Quantifying techno-economic indicators\u27 impact on isolated renewable energy systems

Addressing climate change with the rising global energy usage necessitates electricity sector decarbonization by rapidly moving toward flexible and efficient off-grid renewable energy systems (RESs). This paper analyzes the wind and solar micro-grids, with batteries and pumped hydro storage for a robust off-grid RES techno-economic operation, while considering diverse multi-objective optimization cases. This research has considered the RES variable operational losses in the developed methodology and relations between different indicators are evaluated, revealing a basic understanding between them. The results reveal that the reliability index is inversely related to the oversupply index, while directly related to the system self-sufficiency index. The cost of energy is more sensitive to technical indicators rather than the storage cost and so can be used as a primary monetary index. Energy and cost balance analysis showed that 16%-20% of the used energy was drained in RES operational losses, which were usually ignored in previous studies

Peer-to-peer energy trading in a prosumer based community microgrid: a game-theoretic model

This paper proposes a novel game-theoretic model for peer-to-peer (P2P) energy trading among the prosumers in a community. The buyers can adjust the energy consumption behavior based on the price and quantity of the energy offered by the sellers. There exist two separate competitions during the trading process: 1) price competition among the sellers; and 2) seller selection competition among the buyers. The price competition among the sellers is modeled as a noncooperative game. The evolutionary game theory is used to model the dynamics of the buyers for selecting sellers. Moreover, an M-leader and N-follower Stackelberg game approach is used to model the interaction between buyers and sellers. Two iterative algorithms are proposed for the implementation of the games such that an equilibrium state exists in each of the games. The proposed method is applied to a small community microgrid with photo-voltaic and energy storage systems. Simulation results show the convergence of the algorithms and the effectiveness of the proposed model to handle P2P energy trading. The results also show that P2P energy trading provides significant financial and technical benefits to the community, and it is emerging as an alternative to cost-intensive energy storage systems

Reconfigurable Antenna Systems: Platform implementation and low-power matters

Antennas are a necessary and often critical component of all wireless systems, of which they share the ever-increasing complexity and the challenges of present and emerging trends. 5G, massive low-orbit satellite architectures (e.g. OneWeb), industry 4.0, Internet of Things (IoT), satcom on-the-move, Advanced Driver Assistance Systems (ADAS) and Autonomous Vehicles, all call for highly flexible systems, and antenna reconfigurability is an enabling part of these advances. The terminal segment is particularly crucial in this sense, encompassing both very compact antennas or low-profile antennas, all with various adaptability/reconfigurability requirements. This thesis work has dealt with hardware implementation issues of Radio Frequency (RF) antenna reconfigurability, and in particular with low-power General Purpose Platforms (GPP); the work has encompassed Software Defined Radio (SDR) implementation, as well as embedded low-power platforms (in particular on STM32 Nucleo family of micro-controller). The hardware-software platform work has been complemented with design and fabrication of reconfigurable antennas in standard technology, and the resulting systems tested. The selected antenna technology was antenna array with continuously steerable beam, controlled by voltage-driven phase shifting circuits. Applications included notably Wireless Sensor Network (WSN) deployed in the Italian scientific mission in Antarctica, in a traffic-monitoring case study (EU H2020 project), and into an innovative Global Navigation Satellite Systems (GNSS) antenna concept (patent application submitted). The SDR implementation focused on a low-cost and low-power Software-defined radio open-source platform with IEEE 802.11 a/g/p wireless communication capability. In a second embodiment, the flexibility of the SDR paradigm has been traded off to avoid the power consumption associated to the relevant operating system. Application field of reconfigurable antenna is, however, not limited to a better management of the energy consumption. The analysis has also been extended to satellites positioning application. A novel beamforming method has presented demonstrating improvements in the quality of signals received from satellites. Regarding those who deal with positioning algorithms, this advancement help improving precision on the estimated position

Model Predictive Control for Smart Grids with Multiple Electric-Vehicle Charging Stations

Next-generation power grids will likely enable concurrent service for residences and plug-in electric vehicles (PEVs). While the residence power demand profile is known and thus can be considered inelastic, the PEVs' power demand is only known after random PEVs' arrivals. PEV charging scheduling aims at minimizing the potential impact of the massive integration of PEVs into power grids to save service costs to customers while power control aims at minimizing the cost of power generation subject to operating constraints and meeting demand. The present paper develops a model predictive control (MPC)- based approach to address the joint PEV charging scheduling and power control to minimize both PEV charging cost and energy generation cost in meeting both residence and PEV power demands. Unlike in related works, no assumptions are made about the probability distribution of PEVs' arrivals, the known PEVs' future demand, or the unlimited charging capacity of PEVs. The proposed approach is shown to achieve a globally optimal solution. Numerical results for IEEE benchmark power grids serving Tesla Model S PEVs show the merit of this approach

Energy and throughput efficient strategies for heterogeneous future communication networks

