Search

When located at the cell edge, LTE network users suffer a severe loss of reception capacity due to interference from other cells. The development of 5G indicates that the dense deployment of small cells might worsen interference problems. Ever since this setback has been identified, several techniques emerged to mitigate the situation. Coordinated multipoint (CoMP) is one of these techniques, and dynamic point blanking (DPB) is one of the less complex forms of its implementation. In this work, a DPB scheduler is proposed to reduce interference in IoT scenarios using resources from a 5G network. Results show that the network aggregate throughput is increased while keeping fairness among the sensors when compared to a round-robin scheduler.

Erdinç, Ozan (Arel Author), As the smart grid solutions enable active consumer participation, demand response (DR) strategies have drawn much interest in the literature recently, especially for residential areas. As a new type of consumer load in the electric power system, electric vehicles (EVs) also provide different opportunities, including the capability of utilizing EVs as a storage unit via vehicle-to-home (V2H) and vehicle-to-grid (V2G) options instead of peak power procurement from the grid. In this paper, as the main contribution to the literature, a collaborative evaluation of dynamic-pricing and peak power limiting-based DR strategies with a bi-directional utilization possibility for EV and energy storage system (ESS) is realized. A mixed-integer linear programming (MILP) framework-based modeling of a home energy management (HEM) structure is provided for this purpose. A distributed small-scale renewable energy generation system, the V2H and V2G capabilities of an EV together with two-way energy trading of ESS, and different DR strategies are all combined in a single HEM system for the first time in the literature. The impacts of different EV owner consumer preferences together with the availability of ESS and two-way energy trading capabilities on the reduction of total electricity prices are examined with case studies.

Renewable energies are in constant growth and evolution, being a clean way to provide the energy required for the sustainable development of human society. In this context, energy storage systems are a key factor in the integration of renewable generation, because through them, the flexibility of the power system can be increased. Lead-acid batteries have been extensively used to provide electricity in isolated and rural locations, and could be integrated to the smart grid in order to improve its performance. However, this is a complex element due to its working principle, specifically during charging periods. In this paper, a general purpose model is formulated from a probabilistic point-of-view in order to determine the range of possible values of state-of-charge due to the uncertainty and to estimate the battery efficiency. A case study is analyzed and the results are compared with Monte Carlo Simulation approach in order to evaluate the proposed model.

With the advent of restructuring electricity sector and smart grids, combined with the increased variability and uncertainty associated with electricity market prices (EMP) signals and players' behavior, together with the growing integration of renewable energy sources, enhancing prediction tools are required for players and different regulators agents to face the non-stationarity and stochastic nature of such time series, which must be capable of supporting decisions in a competitive environment with low prediction error, acceptable computational time and low computational complexity. Hybrid and evolutionary approaches are good candidates to surpass most of the previous concern considering time series prediction. In this sense, this work proposes a hybrid model composed by a novel combination of differential evolutionary particle swarm optimization (DEEPSO) and adaptive neuro-fuzzy inference system (ANFIS) to predict, in the short-term, the wind power and EMP, testing its results with real and published case studies, proving its superior performance within a robust prediction software tool.

In this paper, a multi energy system (MES) model incorporating the traffic behavior of plug-in electric vehicles (PEVs) is proposed. It is assumed that in a micro MES two charging options are available for the PEVs: the home charging (HC) stations and the PEV parking lot (PL). The operation of these elements within the micro MES concept is studied. The matrix model of the micro MES is adapted to enable the integration of PL and HC. Moreover, the traffic flow of the PEVs is added to the model as an input to the micro MES. The model is tested for various case studies and possible traffic behavior between the PL and HC. The results show that the presence of these two elements leads to effective integration of reduced system operation costs.

The theoretical potential for renewable energy resources (RES) to meet the global demands of energy is generally high and the ambitions for introducing RES into energy systems are growing worldwide, which also can contribute to global climate change mitigation if it is produced in a sustainable manner. To address these issues, more and more governments are implementing various programs and energy policies to accelerate the deployment of RES. The aforementioned two reasons lead to an urgent need to add new generating capacity or reduce consumption during peak periods, or both. The first option for power generation is to use RES which can inject electric energy to the grid while avoiding greenhouse gas emissions. However, the capacities of RES are not enough to supply all the required power from the side of the load. Facts that are leading to the proposal of original ways to reduce the use of energy in many sectors, namely in commercial, residential, and industrial sectors, in order to reduce the total energy costs of the consumer, to reduce the energy demand specially during on-peak hours and the greenhouse gas emissions while safeguarding end-user preferences. The aim of this paper is to determine the impact of model predictive control (MPC) on energy savings of residential households. Furthermore, the value and impact of generated power by local power sources, such as roof-top-solar, will be determined during off-peak, mid-peak, and on-peak, providing simulations during 24 hours in a house.

