Using a prioritized MAC protocol to efficiently compute aggregated quantities

Consider a distributed computer system such that every computer node can perform a wireless broadcast and when it does so, all other nodes receive this message. The computer nodes take sensor readings but individual sensor readings are not very important. It is important however to compute the aggregated quantities of these sensor readings. We show that a prioritized medium access control (MAC) protocol for wireless broadcast can compute simple aggregated quantities in a single transaction, and more complex quantities with many (but still a small number of) transactions. This leads to significant improvements in the time-complexity and as a consequence also similar reduction in energy “consumption”.

Management Driven Hybrid Multicast Framework for Content Aware Networks

The need for better adaptation of networks to transported flows has led to research on new approaches such as content aware networks and network aware applications. In parallel, recent developments of multimedia and content oriented services and applications such as IPTV, video streaming, video on demand, and Internet TV reinforced interest in multicast technologies. IP multicast has not been widely deployed due to interdomain and QoS support problems; therefore, alternative solutions have been investigated. This article proposes a management driven hybrid multicast solution that is multi-domain and media oriented, and combines overlay multicast, IP multicast, and P2P. The architecture is developed in a content aware network and network aware application environment, based on light network virtualization. The multicast trees can be seen as parallel virtual content aware networks, spanning a single or multiple IP domains, customized to the type of content to be transported while fulfilling the quality of service requirements of the service provider.

Optimization of Generation and Aggregated Consumption Shifting for Demand Response Programs Definition

The concept of demand response has drawing attention to the active participation in the economic operation of power systems, namely in the context of recent electricity markets and smart grid models and implementations. In these competitive contexts, aggregators are necessary in order to make possible the participation of small size consumers and generation units. The methodology proposed in the present paper aims to address the demand shifting between periods, considering multi-period demand response events. The focus is given to the impact in the subsequent periods. A Virtual Power Player operates the network, aggregating the available resources, and minimizing the operation costs. The illustrative case study included is based on a scenario of 218 consumers including generation sources.

Quantum-based Particle Swarm Optimization Application to Studies of Aggregated Consumption Shifting and Generation Scheduling in Smart Grids

Demand response programs and models have been developed and implemented for an improved performance of electricity markets, taking full advantage of smart grids. Studying and addressing the consumers’ flexibility and network operation scenarios makes possible to design improved demand response models and programs. The methodology proposed in the present paper aims to address the definition of demand response programs that consider the demand shifting between periods, regarding the occurrence of multi-period demand response events. The optimization model focuses on minimizing the network and resources operation costs for a Virtual Power Player. Quantum Particle Swarm Optimization has been used in order to obtain the solutions for the optimization model that is applied to a large set of operation scenarios. The implemented case study illustrates the use of the proposed methodology to support the decisions of the Virtual Power Player in what concerns the duration of each demand response event.

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.

A multi-agent system for the support of producer coalition formation in electricity markets

All over the world Distributed Generation is seen as a valuable help to get cleaner and more efficient electricity. To get negotiation power and advantages of scale economy, distributed producers can be aggregated giving place to a new concept: the Virtual Power Producer. Virtual Power Producers are multitechnology 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 development of a multi-agent market simulator – MASCEM – able to study alternative coalitions of distributed producers in order to identify promising Virtual Power Producers in an electricity market.

Intelligent energy resource management considering vehicle-to-grid: A simulated annealing approach

This paper proposes a simulated annealing (SA) approach to address energy resources management from the point of view of a virtual power player (VPP) operating in a smart grid. Distributed generation, demand response, and gridable vehicles are intelligently managed on a multiperiod basis according to V2G user´s profiles and requirements. Apart from using the aggregated resources, the VPP can also purchase additional energy from a set of external suppliers. The paper includes a case study for a 33 bus distribution network with 66 generators, 32 loads, and 1000 gridable vehicles. The results of the SA approach are compared with a methodology based on mixed-integer nonlinear programming. A variation of this method, using ac load flow, is also used and the results are compared with the SA solution using network simulation. The proposed SA approach proved to be able to obtain good solutions in low execution times, providing VPPs with suitable decision support for the management of a large number of distributed resources.

Particle swarm optimization applied to integrated demand response resources scheduling

The concept of demand response has a growing importance in the context of the future power systems. Demand response can be seen as a resource like distributed generation, storage, electric vehicles, etc. All these resources require the existence of an infrastructure able to give players the means to operate and use them in an efficient way. This infrastructure implements in practice the smart grid concept, and should accommodate a large number of diverse types of players in the context of a competitive business environment. In this paper, demand response is optimally scheduled jointly with other resources such as distributed generation units and the energy provided by the electricity market, minimizing the operation costs from the point of view of a virtual power player, who manages these resources and supplies the aggregated consumers. The optimal schedule is obtained using two approaches based on particle swarm optimization (with and without mutation) which are compared with a deterministic approach that is used as a reference methodology. A case study with two scenarios implemented in DemSi, a demand Response simulator developed by the authors, evidences the advantages of the use of the proposed particle swarm approaches.

