees and support for mobility. In this paper, we focus mainly on the RPL routing protocol. We propose some enhancements to the standard specification in order to provide QoS guarantees for static as well as mobile LLNs. For this purpose, we propose OF-FL (Objective Function based on Fuzzy Logic), a new objective function that overcomes the limitations of the standardized objective functions that were designed for RPL by considering important link and node metrics, namely end-to-end delay, number of hops, ETX (Expected transmission count) and LQL (Link Quality Level). In addition, we present the design of Co-RPL, an extension to RPL based on the corona mechanism that supports mobility in order to overcome the problem of slow reactivity to frequent topology changes and thus providing a better quality of service mainly in dynamic networks application. Performance evaluation results show that both OF-FL and Co-RPL allow a great improvement when compared to the standard specification, mainly in terms of packet loss ratio and average network latency. 2015 Elsevier B.V. Al

PIASA: A power and interference aware resource management strategy for heterogeneous workloads in cloud data centers

Cloud data centers have been progressively adopted in different scenarios, as reflected in the execution of heterogeneous applications with diverse workloads and diverse quality of service (QoS) requirements. Virtual machine (VM) technology eases resource management in physical servers and helps cloud providers achieve goals such as optimization of energy consumption. However, the performance of an application running inside a VM is not guaranteed due to the interference among co-hosted workloads sharing the same physical resources. Moreover, the different types of co-hosted applications with diverse QoS requirements as well as the dynamic behavior of the cloud makes efficient provisioning of resources even more difficult and a challenging problem in cloud data centers. In this paper, we address the problem of resource allocation within a data center that runs different types of application workloads, particularly CPU- and network-intensive applications. To address these challenges, we propose an interference- and power-aware management mechanism that combines a performance deviation estimator and a scheduling algorithm to guide the resource allocation in virtualized environments. We conduct simulations by injecting synthetic workloads whose characteristics follow the last version of the Google Cloud tracelogs. The results indicate that our performance-enforcing strategy is able to fulfill contracted SLAs of real-world environments while reducing energy costs by as much as 21%.

A Multiple Criteria Utility-based Approa h for the Unit Commitment with Wind Power and Pumped Storage Hydro

The integration of wind power in eletricity generation brings new challenges to unit commitment due to the random nature of wind speed. For this particular optimisation problem, wind uncertainty has been handled in practice by means of conservative stochastic scenario-based optimisation models, or through additional operating reserve settings. However, generation companies may have different attitudes towards operating costs, load curtailment, or waste of wind energy, when considering the risk caused by wind power variability. Therefore, alternative and possibly more adequate approaches should be explored. This work is divided in two main parts. Firstly we survey the main formulations presented in the literature for the integration of wind power in the unit commitment problem (UCP) and present an alternative model for the wind-thermal unit commitment. We make use of the utility theory concepts to develop a multi-criteria stochastic model. The objectives considered are the minimisation of costs, load curtailment and waste of wind energy. Those are represented by individual utility functions and aggregated in a single additive utility function. This last function is adequately linearised leading to a mixed-integer linear program (MILP) model that can be tackled by general-purpose solvers in order to find the most preferred solution. In the second part we discuss the integration of pumped-storage hydro (PSH) units in the UCP with large wind penetration. Those units can provide extra flexibility by using wind energy to pump and store water in the form of potential energy that can be generated after during peak load periods. PSH units are added to the first model, yielding a MILP model with wind-hydro-thermal coordination. Results showed that the proposed methodology is able to reflect the risk profiles of decision makers for both models. By including PSH units, the results are significantly improved.

AI in power systems and energy markets

In a world increasingly conscientious about environmental effects, power and energy systems are undergoing huge transformations. Electric energy produced from power plants is transmitted and distributed to end users through a power grid. The power industry performs the engineering design, installation, operation, and maintenance tasks to provide a high-quality, secure energy supply while accounting for its systems’ abilities to withstand uncertain events, such as weather-related outages. Competitive, deregulated electricity markets and new renewable energy sources, however, have further complicated this already complex infrastructure.Sustainable development has also been a challenge for power systems. Recently, there has been a signifi cant increase in the installation of distributed generations, mainly based on renewable resources such as wind and solar. Integrating these new generation systems leads to more complexity. Indeed, the number of generation sources greatly increases as the grid embraces numerous smaller and distributed resources. In addition, the inherent uncertainties of wind and solar energy lead to technical challenges such as forecasting, scheduling, operation, control, and risk management. In this special issue introductory article, we analyze the key areas in this field that can benefi t most from AI and intelligent systems now and in the future.We also identify new opportunities for cross-fertilization between power systems and energy markets and intelligent systems researchers.