As a result of the proliferation of wireless-enabled user equipment and data-hungry applications, mobile data traffic has exponentially increased in recent years.This in-crease has not only forced mobile networks to compete on the scarce wireless spectrum but also to intensify their power consumption to serve an ever-increasing number of user devices. The Heterogeneous Network (HetNet) concept, where mixed types of low-power base stations coexist with large macro base stations, has emerged as a potential solution to address power consumption and spectrum scarcity challenges. However, as a consequence of their inflexible, constrained, and hardware-based configurations, HetNets have major limitations in adapting to fluctuating traffic patterns. Moreover, for large mobile networks, the number of low-power base stations (BSs) may increase dramatically leading to sever power consumption. This can easily overwhelm the benefits of the HetNet concept. This thesis exploits the adaptive nature of Software-defined Radio (SDR) technology to design novel and optimal communication strategies. These strategies have been designed to leverage the spectrum-based cell zooming technique, the long-term evolution licensed assisted access (LTE-LAA) concept, and green energy, in order to introduce a novel communication framework that endeavors to minimize overall network on-grid power consumption and to maximize aggregated throughput, which brings significant benefits for both network operators and their customers. The proposed strategies take into consideration user data demands, BS loads, BS power consumption, and available spectrum to model the research questions as optimization problems. In addition, this thesis leverages the opportunistic nature of the cognitive radio (CR) technique and the adaptive nature of the SDR to introduce a CR-based communication strategy. This proposed CR-based strategy alleviates the power consumption of the CR technique and enhances its security measures according to the confidentiality level of the data being sent. Furthermore, the introduced strategy takes into account user-related factors, such as user battery levels and user data types, and network-related factors, such as the number of unutilized bands and vulnerability level, and then models the research question as a constrained optimization problem. Considering the time complexity of the optimum solutions for the above-mentioned strategies, heuristic solutions were proposed and examined against existing solutions. The obtained results show that the proposed strategies can save energy consumption up to 18%, increase user throughput up to 23%, and achieve better spectrum utilization. Therefore, the proposed strategies offer substantial benefits for both network operators and users

Cognitive wireless sensor network platform for cooperative communications

Nowadays, Wireless Ad Hoc Sensor Networks (WAHSNs), specially limited in energy and resources, are subject to development constraints and difficulties such as the increasing RF spectrum saturation at the unlicensed bands. Cognitive Wireless Sensor Networks (CWSNs), leaning on a cooperative communication model, develop new strategies to mitigate the inefficient use of the spectrum that WAHSNs face. However, few and poorly featured platforms allow their study due to their early research stage. This paper presents a versatile platform that brings together cognitive properties into WAHSNs. It combines hardware and software modules as an entire instrument to investigate CWSNs. The hardware fits WAHSN requirements in terms of size, cost, features, and energy. It allows communication over three different RF bands, becoming the only cognitive platform for WAHSNs with this capability. In addition, its modular and scalable design is widely adaptable to almost any WAHSN application. Significant features such as radio interface (RI) agility or energy consumption have been proven throughout different performance tests

Open source software radio platform for research on cellular networked UAVs: It works!

Cellular network-connected unmanned aerial vehicles (UAVs) experience different radio propagation conditions than radio nodes on the ground. Therefore, it has become critical to investigate the performance of aerial radios, both theoretically and through field trials. In this article, we consider low-altitude aerial nodes that are served by an experimental cellular network. We provide a detailed description of the hardware and software components needed to establish a broadband wireless testbed for UAV communications research using software radios. Results show that a testbed for innovation in UAV communications and networking is feasible with commercial off-the-shelf hardware, open source software, and low-power signaling.This work was in part supported by NSF award CNS-1939334.Peer ReviewedPostprint (author's final draft

Design and implementation of components for renewably-powered base-stations with heterogeneous access channel

