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.

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%.

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.

Chaotic Phenomena and Performance Optimization in the Trajectory Control of Redundant Manipulators

Redundant manipulators have some advantages when compared with classical arms because they allow the trajectory optimization, both on the free space and on the presence of abstacles, and the resolution of singularities. For this type of manipulators, several kinetic algorithms adopt generalized inverse matrices. In this line of thought, the generalized inverse control scheme is tested through several experiments that reveal the difficulties that often arise. Motivated by theseproblems this paper presents a new method that ptimizes the manipulability through a least squre polynomialapproximation to determine the joints positions. Moreover, the article studies influence on the dynamics, when controlling redundant and hyper-redundant manipulators. The experiment confirm the superior performance of the proposed algorithm for redundant and hyper-redundant manipulators, revealing several fundamental properties of the chaotic phenomena, and gives a deeper insight towards the future development of superior trajectory control algorithms.

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

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.

Optimization techniques for power distribution planning with uncertainties: a comparative study

Important research effort has been devoted to the topic of optimal planning of distribution systems. The non linear nature of the system, the need to consider a large number of scenarios and the increasing necessity to deal with uncertainties make optimal planning in distribution systems a difficult task. Heuristic techniques approaches have been proposed to deal with these issues, overcoming some of the inherent difficulties of classic methodologies. This paper considers several methodologies used to address planning problems of electrical power distribution networks, namely mixedinteger linear programming (MILP), ant colony algorithms (AC), genetic algorithms (GA), tabu search (TS), branch exchange (BE), simulated annealing (SA) and the Bender´s decomposition deterministic non-linear optimization technique (BD). Adequacy of theses techniques to deal with uncertainties is discussed. The behaviour of each optimization technique is compared from the point of view of the obtained solution and of the methodology performance. The paper presents results of the application of these optimization techniques to a real case of a 10-kV electrical distribution system with 201 nodes that feeds an urban area.

Multi-Objective and Financial Portfolio Optimization of Carrier-Sense Multiple Access Protocols with Cooperative Diversity

8th International Workshop on Multiple Access Communications (MACOM2015), Helsinki, Finland.

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.

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.

A literature review on the optimization of legged robots

Over the last two decades the research and development of legged locomotion robots has grown steadily. Legged systems present major advantages when compared with ‘traditional’ vehicles, because they allow locomotion in inaccessible terrain to vehicles with wheels and tracks. However, the robustness of legged robots, and especially their energy consumption, among other aspects, still lag behind mechanisms that use wheels and tracks. Therefore, in the present state of development, there are several aspects that need to be improved and optimized. Keeping these ideas in mind, this paper presents the review of the literature of different methods adopted for the optimization of the structure and locomotion gaits of walking robots. Among the distinct possible strategies often used for these tasks are referred approaches such as the mimicking of biological animals, the use of evolutionary schemes to find the optimal parameters and structures, the adoption of sound mechanical design rules, and the optimization of power-based indexes.

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.

Self-optimization module for scheduling using case-based reasoning

Metaheuristics performance is highly dependent of the respective parameters which need to be tuned. Parameter tuning may allow a larger flexibility and robustness but requires a careful initialization. The process of defining which parameters setting should be used is not obvious. The values for parameters depend mainly on the problem, the instance to be solved, the search time available to spend in solving the problem, and the required quality of solution. This paper presents a learning module proposal for an autonomous parameterization of Metaheuristics, integrated on a Multi-Agent System for the resolution of Dynamic Scheduling problems. The proposed learning module is inspired on Autonomic Computing Self-Optimization concept, defining that systems must continuously and proactively improve their performance. For the learning implementation it is used Case-based Reasoning, which uses previous similar data to solve new cases. In the use of Case-based Reasoning it is assumed that similar cases have similar solutions. After a literature review on topics used, both AutoDynAgents system and Self-Optimization module are described. Finally, a computational study is presented where the proposed module is evaluated, obtained results are compared with previous ones, some conclusions are reached, and some future work is referred. It is expected that this proposal can be a great contribution for the self-parameterization of Metaheuristics and for the resolution of scheduling problems on dynamic environments.

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.

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.