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The widespread adoption of photovoltaic (PV) systems results in reverse power flows that are already causing overvoltage problems in many distribution networks. To mitigate these voltage problems, distribution companies are using different voltage regulation approaches that exploit the flexibility of existing devices, such as on-load tap changers (OLTC) at primary substations, and the PV inverters themselves. However, the real-time process that determines the most adequate settings becomes more complex with the increasing number of devices that need to be controlled. This work presents a voltage regulation approach that uses the Machine Learning technique called Double Dueling Q-learning (DDQN) as an extremely fast alternative to coordinate the OLTC-fitted transformer at primary substations and the power factor of PV inverters. The proposed voltage regulation approach is assessed using a real Brazilian MV/LV feeder with 108 residential customers and 15 industrial/commercial customers, in which 60% of the residential customers have a PV system. Results validate the ability of using the DDQN control for voltage regulation in real time applications.

The growing amount of residential solar photovoltaic (PV) systems has increased the need for adequate three-phase low-voltage (LV) feeder models so distribution companies can carry out detailed studies that identify potential impacts and solutions. One of the major challenges, however, is that the phase group to which residential customers are connected to is largely unknown. Using voltage time-series data extracted from single-phase smart meters (a technology being deployed around the world), this paper proposes an approach based on Principal Component Analysis (PCA) and an unconstrained k-means clustering technique to determine the phase group of customers in a given LV feeder. The approach, which does not require prior knowledge (i.e., phase group of a few customers), is tested on a realistic Australian LV feeder with 31 customers and different PV penetrations. Results demonstrate that the approach is accurate without and with PV and practical as only smart meter data is required.

The widespread adoption of residential photovoltaic (PV) systems is causing voltage rise issues on low voltage (LV) networks. Operationally, voltage calculations can help determining specific settings (e.g., PV curtailment). From a planning perspective, voltage calculations can be used to assess the PV hosting capacity. However, voltage calculations normally require power flow analyses and, consequently, detailed three-phase LV network models which, in practice, are not readily available. This paper proposes a model-free voltage calculation approach that uses a Deep Neural Network (DNN) trained to capture the nonlinear relationships among historical single-phase smart meter data (P, Q, V) and the corresponding LV feeder. A methodology is proposed to determine hyperparameters and parameters suitable for the investigated LV feeder. Results using an Australian realistic LV feeder, demonstrate that the approach calculates voltages accurately for PV injections that are significantly different from the historical data, i.e., is suitable for any type of what-if scenario.

The operation of MV-LV networks with high penetrations of residential PV systems will require the active management of PV settings (curtailment). The conventional three-phase AC optimal power flow (OPF) has shown great potential, how-ever, its formulation as a single problem might not be scalable (i.e., solvable fast enough to be relevant). The added complexity due to multiple voltage levels and unbalance can further exacerbate this. An alternative is to break down the single OPF problem into multiple smaller subproblems but considering the interactions at their interfaces. Here, the distributed optimization algorithm alternating direction method of multipliers (ADMM) is adopted to solve a three-phase AC OPF that calculates PV set-tings in MV-LV networks to avoid voltage and congestion problems. Its performance is assessed for different numbers of LV net-works (subproblems). Results show that the proposed algorithm is not only accurate but much faster than the conventional approach for large distribution networks.

At project pre-selling time, customers and software providers collaborate to define the product required by the former; and based on it, the latter can generate a project proposal and a bid. This collaborative activity, that we have named product scoping, is not properly supported by the current tools and techniques, mainly due to the constraints imposed by the preselling phase; e.g., the activity has to be done quickly, accurately, and involving few resources. This article presents an instrument that supports the product scoping in bespoke projects, dealing with the main constraints involved in the pre-selling phase. The instrument is a canvas, and its design was particularly conceived to support the product scoping of data processing projects; however, the canvas can be adapted to address other project types. The instrument has been evaluated with real customers and providers, and the obtained results show that it helps conduct the product scoping process in a cost-effective way.

The software engineering research recognizes that small software companies are different to medium-sized and large organizations. Therefore, they require particular implementations of already known software practices to support their developments; this includes the estimation of the software development effort. The literature reports that estimation techniques based on expert judgement are preferred by the software industry in general, however, it is not clear what practices are used by small software companies and why they use them. Trying to clarify these aspects, particularly for small software companies in Chile, this article presents an empirical study based on semi-structured interviews that explores the effort estimation practices used by ten business stable organizations. The study results show the practices preferred by them, the reasons behind their preferences, and the expected accuracy of their estimates. The study also shows that using an appropriate estimation technique is not enough to keep the estimates under control.