Virtual power producers integration into MASCEM

All over the world Distributed Generation is seen as a valuable help to get cleaner and more efficient electricity. Under this context distributed generators, owned by different decentralized players can provide a significant amount of the electricity generation. To get negotiation power and advantages of scale economy, these players can be aggregated giving place to a new concept: the Virtual Power Producer. Virtual Power Producers are multi-technology and multi-site heterogeneous entities. Virtual Power Producers should adopt organization and management methodologies so that they can make Distributed Generation a really profitable activity, able to participate in the market. In this paper we address the integration of Virtual Power Producers into an electricity market simulator –MASCEM – as a coalition of distributed producers.

An intelligent tutoring approach to support students and technicians in electrical installation design

Designing electric installation projects, demands not only academic knowledge, but also other types of knowledge not easily acquired through traditional instructional methodologies. A lot of additional empirical knowledge is missing and so the academic instruction must be completed with different kinds of knowledge, such as real-life practical examples and simulations. On the other hand, the practical knowledge detained by the most experienced designers is not formalized in such a way that is easily transmitted. In order to overcome these difficulties present in the engineers formation, we are developing an Intelligent Tutoring System (ITS), for training and support concerning the development of electrical installation projects to be used by electrical engineers, technicians and students.

An efficient method for optimal location and sizing of fixed and switched shunt capacitors in large distribution systems

This paper proposes a computationally efficient methodology for the optimal location and sizing of static and switched shunt capacitors in large distribution systems. The problem is formulated as the maximization of the savings produced by the reduction in energy losses and the avoided costs due to investment deferral in the expansion of the network. The proposed method selects the nodes to be compensated, as well as the optimal capacitor ratings and their operational characteristics, i.e. fixed or switched. After an appropriate linearization, the optimization problem was formulated as a large-scale mixed-integer linear problem, suitable for being solved by means of a widespread commercial package. Results of the proposed optimizing method are compared with another recent methodology reported in the literature using two test cases: a 15-bus and a 33-bus distribution network. For the both cases tested, the proposed methodology delivers better solutions indicated by higher loss savings, which are achieved with lower amounts of capacitive compensation. The proposed method has also been applied for compensating to an actual large distribution network served by AES-Venezuela in the metropolitan area of Caracas. A convergence time of about 4 seconds after 22298 iterations demonstrates the ability of the proposed methodology for efficiently handling large-scale compensation problems.

Intelligent power system

Artificial intelligence techniques are being widely used to face the new reality and to provide solutions that can make power systems undergo all the changes while assuring high quality power. In this way, the agents that act in the power industry are gaining access to a generation of more intelligent applications, making use of a wide set of AI techniques. Knowledge-based systems and decision-support systems have been applied in the power and energy industry. This article is intended to offer an updated overview of the application of artificial intelligence in power systems. This article paper is organized in a way so that readers can easily understand the problems and the adequacy of the proposed solutions. Because of space constraints, this approach can be neither complete nor sufficiently deep to satisfy all readers’ needs. As this is amultidisciplinary area, able to attract both software and computer engineering and power system people, this article tries to give an insight into themost important concepts involved in these applications. Complementary material can be found in the reference list, providing deeper and more specific approaches.

Ancillary services market clearing simulation: a comparison between deterministic and heuristic methods

Electricity market players operating in a liberalized environment requires access to an adequate decision support tool, allowing them to consider all the business opportunities and take strategic decisions. Ancillary services represent a good negotiation opportunity that must be considered by market players. For this, decision support tool must include ancillary market simulation. This paper proposes two different methods (Linear Programming and Genetic Algorithm approaches) for ancillary services dispatch. The methodologies are implemented in MASCEM, a multi-agent based electricity market simulator. A test case based on California Independent System Operator (CAISO) data concerning the dispatch of Regulation Down, Regulation Up, Spinning Reserve and Non-Spinning Reserve services is included in this paper.

