Sustainable Energy for Smart Cities

The electric mobility dissemination is forcing the adoption of new technologies and operation paradigms, not only focusing on smart grids, but also on smart homes. In fact, the emerging technologies for smart homes are also altering the conventional grids toward smart grids. By combining the key pillars of electric mobility and smart homes, this paper characterizes the paradigms of the electric vehicle (EV) in smart homes, presenting a review about the state-of-the-art and establishing a relation with future perspectives. Since the smart home must be prepared to deal with the necessities of the EV, the analysis of both on-board and off-board battery charging systems are considered in the paper. Moreover, the inclusion of renewable energy sources, energy storage systems, and dc electrical appliances in smart homes towards sustainability is also considered in this paper, but framed in the perspective of an EV off-board battery charging system. As a pertinent contribution, this paper offers future perspectives for the EV in smart homes, including the possibility of ac, dc, and hybrid smart homes. Covering all of these aspects, exemplificative and key results are presented based on numerical simulations and experimental results obtained with a proof-of-concept prototype.

Rail transport has always been one of the greatest economic boosters of several world nations, allowing the freight and passenger transport. In addition, it is the most secure and economic land transportation mode. From the energetic perspective, the electric locomotives emerge as one of the most efficient land transportation mode, as well as allow a more sustainable development. However, when an electric locomotive is connected to the three-phase power grid, power quality (PQ) deterioration arise, leading to the distortion and unbalance of the three-phase power grid currents and voltages which imply higher operational costs, raising economic and functional issues. In order to overcome the PQ deterioration phenomena, several solutions based power electronics technology have been studied and developed. These solutions vary in terms of control, functionality, implementation costs and complexity. One of the existing solutions is a static synchronous compensator (STATCOM), which compensates the three-phase currents imbalance and harmonics.

In this paper, a comprehensive review of the electrified railway systems is carried out, identifying the electric PQ phenomena which may appear due to the non-linear dynamic traction loads. Following this topic, a computational simulation of the STATCOM is presented, making analysis of its behavior regarding the PQ improvement in electrified railway systems. Two case studies are presented: (i) a traction power system fed with V/V power transformer; (ii) a traction power system fed with Scott power transformer.

The huge power requirements of future railway transportation systems require the usage of energy efficient strategies towards a more intelligent railway system. With the usage of on-board energy storage systems, it is possible to increase the energy efficiency of railways. In this paper, a top-level charging controller for the on-board energy storage system is proposed based on a fuzzy logic controller. As an optimization procedure to increase the energy efficiency of such charging controller, a genetic algorithm meta-heuristic is used to automatically tune the fuzzy rules weight. To validate the proposed controller, two sets of rules were defined, one considering only known rules and the other also considering all possible combinations of rules. As global results, the reduction of regenerated energy reached 30%, and the net energy consumption reduction is near 10%.

This paper presents the implementation and subsequent experimental verification of an electronic variable speed drive (VSD) for driving an induction motor, which is composed by a three-phase ac-dc converter on the grid-side and by a three-phase dc-ac converter on the motor-side. With the proposed solution, besides driving the motor, it is possible to mitigate power quality problems on the grid-side (e.g., current harmonics and power factor) associated with the use of diode-bridge ac-dc converters in the conventional VSDs. Besides, with the proposed solution, a bidirectional operation is possible, allowing to deliver to the power grid the energy generated in motor braking processes. As demonstrated along the paper, with the proposed VSD, it is possible to control the motor speed (including the rotation direction), and the operation with sinusoidal currents and unitary power factor on the grid-side. A laboratory prototype was developed, permitting to perform an experimental validation and prove the main functionalities of the VSD.

