843 resultados para demand-side management


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The focus in this article is how the extensive use of fly-in fly-out (FIFO) working arrangements in the Western Australian resources sector has an impact directly and indirectly on smaller firms and their ability to recruit workers in remote locations. We argue that the growth of FIFO working arrangements has disadvantaged smaller resource-sector firms by increasing their employment costs and decreasing their ability to attract skilled workers. As a result, smaller resource-sector firms are recruiting skilled workers on 457 visas to secure their business stability and growth, despite the complexity, costs, and risks involved.

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Peak electricity demand requires substantial investment to update transmission, distribution and generation infrastructure. A successful community peak demand reduction project was examined to identify residential consumer motivational and contextual factors involved in their decision to adopt/not adopt interventions. Energy professionals actively worked to achieve community 'peer' membership and by becoming a trusted information source, facilitated voluntary home energy assessment requests from over 80% of the residential community. By combining and tailoring interventions to the specific needs and motivations of individual householders and the community, interventions promoting energy conservation and efficiency can be effective in achieving sustained reduction in peak demand.

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For necessary goods like water, under supply constraints, fairness considerations lead to negative externalities. The objective of this paper is to design an infinite horizon contract or relational contract (a type of long-term contract) that ensures self-enforcing (instead of court-enforced) behaviour by the agents to mitigate the externality due to fairness issues. In this contract, the consumer is induced to consume at firm-supply level using the threat of higher fair price for future time periods. The pricing mechanism, computed in this paper, internalizes the externality and is shown to be economically efficient and provides revenue sufficiency.

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In this paper, we have proposed a novel certificate-less on-demand public key management (CLPKM) protocol for self-organized MANETs. The protocol works on flat network architecture, and distinguishes between authentication layer and routing layer of the network. We put an upper limit on the length of verification route and use the end-to-end trust value of a route to evaluate its strength. The end-to-end trust value is used by the protocol to select the most trusted verification route for accomplishing public key verification. Also, the protocol uses MAC function instead of RSA certificates to perform public key verification. By doing this, the protocol saves considerable computation power, bandwidth and storage space. The saved storage space is utilized by the protocol to keep a number of pre-established routes in the network nodes, which helps in reducing the average verification delay of the protocol. Analysis and simulation results confirm the effectiveness of the proposed protocol.

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The advent of virtualization and cloud computing technologies necessitates the development of effective mechanisms for the estimation and reservation of resources needed by content providers to deliver large numbers of video-on-demand (VOD) streams through the cloud. Unfortunately, capacity planning for the QoS-constrained delivery of a large number of VOD streams is inherently difficult as VBR encoding schemes exhibit significant bandwidth variability. In this paper, we present a novel resource management scheme to make such allocation decisions using a mixture of per-stream reservations and an aggregate reservation, shared across all streams to accommodate peak demands. The shared reservation provides capacity slack that enables statistical multiplexing of peak rates, while assuring analytically bounded frame-drop probabilities, which can be adjusted by trading off buffer space (and consequently delay) and bandwidth. Our two-tiered bandwidth allocation scheme enables the delivery of any set of streams with less bandwidth (or equivalently with higher link utilization) than state-of-the-art deterministic smoothing approaches. The algorithm underlying our proposed frame-work uses three per-stream parameters and is linear in the number of servers, making it particularly well suited for use in an on-line setting. We present results from extensive trace-driven simulations, which confirm the efficiency of our scheme especially for small buffer sizes and delay bounds, and which underscore the significant realizable bandwidth savings, typically yielding losses that are an order of magnitude or more below our analytically derived bounds.

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The smart grid concept is rapidly evolving in the direction of practical implementations able to bring smart grid advantages into practice. Evolution in legacy equipment and infrastructures is not sufficient to accomplish the smart grid goals as it does not consider the needs of the players operating in a complex environment which is dynamic and competitive in nature. Artificial intelligence based applications can provide solutions to these problems, supporting decentralized intelligence and decision-making. A case study illustrates the importance of Virtual Power Players (VPP) and multi-player negotiation in the context of smart grids. This case study is based on real data and aims at optimizing energy resource management, considering generation, storage and demand response.