Corporate brand identity management in a technological faculty – how brand relationships influence students and companies reputations

Identity is traditionally defined as an emission concept [1]. Yet, some research points out that there are external factors that can influence it [2]; [3]; [4]. This subject is even more relevant if one considers corporate brands. According to Aaker [5] the number, the power and the credibility of corporate associations are bigger in the case of corporate brands. Literature recognizes the influence of relationships between companies in identity management. Yet, given the increasingly important role of corporate brands, it is surprising that to date no attempt to evaluate that influence has been made in the management of corporate brand identity. Also Keller and Lehman [6] highlight relationships and costumer experience as two areas requiring more investigation. In line with this, the authors intend to develop an empirical research in order to evaluate the influence of relationships between brands in the identity of corporate brand from an internal perspective by interviewing internal stakeholders (brand managers and internal clients). This paper is organized by main contents: theoretical background, research methodology, data analysis and conclusions and finally cues to future investigation.

Flavored waters: influence of ingredients on antioxidant capacity and terpenoid profile by HS-SPME/GC-MS

The antioxidant profiles of 39 water samples (29 flavored waters based on 10 natural waters) and 6 flavors used in their formulation (furnished by producers) were determined. Total phenol and flavonoid contents, reducing power, and DPPH radical scavenging activity were the optical techniques implemented and included in the referred profile. Flavor extracts were analyzed by HS-SPME/GC-MS to obtain the qualitative and quantitative profiles of the volatile fraction of essential oils. Results pointed out a higher reducing power (0.14 11.8 mg of gallic acid/L) and radical scavenging activity (0.29 211.5 mg Trolox/L) of flavored waters compared with the corresponding natural ones, an interesting fact concerning human health. Bioactive compounds, such as polyphenols, were present in all samples (0.5 359 mg of gallic acid/L), whereas flavonoids were not present either in flavored waters or in flavors. The major components of flavor extracts were monoterpenes, such as citral, R-limonene, carveol, and R-terpineol.

Angolan cymbopogon citratus used for therapeutic benefits: nutritional composition and influence of solvents in phytochemicals content and antioxidant activity of leaf extracts

Folk medicine is a relevant and effective part of indigenous healthcare systems which are, in practice, totally dependent on traditional healers. An outstanding coincidence between indigenous medicinal plant uses and scientifically proved pharmacological properties of several phytochemicals has been observed along the years. This work focused on the leaves of a medicinal plant traditionally used for therapeutic benefits (Angolan Cymbopogon citratus), in order to evaluate their nutritional value. The bioactive phytochemical composition and antioxidant activity of leaf extracts prepared with different solvents (water, methanol and ethanol) were also evaluated. The plant leaves contained ~60% of carbohydrates, protein (~20%), fat (~5%), ash (~4%) and moisture (~9%). The phytochemicals screening revealed the presence of tannins, flavonoids, and terpenoids in all extracts. Methanolic extracts also contained alkaloids and steroids. Several methods were used to evaluate total antioxidant capacity of the different extracts (DPPH; NO; and H2O2 scavenging assays, reducing power, and FRAP). Ethanolic extracts presented a significantly higher antioxidant activity (p < 0.05) except for FRAP, in which the best results were achieved by the aqueous extracts. Methanolic extracts showed the lowest radical scavenging activities for both DPPH; and NO; radicals.

Study of optical and structural properties of Cu2ZnSnS4 thin films

Cu2ZnSnS4 is a promising semiconductor to be used as absorber in thin film solar cells. In this work, we investigated optical and structural properties of Cu2ZnSnS4 thin films grown by sulphurization of metallic precursors deposited on soda lime glass substrates. The crystalline phases were studied by X-ray diffraction measurements showing the presence of only the Cu2ZnSnS4 phase. The studied films were copper poor and zinc rich as shown by inductively coupled plasma mass spectroscopy. Scanning electron microscopy revealed a good crystallinity and compactness. An absorption coefficient varying between 3 and 4×104cm−1 was measured in the energy range between 1.75 and 3.5 eV. The band gap energy was estimated in 1.51 eV. Photoluminescence spectroscopy showed an asymmetric broad band emission. The dependence of this emission on the excitation power and temperature was investigated and compared to the predictions of the donor-acceptor-type transitions and radiative recombinations in the model of potential fluctuations. Experimental evidence was found to ascribe the observed emission to radiative transitions involving tail states created by potential fluctuations.