Providing high-speed broadband services in remote areas can be a challenging task, especially because of the lack of network infrastructure. As typical broadband technologies are often expensive to deploy, they require large investment from the local authorities. Previous studies have shown that a viable alternative is to use wireless base stations with high-throughput point to point (PTP) backhaul links. With base stations comes the problem of powering their systems, it is tackled in this thesis by relying on renewable energy harvesting, such as solar panels or wind turbines. This thesis, in the context of the sustainable cellular network harvesting ambient energy (SCAVENGE) project, aims to contribute to a reliable and energy efficient solution to this problem, by adjusting the design of an existing multi-radio energy harvesting base station. In Western Europe, 49 channels of 8 MHz were used for analogue TV transmissions, ranging from 470 MHz (Channel 21) to 862 MHz (Channel 69); this spectrum, now partially unused due to the digital television (DTV) switch-over, has been opened to alternative uses by the regulatory authorities. Using this newly freed ultra high frequency (UHF) range, also known as TV white space (TVWS), can offer reliable low-cost broadband access to housings and businesses in low-density areas. While UHF transmitters allow long range links, the overcrowding of the TV spectrum limits the achievable throughput; to increase the capacity of such TVWS rural broadband base station the UHF radio has previously been combined with a lower-range higher throughput GHz radio like Wireless Fidelity (WiFi). From the regulatory constraints of TVWS applications arises the need for frequency agile transceivers that observe strict spectral mask requirements, this guided previous works towards discrete Fourier transform (DFT) modulated filter-bank multicarrier (FBMC) systems. These systems are numerically efficient, as they permit the up-and-down conversion of the 40 TV channels at the cost of a single channel transceiver and the modulating transform. Typical implementations rely on power-of two fast Fourier transforms (FFTs); however the smallest transform covering the full 40 channels of the TVWS spectrum is a 64 points wide, thus involving 24 unused channels. In order to attain a more numerically-efficient implemented design, we introduce the use of mixed-radix FFTs modulating transform. Testing various sizes and architectures, this approach provides up to 6.7% of energy saving compared to previous designs. Different from orthogonal frequency-division multiplexing (OFDM), FBMC systems are generally expected to be more robust to synchronisation errors, as oversampled FBMC systems can include a guard band, and even in a doubly-dispersive channel, inter-carrier interference (ICI) can be considered negligible. Even though sub-channels can be treated independently—i.e. without the use of cross-terms—they still require equalisation. We introduce a per-band equalisation, amongst different options, a robust and fast blind approach based on a concurrent constant modulus (CM)/decision directed (DD) fractionally-space equaliser (FSE) is selected. The selected approach is capable of equalising a frequency-selective channel. Furthermore the proposed architecture is advantageous in terms of power consumption and implementation cost. After focussing on the design of the radio for TVWS transmission, we address a multi-radio user assignment problem. Using various power consumption and harvesting models for the base station, we formulate two optimisation problems, the first focuses on the base station power consumption, while the second concentrates on load balancing. We employ a dynamic programming approach to optimise the user assignment. The use of such algorithms could allow a downsizing of the power supply systems (harvesters and batteries), thus reducing the cost of the base station. Furthermore the algorithms provide a better balance between the number of users assigned to each network, resulting in a higher quality of service (QoS) and energy efficiency.Providing high-speed broadband services in remote areas can be a challenging task, especially because of the lack of network infrastructure. As typical broadband technologies are often expensive to deploy, they require large investment from the local authorities. Previous studies have shown that a viable alternative is to use wireless base stations with high-throughput point to point (PTP) backhaul links. With base stations comes the problem of powering their systems, it is tackled in this thesis by relying on renewable energy harvesting, such as solar panels or wind turbines. This thesis, in the context of the sustainable cellular network harvesting ambient energy (SCAVENGE) project, aims to contribute to a reliable and energy efficient solution to this problem, by adjusting the design of an existing multi-radio energy harvesting base station. In Western Europe, 49 channels of 8 MHz were used for analogue TV transmissions, ranging from 470 MHz (Channel 21) to 862 MHz (Channel 69); this spectrum, now partially unused due to the digital television (DTV) switch-over, has been opened to alternative uses by the regulatory authorities. Using this newly freed ultra high frequency (UHF) range, also known as TV white space (TVWS), can offer reliable low-cost broadband access to housings and businesses in low-density areas. While UHF transmitters allow long range links, the overcrowding of the TV spectrum limits the achievable throughput; to increase the capacity of such TVWS rural broadband base station the UHF radio has previously been combined with a lower-range higher throughput GHz radio like Wireless Fidelity (WiFi). From the regulatory constraints of TVWS applications arises the need for frequency agile transceivers that observe strict spectral mask requirements, this guided previous works towards discrete Fourier transform (DFT) modulated filter-bank multicarrier (FBMC) systems. These systems are numerically efficient, as they permit the up-and-down conversion of the 40 TV channels at the cost of a single channel transceiver and the modulating transform. Typical implementations rely on power-of two fast Fourier transforms (FFTs); however the smallest transform covering the full 40 channels of the TVWS spectrum is a 64 points wide, thus involving 24 unused channels. In order to attain a more numerically-efficient implemented design, we introduce the use of mixed-radix FFTs modulating transform. Testing various sizes and architectures, this approach provides up to 6.7% of energy saving compared to previous designs. Different from orthogonal frequency-division multiplexing (OFDM), FBMC systems are generally expected to be more robust to synchronisation errors, as oversampled FBMC systems can include a guard band, and even in a doubly-dispersive channel, inter-carrier interference (ICI) can be considered negligible. Even though sub-channels can be treated independently—i.e. without the use of cross-terms—they still require equalisation. We introduce a per-band equalisation, amongst different options, a robust and fast blind approach based on a concurrent constant modulus (CM)/decision directed (DD) fractionally-space equaliser (FSE) is selected. The selected approach is capable of equalising a frequency-selective channel. Furthermore the proposed architecture is advantageous in terms of power consumption and implementation cost. After focussing on the design of the radio for TVWS transmission, we address a multi-radio user assignment problem. Using various power consumption and harvesting models for the base station, we formulate two optimisation problems, the first focuses on the base station power consumption, while the second concentrates on load balancing. We employ a dynamic programming approach to optimise the user assignment. The use of such algorithms could allow a downsizing of the power supply systems (harvesters and batteries), thus reducing the cost of the base station. Furthermore the algorithms provide a better balance between the number of users assigned to each network, resulting in a higher quality of service (QoS) and energy efficiency