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In future power systems, in the smart grid and microgrids operation paradigms, consumers can be seen as an energy resource with decentralized and autonomous decisions in the energy management. It is expected that each consumer will manage not only the loads, but also small generation units, heating systems, storage systems, and electric vehicles. Each consumer can participate in different demand response events promoted by system operators or aggregation entities. This paper proposes an innovative method to manage the appliances on a house during a demand response event. The main contribution of this work is to include time constraints in resources management, and the context evaluation in order to ensure the required comfort levels. The dynamic resources management methodology allows a better resources’ management in a demand response event, mainly the ones of long duration, by changing the priorities of loads during the event. A case study with two scenarios is presented considering a demand response with 30 min duration, and another with 240 min (4 h). In both simulations, the demand response event proposes the power consumption reduction during the event. A total of 18 loads are used, including real and virtual ones, controlled by the presented house management system.

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Demand response concept has been gaining increasing importance while the success of several recent implementations makes this resource benefits unquestionable. This happens in a power systems operation environment that also considers an intensive use of distributed generation. However, more adequate approaches and models are needed in order to address the small size consumers and producers aggregation, while taking into account these resources goals. The present paper focuses on the demand response programs and distributed generation resources management by a Virtual Power Player that optimally aims to minimize its operation costs taking the consumption shifting constraints into account. The impact of the consumption shifting in the distributed generation resources schedule is also considered. The methodology is applied to three scenarios based on 218 consumers and 4 types of distributed generation, in a time frame of 96 periods.

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Empirical evidence suggests that ambiguity is prevalent in insurance pricing and underwriting, and that often insurers tend to exhibit more ambiguity than the insured individuals (e.g., [23]). Motivated by these findings, we consider a problem of demand for insurance indemnity schedules, where the insurer has ambiguous beliefs about the realizations of the insurable loss, whereas the insured is an expected-utility maximizer. We show that if the ambiguous beliefs of the insurer satisfy a property of compatibility with the non-ambiguous beliefs of the insured, then there exist optimal monotonic indemnity schedules. By virtue of monotonicity, no ex-post moral hazard issues arise at our solutions (e.g., [25]). In addition, in the case where the insurer is either ambiguity-seeking or ambiguity-averse, we show that the problem of determining the optimal indemnity schedule reduces to that of solving an auxiliary problem that is simpler than the original one in that it does not involve ambiguity. Finally, under additional assumptions, we give an explicit characterization of the optimal indemnity schedule for the insured, and we show how our results naturally extend the classical result of Arrow [5] on the optimality of the deductible indemnity schedule.

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Empirical evidence suggests that ambiguity is prevalent in insurance pricing and underwriting, and that often insurers tend to exhibit more ambiguity than the insured individuals (e.g., [23]). Motivated by these findings, we consider a problem of demand for insurance indemnity schedules, where the insurer has ambiguous beliefs about the realizations of the insurable loss, whereas the insured is an expected-utility maximizer. We show that if the ambiguous beliefs of the insurer satisfy a property of compatibility with the non-ambiguous beliefs of the insured, then there exist optimal monotonic indemnity schedules. By virtue of monotonicity, no ex-post moral hazard issues arise at our solutions (e.g., [25]). In addition, in the case where the insurer is either ambiguity-seeking or ambiguity-averse, we show that the problem of determining the optimal indemnity schedule reduces to that of solving an auxiliary problem that is simpler than the original one in that it does not involve ambiguity. Finally, under additional assumptions, we give an explicit characterization of the optimal indemnity schedule for the insured, and we show how our results naturally extend the classical result of Arrow [5] on the optimality of the deductible indemnity schedule.

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Media content distribution on-demand becomes more complex when performed on a mass scale involving various channels with distinct and dynamic network characteristics, and, deploying a variety of terminal devices offering a wide range of capabilities. It is practically impossible to create and prepackage various static versions of the same content to match all the varying demand parameters of clients for various contexts. In this paper we present a profiling management approach for dynamically personalised media content delivery on-demand integrated with the AXMEDIS Framework. The client profiles comprise the representation of User, Device, Network and Context of content delivery based on MPEG-21:DIA. Although the most challenging proving ground for this personalised content delivery has been the mobile testbed i.e. the distribution to mobile handsets, the framework described here can be deployed for disribution, by the AXMEDIS PnP module, through other channels e.g. satellite, Internet to a range of client terminals e.g. desktops, kiosks, IPtv and other terrminals whose baseline terminal capabilities can be made availabe by the manufacturers as is normal.