Genuine concerns regarding air pollution, climate change, and dependence on unstable and expensive supplies of fossil fuels have lead policy makers and researchers to search for alternatives to conventional petroleum-fueled combustion power plants with the purpose to reduce greenhouse gas emission. This leads to an urgent need to substitute them with alternate generating capacity or reduce the consumption during peak periods, or both. One of the options for power generation is the use of renewable energy resources, which can inject power to the grid deprived of greenhouse gas emissions. But, from the load point of view, the renewable energy resources capacity is not sufficient to supply all the required power. These points to the necessity of innovative methods, able to diminish energy consumption in different sectors, but also with the aim of reducing the domestic customer's total energy costs, greenhouse gas emissions and energy demand, especially during on-peak, while always considering the end user preferences. Hence, this paper analyses model predictive control (MPC) application in domestic appliances with the purpose of energy optimization. In this context, the research theme is focused on the relation between MPC weighting adjustment and the minimization of energy consumption. Three domestic loads are used for MPC tuning evaluation: water heater (WH), room temperature control by conditioner (AC) and refrigerator (RF).

Everyday, more sensor nodes become interconnected to make the Internet of Things real. However, since Wireless Sensor Networks comprise energy-limited devices, techniques to save energy have become a trend in research. Since energy-saving comes at a cost to the network, its consequences must be thoroughly considered in order to keep their communication effective. In this work, the use of particle swarm optimization (PSO) is proposed to calculate different transmission powers for each node, without creating disconnected areas in a sensors cluster. The achieved results show that the proposed PSO algorithm is able to save sensors energy when compared to the common deployment of nodes with a single transmission power.

Smart home vision aims for the integration of key electrical appliances and services, communications and information technologies into one intelligent network to be controlled, monitored and managed by the building user. A wireless infrastructure as a smart home communication backbone is the logical choice for a so high level of integration. This paper updates on wireless communication technologies state-of-the-art for short range applications, and discusses their suitability according to major functional areas or services that may benefit from smart home implementation.

Several governments are phasing out coal fired generation power plants to reduce greenhouse gas emission. At the same time, nuclear generating facilities are reaching the end of their life and in the wake of the Fukushima disaster, developed countries have chosen to phase out nuclear energy early in the 2020s, removing 15% of the most stable and reliable portion of their energy mix. These two reasons create an urgent need to add new generating capacity or reduce consumption during peak periods, or both. The first option for power generation is the use of renewable energy resources, which can inject power to the grid without greenhouse gas emissions. But, the capacities of renewable energy resources are not enough to supply all the required power from the load side. All of these facts are leading to the proposal of novel approaches to reduce the utilization of energy in different sectors i.e. in residential, commercial, agricultural and/or industrial sectors to reduce the customer's total energy costs, energy demand, especially during on-peak, and greenhouse gas emissions, while taking into account the end-user preferences. The main objective of this paper is to demonstrate the impact of optimization technologies on energy savings of residential households. In this regard, a model-based predictive control approach is proposed for home cooling and heating systems. Its effectiveness is compared to thermostat conventional control by providing simulations upon 24 hours in a household.

Cloud-based communications system is now widely used in many application fields such as medicine, security, environment protection, etc. Its use is being extended to the most demanding services like multimedia delivery. However, there are a lot of constraints when cloud-based sensor networks use the standard IEEE 802.15.3 or IEEE 802.15.4 technologies. This paper proposes a channel characterization scheme combined to a cross-layer admission control in dynamic cloud-based multimedia sensor networks to share the network resources among any two nodes. The analysis shows the behavior of two nodes using different network access technologies and the channel effects for each technology. Moreover, the existence of optimal node arrival rates in order to improve the usage of dynamic admission control when network resources are used is also shown. An extensive simulation study was performed to evaluate and validate the efficiency of the proposed dynamic admission control for cloud-based multimedia sensor networks., This work has been supported in part by Instituto de Telecomunicacoes, Next Generation Networks and Applications Group (NetGNA), Portugal, and in part by National Funding from the Fundacao para a Ciencia e Tecnologia through the Pest-OE/EEI/LA0008/2011.