Although the AC optimal power flow (OPF) has shown great potential for operating distribution networks, its formulation as a single problem might not be scalable (solvable fast enough to be relevant). This is exacerbated by the added complexity when multiple voltage levels and the unbalanced nature of distribution networks are considered. Distributed solution algorithms can help by breaking down a single OPF problem into multiple smaller problems. This paper explores the use of a three-phase AC OPF, solved in a distributed fashion by adopting the alternating direction method of multipliers (ADMM) algorithm, to manage residential PV systems in MV-LV distribution networks. The performance of the ADMM-based OPF is assessed using a test MV-LV feeder and compared with the conventional approach (solved as a single problem). Results show that the proposed algorithm is accurate, suggesting it has the potential to outperform the conventional approach when handling large-scale distribution networks.

Many electrical distribution networks around the world are already experiencing the massive integration of distributed energy resources, in particular, residential solar photovoltaic (PV) systems. As networks become more observable and controllable, coordinated and active management of all available assets will be required to prevent high PV penetrations from affecting network integrity. This paper explores the benefits and implementation challenges of using Q-learning, a Reinforcement Learning technique, to actively control the on-load tap changer (OLTC) at primary substations and, thus, mitigate voltage rise issues in PV-rich distribution networks. The training process occurs in both offline and online modes. Furthermore, some adjustments are proposed to improve scalability. As a case study, a real Brazilian three-phase MV/LV distribution network with 5,000+ customers is used considering a high PV penetration. Controlling only the OLTC, results demonstrate the importance of adequate reward functions (targeted at reducing voltage problems) and size of the training dataset.

Residential consumers with distributed energy resources such as solar PV and batteries, also known as prosumers, are becoming increasingly common. This, in turn, has created the opportunity to provide system-level services through aggregators. However, as the number of residential prosumers providing services increases, low voltage (LV) network constraints must be considered. This paper explores the use of day-ahead, time-varying operating envelopes (limits) calculated for each prosumer with an Optimal Power Flow (OPF) that incorporates the constraints of three-phase LV networks. The concept of weighted proportional fairness is adopted and implemented through the maximization of a logarithmic objective function. Once defined, these envelopes are used by aggregators to manage their portfolios accordingly. Using real Australian LV networks, consumer and pricing data, it is shown that the proposed day-ahead operating envelopes not only ensure network integrity but also allow aggregators to manage their portfolios more effectively when compared with unfair objective functions.

The increasing penetration of residential photovoltaic (PV) systems is displacing synchronous generation, causing serious impacts on the provision of primary frequency response (PFR). However, residential PV systems could also be part of the solution by being pre-curtailed to help with PFR. This requires considering not only the changes on system-level dispatch and PFR requirements, but also distribution network constraints to avoid overestimating PV availability. In this work, the distribution network is first analyzed to estimate feasible volumes of PV generation. The results then inform the system-level simulation, where a unit commitment determines the level of pre-curtailment needed for PV systems to provide PFR. The methodology is demonstrated using a modified IEEE 9-bus system and an Australian MV-LV distribution network. Results show that, distribution network constraints must be considered for realistic quantifications of system-level effects. Crucially, by sourcing PFR from PV systems, overall system cost can be reduced.

The growing integration of distributed energy resources (DER) into distribution networks is increasing the complexity of planning and operational tasks carried out by distribution network operators (DNOs). To achieve this in a timely manner, they need new advanced analysis tools to adequately quantify the impacts and the benefits from potential solutions to be seen in future distribution networks. In this context, this paper presents in detail the architecture and implementation of an advanced geographic information system (GIS)-based planning tool, named OpenGIS, to assess the impacts of DER on distribution networks, and to investigate potential solutions for thermal and/or voltage problems. OpenGIS provides a user-friendly graphical user interface, flexible analysis with detailed results in a GIS environment, and is built through the integration of free-license software packages. A real UK low voltage network with residential photovoltaic (PV) systems is used to demonstrate the different features of the tool.

As residential battery energy storage (BES) and solar PV systems are becoming increasingly popular, the opportunity arises for these devices to be used in the provision of services, enabling customers to further benefit from their investment. This, however, requires an adequate household-level control strategy to maximize customer benefits considering revenues (provision of energy services), cost (grid imports), and the inherent near real-time changes in demand and generation. This work proposes a time-composite rolling-horizon optimization for the provision of energy services, where a mix of high and low granularity periods are used in a mixed-integer linear optimization problem. The effectiveness of the approach is demonstrated in a year-long case study considering real smart meter demand, generation as well as pricing data from Victoria, Australia. The results show that while customers experience a small increase in grid imports, the revenues from the provision of energy services lead to an overall net benefit.