This paper presents an analysis of unified traction and battery charging systems for electric vehicles (EVs), both in terms of operation modes and in terms of implementation cost, when compared to dedicated solutions that perform the same operation modes. Regarding the connection of the EV battery charging system with the power grid, four operation modes are analyzed: (1) Grid–to–Vehicle (G2V); (2) Vehicle–to–Grid (V2G); (3) Vehicle–to–Home (V2H); and (4) Vehicle–for–Grid (V4G). With an EV unified system, each of these operation modes can be used in single–phase and three–phase power grids. Furthermore, a cost estimation is performed for an EV unified system and for dedicated systems that can perform the same functionalities, in order to prove the benefits of the EV unified approach. The cost estimation comprises two power levels, namely 6 kW, single–phase, related to domestic installations, and 50 kW, three–phase, related to industrial installations. The relevance of unified traction and battery charging systems for EVs is proven for single–phase and three–phase power grids.

The Internet of Things (IoT) is applied to many cases on the topic of smart cities. In the scope of this paper, we apply a flexible IoT-Developed platform, using LoRa communication, applied to university auditoriums in order to try and find patterns and/or anomalies in energy consumption and in the interior temperature. This platform enables the interested parties to monitor the energy consumption of lighting, of HVAC (Heating, Ventilation, and Air Conditioning) and if electrical plugs alongside the monitoring of temperatures, aiming to create a report about the efficient, the thermal insulation and the HVAC behavior. Based on the acquired information, a management strategy is applied to find that the lack of certain systems leads to an extreme waste of energy and the lack of proper cleaning procedures can lead to decreasing the efficiency of the HVAC.

In this paper, it is demonstrated the importance of the information concerning the power flow and the state of a building’s thermal system for the management and control of electricity consumption. The proposed approach is evaluated simulating the heating exchange behavior of a configurable base model for an isolated and non-thermally insulated habitat in order to determine the influence of the energy losses on the control system. Based on the results obtained with this study, some improvements were carried-out for the management system of the heating flow by introducing appropriated parameters into the control algorithm.

The rise in the accessibility of photovoltaic (PV) generators to consumers increases the possibility of reverse power flow (RPF) in the electric distribution system. RPF occurs when power flows to the design of the system. Overvoltage, power losses and protection system coordination are among the problems that could occur due to the presence of RPF. This paper describes an algorithm to detect the presence of RPF using optimally-placed micro-phasor measurement units (µPMUs) in the IEEE 34-Bus System with 5 PV generators. A machine learning algorithm based on a feedforward artificial neural network (ANN) was developed. The algorithm was able to detect the presence of RPF using (1) voltage and current and (2) polar- and (3) rectangular-impedance methods for training. The algorithm was also able to detect RPF under scenarios that were not used during the training process. Sensitivity analyses were performed for cases such as PV outage, PV relocation, PV addition, PV expansion and load increase. The susceptibility of the algorithm to true value errors (TVEs) was tested by adding error vectors on the µPMU measurements for both the training and testing populations.

Demand response has the potential to reduce end-users electricity costs by promoting judicious use of existing power system infrastructure. This is most often assumed to require the adoption of time-varying electricity prices which can make load scheduling and energy resource management difficult to carry out in a time-effective and comfortable way without computational assistance and automated control. Automated home energy management systems can facilitate this process including by providing users with optimised plans. Creating these plans requires optimisation tools operating on mathematical models of the underlying problem. Mixed-integer linear programming (MILP) has been used extensively for this purpose though increasing complexity and time resolution can render this approach impractical. In this paper, we describe and compare MILP formulations of the same demand response problems using alternative thermal load models. The results, obtained using a state-of-the-art solver, can be summarised as follows: (1) the elimination of continuous temperature variables in one thermal load submodel increased the computation time in 99% of cases and by 981% on average; (2) two new discrete control formulations leading to a 40% reduction in the number of binary variables relative to the standard formulation were found to decrease the computation time in approximately 63% of cases and by 38–40% on average. Efforts are ongoing to evaluate these techniques under more diverse scenarios.