Virtual power producers simulation - negotiating renewable distributed generation in competitive electricity markets

Sustainable development concerns made renewable energy sources to be increasingly used for electricity distributed generation. However, this is mainly due to incentives or mandatory targets determined by energy policies as in European Union. Assuring a sustainable future requires distributed generation to be able to participate in competitive electricity markets. To get more negotiation power in the market and to get advantages of scale economy, distributed generators can be aggregated giving place to a new concept: the Virtual Power Producer (VPP). VPPs are multi-technology and multisite heterogeneous entities that should adopt organization and management methodologies so that they can make distributed generation a really profitable activity, able to participate in the market. This paper presents ViProd, a simulation tool that allows simulating VPPs operation, in the context of MASCEM, a multi-agent based eletricity market simulator.

MV producers and consumers agents characterization with DSM techniques

This paper consist in the establishment of a Virtual Producer/Consumer Agent (VPCA) in order to optimize the integrated management of distributed energy resources and to improve and control Demand Side Management DSM) and its aggregated loads. The paper presents the VPCA architecture and the proposed function-based organization to be used in order to coordinate the several generation technologies, the different load types and storage systems. This VPCA organization uses a frame work based on data mining techniques to characterize the costumers. The paper includes results of several experimental tests cases, using real data and taking into account electricity generation resources as well as consumption data.

Demand response in future power systems management – a conceptual framework and simulation tool

Mestrado em Engenharia Electrotécnica – Sistemas Eléctricos de Energia

Reliable real-time communication in CAN networks

Controller area network (CAN) is a fieldbus network suitable for small-scale distributed computer controlled systems (DCCS), being appropriate for sending and receiving short real-time messages at speeds up to 1 Mbit/sec. Several studies are available on how to guarantee the real-time requirements of CAN messages, providing preruntime schedulability conditions to guarantee the real-time communication requirements of DCCS traffic. Usually, it is considered that CAN guarantees atomic multicast properties by means of its extensive error detection/signaling mechanisms. However, there are some error situations where messages can be delivered in duplicate or delivered only by a subset of the receivers, leading to inconsistencies in the supported applications. In order to prevent such inconsistencies, a middleware for reliable communication in CAN is proposed, taking advantage of CAN synchronous properties to minimize the runtime overhead. Such middleware comprises a set of atomic multicast and consolidation protocols, upon which the reliable communication properties are guaranteed. The related timing analysis demonstrates that, in spite of the extra stack of protocols, the real-time properties of CAN are preserved since the predictability of message transfer is guaranteed.

Programming atomic multicasts in CAN

In Distributed Computer-Controlled Systems (DCCS), a special emphasis must be given to the communication infrastructure, which must provide timely and reliable communication services. CAN networks are usually suitable to support small-scale DCCS. However, they are known to present some reliability problems, which can lead to an unreliable behaviour of the supported applications. In this paper, an atomic multicast protocol for CAN networks is proposed. This protocol explores the CAN synchronous properties, providing a timely and reliable service to the supported applications. The implementation of such protocol in Ada, on top of the Ada version of Real-Time Linux is presented, which is used to demonstrate the advantages and disadvantages of the platform to support reliable communications in DCCS.

LNT: a logical neighbor tree secure group communication scheme for wireless sensor networks

Secure group communication is a paradigm that primarily designates one-to-many communication security. The proposed works relevant to secure group communication have predominantly considered the whole network as being a single group managed by a central powerful node capable of supporting heavy communication, computation and storage cost. However, a typical Wireless Sensor Network (WSN) may contain several groups, and each one is maintained by a sensor node (the group controller) with constrained resources. Moreover, the previously proposed schemes require a multicast routing support to deliver the rekeying messages. Nevertheless, multicast routing can incur heavy storage and communication overheads in the case of a wireless sensor network. Due to these two major limitations, we have reckoned it necessary to propose a new secure group communication with a lightweight rekeying process. Our proposal overcomes the two limitations mentioned above, and can be applied to a homogeneous WSN with resource-constrained nodes with no need for a multicast routing support. Actually, the analysis and simulation results have clearly demonstrated that our scheme outperforms the previous well-known solutions.

Nano-CF: a Coordination Framework for macro-programming in wireless sensor networks

Wireless Sensor Networks (WSN) are being used for a number of applications involving infrastructure monitoring, building energy monitoring and industrial sensing. The difficulty of programming individual sensor nodes and the associated overhead have encouraged researchers to design macro-programming systems which can help program the network as a whole or as a combination of subnets. Most of the current macro-programming schemes do not support multiple users seamlessly deploying diverse applications on the same shared sensor network. As WSNs are becoming more common, it is important to provide such support, since it enables higher-level optimizations such as code reuse, energy savings, and traffic reduction. In this paper, we propose a macro-programming framework called Nano-CF, which, in addition to supporting in-network programming, allows multiple applications written by different programmers to be executed simultaneously on a sensor networking infrastructure. This framework enables the use of a common sensing infrastructure for a number of applications without the users having to worrying about the applications already deployed on the network. The framework also supports timing constraints and resource reservations using the Nano-RK operating system. Nano- CF is efficient at improving WSN performance by (a) combining multiple user programs, (b) aggregating packets for data delivery, and (c) satisfying timing and energy specifications using Rate- Harmonized Scheduling. Using representative applications, we demonstrate that Nano-CF achieves 90% reduction in Source Lines-of-Code (SLoC) and 50% energy savings from aggregated data delivery.