Thanks to recent advances in computing power, orchestrating the operations of distributed energy resources (DER) and network assets in real-time (i.e., decisions within minutes) using advanced schemes is becoming a plausible concept. To this end, hardware-in-the-loop (HIL) simulation can become a powerful technique allowing advanced control schemes to be demonstrated in a realistic environment. However, building a HIL-based platform can be quite challenging as it involves specialized software and hardware from multiple domains. To help researchers carrying out similar studies, this paper presents the architecture and the implementation details of a HIL-based platform used to demonstrate real-time management of active distribution networks. The platform is comprised of a real-time simulator used as a proxy of an actual network and a control room-like environment created using off-the-shelf solutions. An AC Optimal Power Flow-based control scheme to manage DER and network assets is applied to a UK-style test network for demonstration purposes.

Most commercially available residential PV systems in the world are single-phase 220-240 V systems with rated power up to 8 kWp. In Brazil, LV networks are typically three-phase four-wire. Houses have a single-phase connection (127 V phase-to-neutral) for most loads and a two-phase connection (220 V phase-to-phase) for large loads such as air conditioners or electric showers. Due to the voltage level, single-phase PV systems are being connected phase-to-phase (ungrounded). In this type of connection, the absorption of reactive power, a strategy used to reduce voltages, can have unintended consequences. This paper presents the modelling of this type of connection, laboratory tests, and an impact analysis on a real Brazilian LV network. Results demonstrate that reactive power absorption can actually exacerbate voltage rise and unbalance issues. Consequently, in Brazil and other countries with similar LV networks, it is imperative for PV integration studies to adequately model their phase-phase connection.

The rapid uptake of residential-scale photovoltaic (PV) systems in low-voltage (LV) networks is creating technical issues such as voltage rise and asset congestion (i.e., transformer, cables). An alternative to traditional time-consuming and expensive mitigation approaches, such as network reinforcements, could be to actively manage controllable elements such as PV systems. This work proposes an adaptive centralized asset congestion management (ACACM) technique. It is generic as it only requires knowledge of the total PV installed capacity, the rated capacity of the assets and measurements at the substation. Based on irradiance measurements, it estimates total PV generation and demand, making it possible to constantly calculate the maximum PV generation that avoids congestion issues. Its effectiveness is demonstrated using a real UK LV network with 100% PV penetration. The generic nature of the ACACM makes it a practical and scalable solution to manage PV-rich LV networks.

The growing adoption of residential battery energy storage systems (BESS) and photovoltaic (PV) systems will eventually reduce demand, forcing more synchronous generation offline. As BESS and PV penetrations increase, system frequency excursions following contingencies are expected to become more significant due to the reduced inertia and primary frequency response (PFR). In this context, the ability of BESS to quickly inject or absorb active power could be used to mitigate frequency excursions. This work proposes an analytical approach to quantify the response required from residential BESS to achieve a specified quasi steady-state frequency following a contingency at any given time. An analysis is carried out to verify whether BESS can deliver the required PFR considering different PV and BESS penetrations. Applying this approach to the IEEE 9-bus test system, this paper demonstrates the relationship among time, BESS droop and their rated power capacity to ensure the provision of the required PFR.

The popularity of residential solar PV systems in Australia has paved the way for the growing adoption of battery energy storage (BES) systems. To bring even more benefits to customers, Australian companies are developing control schemes for residential BES systems to also respond to price signals. However, depending on the time at which a high price signal is provided, the resulting power injection from the BES system can be coincident with that of the solar PV. If unrestricted, this combined injection could exacerbate reverse power flows and the associated effects, potentially posing a risk to the operation of the electricity infrastructure. Using an optimization-based price-led control scheme, this paper demonstrates probabilistically the impacts that such BES operation might have on a real Australian MV-LV network with 4,626 residential customers. Findings highlight the need for the electricity sector to adopt appropriate measures to prevent such issues from emerging in the future.

Residential photovoltaic (PV) adoption has seen continued growth over the past decade. Of concern is the impact on the dynamic performance of the power system resulting from the displacement of rotating mass, where inadequate damping can potentially lead to the damage of online generating units and other ramifications. Here, a holistic approach is proposed to assess small signal stability in time and quantify the effects on damping when PV displaces synchronous generation. A unit commitment is used to determine the true displacement from net demand changes throughout the day. The oscillatory modes associated with units remaining online are tracked using participation factors to identify which units are problematic and when. The methodology is applied to a modified IEEE 9-bus system. Results show that as the criticality of modes change with more PV penetration, the tracking of modes allows identifying where and when additional measures are required to achieve satisfactory damping.