This paper describes the development of a weather station integrating several sensors which allows the measurement and data storage of the following environmental parameters: solar irradiance, temperature, humidity, wind speed, and wind direction. The collected data is later transferred to a mobile device, where it is stored in a database and processed in order to be visualized and analyzed by the user. For such purpose, a dedicated mobile app was developed and presented along the paper. The weather station also integrates small solar photovoltaic modules of three different technologies: polycrystalline, monocrystalline and amorphous silicon. Based on that, the weather station also collects information that may be employed to help the user in determining the most suitable solar photovoltaic technology for installation in a particular location. The developed system uses a Bluetooth Low Energy (BLE) wireless network to transfer the data to the mobile device when the user approaches the weather station. The system operation was validated through experimental tests that encompass all the main developed features, from the data acquisition in the weather station, to the visualization in the mobile device.

This paper addresses the long-term power planning in interconnected system with high renewable share. A case close to the Portuguese electricity system was modeled to assess the relevance of the interconnection with Spain in future scenarios. The results show that the increase on the renewable energy share will lead to a higher total cost of the system mainly due to investment costs. On the other hand, CO₂ emissions will be significantly reduced for each scenario in question. Another significant result is that the increase on renewable power, will lead to an excess of electricity production mainly during winter. For a 100% renewable scenario, the importance of interconnection is demonstrated, in particular, for the summer months for which importation can compensate the reduction of wind and hydropower output.

The anticipated development of decentralized electricity generation is expected to strengthen the opportunities of prosumers in the residential areas of cities, in line with the predicted establishment of renewable energy generation and storage. Based on academic research and on successful case studies, the opportunity for residential prosumers to organize in microgrids emerges as a viable and promising solution. This paper focuses on microgrids that are planned to generate electricity with a PV unit and use a shared storage system, and that opt to have a connection with the main grid. However, the point of common coupling needs to be agreed first between the microgrid operator and the network operator, and this agreement is determined by several factors and conditions beyond the basic technical and regulatory requirements. A survey of academic literature on the determinant factors for such an agreement exposes the fact that current research either focuses on the integration of individual prosumers in the main grid, or regards the point of common coupling as a given component of microgrids. We argue that neither of the two approaches is helpful in the case of microgrids vs. main grid, seeing as the agreement is not self-evident under just any circumstances, nor can the microgrid be equated to a single, large prosumer. Therefore this short paper compiles a set of determinant factors for the microgrid integration, as they emerge from academic literature, with the aim to document further research needs and support the discussion on microgrid integration.

The continuing increase of photovoltaic (PV) generation in distribution systems comes with difficulties in keeping voltages within acceptable limits, especially during peak generation. Two conventional alternatives exist to solve these overvoltage issues: to install voltage regulation equipment (AVR) or curtail PV generation, but there is no existing procedure to aid distribution system operators (DSO) in choosing either solution from an economical perspective. This project presents a methodology to evaluate the two aforementioned alternatives. The equivalent annual cost of installing automatic voltage regulator systems in the network was compared to the annual compensation awarded to curtailed PV generator owners. Several case studies were explored and show that in some situations, curtailment can be more cost-effective depending on the curtailment compensation scheme used, amount of PV penetration, location of PV in the network, and demand profiles. Additionally, the researchers explored the economic viability of using curtailment in conjunction with existing AVR installations instead of installing additional AVRs.

In this paper, we present a distributed approach to optimise self-consumption on a university campus grid. The grid contains photovoltaic generators, electric vehicles, loads and a battery. We propose to solve the optimisation problem with a distributed method using game theory, where each element of the grid tries to reach its own objectives. In addition to this optimisation framework, we develop a physical model of the grid. This model uses real consumption and production data. We use it to simulate the production and consumption profiles obtained from the optimisation problem in order to check if these solutions respect the grid constraints. Finally, we propose to implement concretely this distributed approach using a private blockchain, which stores production and consumption data. In addition, a smart contract is deployed on the blockchain to transcribe the game theory framework. The smart contract collects the preferences of each element of the grid and launches the optimisation process. Then the blockchain gathers the results and replaces the role of a central optimisation supervisor. We present some preliminary results to illustrate our method.