IPv6 over Low-power Personal Area Networks (6LoWPANs) enables IPv6 support for wireless sensor networks (WSNs), making them connectable through the Internet. However, new gateway devices are needed in order to make end-to-end connectivity between sensor nodes and Internet devices possible. In this paper, a ready-to-use WSN gateway is constructed to connect WSNs and IPv6 hosts, allowing interaction with each other in real time through the IPv6 protocol. The experimental results show that the gateway successfully enables communication between a WSN comprising distinct nodes and IPv6 clients, allowing IPv6 clients to read and show sensor nodes data and to actuate on their assets.

Two sets of authors discuss the paper "A simulation model for a competitive generation market" (see ibid., vol.15, no.1, p.250-6, 2000), requesting comments and clarification on the proposed simulation tool for mainland Spain's competitive electricity market. The original authors' responses to these discussions are also included.

The development of wireless networks can damage networks Transport Layer operation. There has been much research effort to solve these problems. One of the approaches is known as Cross-Layer Design (CLD), where interactions between non-adjacent layers, forbidden by the layered model, become possible. However, CLD proposals must be carefully tested in order to discover if a gain in one parameter does not result in a loss in any other. Thus, we propose to test the influence of the use of a Cross-Layer Snoop Agent on TCP timeouts. In order to perform this test, we have created a Monte Carlo Simulator and we have presented, in this work, the algorithm followed in its implementation. Also, we have used our analysis to propose an equation to predict the TCP Throughput in the considered scenario, achieving results that agree with the simulated ones. Finally, we have concluded that the use of the Snoop Agent increases TCP Throughput, decreasing TCP timeout probability as well.

Many models for TCP Throughput Prediction were proposed in the literature. However, the first ones only considered the most common networks at that time: wired networks. Thus, these models could not predict the behavior of a network that included wireless links. Also, different Link Layer protocols had to be considered, resulting in different modifications of the former throughput prediction models. During our research on TCP Models, we have found an interesting modification including ARQ, which is one of our objects of study. Also, this modification proposed a new frame retransmission scheme, which could reduce the TCP segment loss probability in a wireless link. However, when we tried to use this modified model, some errors appeared. For instance, the equation to calculate the average frame retransmissions number did not consider all the error possibilities. Thus, we decided to make some corrections in order to use this model in future works. Based on the corrected model, new comparisons were made and new conclusions, different from the original ones, were achieved.

The Transport Control Protocol (TCP) throughput can be decreased in wireless networks. One of the approaches presented in the literature to overcome this problem is Cross-Layer Design. In this paper, we have computed the TCP throughput in a wireless network as a function of the wireless channel Bit Error Rate (BER), the frame size and the retransmission protocol used at the Data Link Layer (DLL). We have proved that TCP throughput can be increased if the segment size in the Transport Layer is adjusted according to the BER and the DLL protocol. Based on our results, we have proposed a Cross-Layer scheme in which these two parameters are informed to TCP, so its segment size (which affects the frame size) can be adjusted in order to achieve optimal throughput.

The mature bulk power system requires to meet the needs of 21th century in terms of efficient and effective utilization of electric energy, together with the capability of accommodating recently growing renewable energy resources penetration. As a new idea of modernizing the current grid structure, the smart grid issue is a widely growing area of interest with investments from developed/developing country governments. As the smart grid solutions enable active consumer participation, demand response (DR) strategies have drawn much interest as such strategies provide consumers the chance for the real-time control of their consumption to reduce their bills, while utilities can lower the peak power value to be supplied to consumers. As a new type of consumer load in the electric market, electric vehicles (EVs) also provide different opportunities, including the capability of utilizing EVs as a storage unit via vehicle-to-grid (V2G) option instead of peak power procurement from utility. This study aims to discuss the impacts of different DR strategies and EV owner consumer preferences on the reduction of total electricity prices. Different case studies are conducted to better analyze the price reduction potential of different operating strategies.