This paper presents the development and test of an Internet of Things (IoT) system applied to the monitoring and control of an HVAC (Heating, Ventilation and Air Conditioning) system that includes parameters such as temperature, humidity, air quality, human presence and smoke detection. For this purpose, a hybrid wireless network combining Bluetooth Low Energy (BLE) and IEEE 802.11/Wi-Fi was implemented inside a house. An online database for the synchronization of the HVAC data, which was developed using the Amazon Web Services (AWS) cloud platform, allows the user to access the data and control the system parameters through the Internet using an Android mobile app. A smart temperature control system was also developed in the BLE/Wi-Fi gateway to keep the room temperature inside a user-defined range. The functionalities and performance of the proposed system were both validated through experimental tests.

The WaterAMI is an Integrated Management of Efficiency System (IMES) of Water Distribution Networks (WDN) supported in an Automated Metering Infrastructure (AMI). It has a positive impact in energy consumption and in the water management, on one hand decreasing the water losses, on the other, by measuring and controlling water resources as well as water demand, supported in data science by predictive analytics.

The communications between devices of WaterAMI are realized through a Low Power Wide Area Network - All for Everyone - Energy Aware (LPWAN-AfE-EA), developed by CWJ Power Electronics. The AfE-EA protocol uses a mesh topology that grants the coverage of all the water infrastructure’s devices, including devices placed in building’s basements, normally not covered by other IoT communications protocols.

In order to maximize the operational performance of entire network, AfE-EA uses an efficient math algorithm that computes efficiently the Optimal Hop-Constrained Maximum Capacity Spanning Tree (OH-CMCST), which maximizes the routing path energy capacity and minimizes the number of hops of a battery-operated or energy constrained AMI’s communications network, by taking in account the strengths of radio signal links and the State of Charge (SoC) of all batteries that power the smart sensors.

The WaterAMI is already installed and in full operation in several WDNs in Portugal. Where it solved constrains of previously installed similar systems.

This publication presents the main features of AfE-EA protocol, compares with other LPWANs and briefly describes AfE-EA implementation in the first application of WaterAMI in Portugal.

In recent years, there has been an increase in demand for food of animal origin. The number of intensive production systems such as pig and poultry farming has been increasing more and more and exerting great impacts on the environment, due to a large amount of particulate material and gaseous pollutants that are generated within these facilities. Thus, low-cost devices emerge as a cheap alternative that provides farmers with information on indoor air quality in its facilities. However, it is important that these devices make precise and accurate measurements, providing reliable concentration readings. Therefore, the objective of this study is the construction and validation of a low-cost system capable of measuring, storing and sending, via the mobile network, the concentrations of hydrogen sulfide, ammonia, carbon dioxide, PM_2.5, PM₁₀, temperature, and relative humidity. Preliminary inter-comparison tests showed that the built system had a reliable behavior in relation to all variables, even though the CO₂ sensor was the one with the highest determination coefficient. The built device is able to provide continuous monitoring of atmospheric pollutants concentrations, at low cost and with simple handling.

The problem of simultaneous localization and mapping (SLAM) has been extensively studied by using a variety of specialized sensors. In this paper, we show that the SLAM could be realized using a sensor-rich smartphone. We assume that an indoor pedestrian always carries a sounding smartphone and the pedestrian moves autonomously inside a room. At every step, the loudspeaker of the smartphone produces a chirp pulse (frequency band is in the upper of human hearing area), the microphone of this smartphone registers the echoes, and the inertial sensors record the accelerometer and gyroscope readings, then the position of the moving pedestrian and the geometry map of the room are done simultaneously. However, when in a rectangular room of regular shape, reconstructing the room geometry at each sound source position is quite redundant. To avoid this redundancy and improve the sound source localization performance, we address SLAM by Matrix Analysis-based geometry estimation, and then this information is applied to the real-time positioning requirements taking the advantage of multi-source information fusion concept. Finally, we show the effectiveness of the proposed SLAM method by experiments with real measured acoustic events, the result fully demonstrates that the proposed SLAM method could be easily implemented using the smartphone carried by the pedestrian.

This paper presents a load-shift system with advanced functionalities to interface the power grid (PG). When compared with the conventional approach, an advanced load-shift system (aLSS) permits the compensation of power quality (PQ) problems for the grid-side, namely problems related to current harmonics, current imbalance, and power factor. The proposed aLSS is composed by a bidirectional ac-dc converter to interface the PG and by a bidirectional dc-dc converter to interface an energy storage system (ESS). Since the main innovation is related with the PG interface, the focus of this work is on the analysis of the ac-dc converter, which is based on a three-phase four-leg converter. A theoretical study and the details concerning the control algorithm are presented and discussed along the paper. A laboratory prototype of the proposed aLSS was developed and the details of implementation are described in the paper. Experimental results obtained with the developed prototype prove that the aLSS contributes for the technology progress in this area, validating a new concept of operation concerning the PQ on the PG side.

Nowadays, the majority of electronic equipment behave as nonlinear loads, introducing Power Quality (PQ) problems into the Power Grid (PG), namely, current harmonics and low power factor. These PQ problems contribute to the reduction of the efficiency of the transmission and distribution PG, as well as induce the malfunctioning of sensitive loads connected to the PG. Therefore, the development of equipment able to mitigate these PQ problems is extremely important. In this context, this paper presents a novel single-phase Shunt Active Power Filter (SAPF) based on a current-source converter, where the key differencing factor, when compared with the conventional approach, is the reduced dc-link. As the proposed topology requires a reduced dc-link, it represents a relevant advantage, since a typical current-source converter needs an inductor with a high inductance in dc-link, which results in higher losses, costs and component sizing. The proposed SAPF with reduced dc-link is introduced in detail along the paper and a comprehensive comparison with the conventional SAPF is established based on computer simulations. Besides, an experimental validation was carried-out with a developed laboratory prototype, validating the main advantages of the proposed SAPF with reduced dc-link.

High levels of solar energy are a good orientation for the development of grid-connected power converters used in the interface of photovoltaic (PV) installations with the power grid. In this case, in order to define control strategies and the respective tariffs, the injected power into the grid must be accounted using an energy meter. Therefore, in this paper, the implementation of a three-phase smart energy meter for PV installations is presented, ensuring the registration of the electrical energy supplied to the grid. This three-phase energy meter consists of Hall-effect sensors, used to adapt the high voltages and currents with the analog circuits of the signal conditioning, which is connected to a DSP. The developed energy meter ensures data acquisition and processing and, based on that, the energy calculation and standardization in real time. As presented along the paper, a detailed metrology analysis was developed to identify the smart meter metrological characteristics. Through the experimental validations, it was possible to validate the main features of the developed smart energy meter for grid-connected PV installations.

Green Computing has been the trend among computer scientists for its eco-friendliness. It serves as a great solution to be integrated with Smart Grids (SG). Data stemming from SGs falls under the realm of Big Data as it is voluminous, various, and has a great velocity. Hence, these data need processing and storage. For this, High-Performance Computing, through clustering a set of computers, proves necessary. Nowadays, with the hardware advances that the world is witnessing, the Raspberry Pi (RP) creates a number of opportunities to deploy cost-effective and energy-efficient clusters, which respect the concepts of Green Computing. In this paper, we are presenting the work done within a USAID sponsored project which aims at developing a SG testbed at Al Akhawayn University in Ifrane, Morocco. We are presenting the deployment of a 5-node cluster based on RPs. The cluster has Hadoop installed and runs the TestDFSIO and Terasort benchmarks for the performance analysis in addition to an energy efficiency analysis.