Residential-scale photovoltaic (PV) inverters available in the market are embedded with two key functions designed to help mitigating voltage rise issues: Volt-Watt and Volt-var. Volt-var has attracted significant attention as it does not involve generation curtailment. However, residential-scale PV inverters are sized (kVA) to meet the peak active power generation of the panels (kW). Since active power generation is typically prioritized, their capabilities to absorb reactive power (kvar) during high generation periods will be limited. Using a real UK LV network, this work provides a comprehensive analysis to understand the extent to which the adoption of Volt-var control helps managing voltage rise issues. Considering different Volt-var curves and PV penetrations, results demonstrate that Volt-var control is ineffective when reactive power is needed the most. Furthermore, reactive power increases currents and, therefore, asset utilization. This highlights the importance of considering the kVA rating of inverters when adopting solutions involving reactive power.

The development of software systems that support socio-technical activities, also known as collaborative or groupware systems, has historically been carried out in an ad hoc manner given its particular nature. However, the scientific literature and practical experience have been demanding for more systematization of these developments, due to the increasing need to count on these systems in several application domains, like education, healthcare, tourism, disaster relief and productive activities. In order to shed light on this matter, this paper presents a survey on literature of software strategies to support the development of collaborative systems. The study analyzes, classifies and compares these strategies considering several criteria. The results indicate that there is no development method or strategy that covers the whole lifecycle of these applications, but various of them are complementary, opening thus an opportunity to advance the state-of-the-art in this study domain.

This letter describes a voltage control scheme which maximises the exported active power from generation-dominated networks. The proposed scheme exploits recent theoretical advances which rigorously characterise the origins of power losses in electrical networks. In situations where load-driven losses can be disregarded, these theoretical advances simplify the calculations and data inputs required by an export-maximizing optimal control scheme. UCD Energy Institute

The declining cost of residential energy storage and photovoltaic (PV) systems will soon make it possible for many customers to significantly reduce their energy dependency from the grid. However, since off-the-shelf storage systems are controlled for the sole benefit of the customer, distribution network impacts resulting from high PV penetrations might still occur. This paper quantifies the extent to which residential storage systems (i.e., batteries), can mitigate technical problems across low (LV) and medium voltage (MV) networks. A stochastic assessment is carried out considering a real Australian 22kV feeder along with a realistic LV network as well as real smart meter data (demand and PV generation) from Victoria. It is demonstrated that off-the-shelf storage systems are limited in their ability to reduce voltage and thermal issues. Nonetheless, it is shown that slight control changes could unlock significant network benefits at the expense of higher customer grid dependency.

The electrification of the transport sector is posed to create challenges but also opportunities for the electricity system. In this transition, it is crucial to understand the charging behavior of electric vehicles (EVs) so detailed studies can be carried out. However, to date, EV data is scarce. This paper proposes the use of probability density functions based on Gaussian Mixture Models (GMMs) to represent key charging metrics of EVs. These GMMs are then combined to produce realistic EV profiles needed in diverse studies. Real data from 221 EVs part of the largest trial in the UK and Europe (My Electric Avenue) is used to demonstrate the approach. The importance of using these realistic profiles is illustrated by comparing three studies with those adopting data based on travel surveys or small-scale trials. Results demonstrate that using realistic profiles avoids under or overestimations; thus, ensuring better planning and operation of electricity networks.

Distribution networks have been traditionally designed with limited monitoring and controllability. Therefore, most Conservation Voltage Reduction (CVR) schemes have been implemented in MV networks. However, the deployment of on-load tap changers (OLTCs) and monitoring in LV networks creates the opportunity to actively manage voltages closer to end customers, unlocking further benefits. This work proposes an active CVR control scheme that, using limited monitoring, manages voltages in OLTC-enabled LV networks. A stochastic quantification process that caters for load uncertainties is used to assess the active control's annual benefits for different control cycles, considering realistic time-varying household profiles and models per day type and season (l-min resolution). Results on a real UK residential LV network model demonstrate the inadequacy of the well-known CVR factor to assess advanced control schemes. More importantly, the proposed approach significantly increases annual energy savings compared to fixed or active primary substation voltage reductions, regardless the control cycle.

Distribution network analyses have been traditionally carried out by sequentially processing computational tasks, i.e., without taking advantage of parallel processing now available in multi-core machines. However, future distribution networks require studies that cater for the uncertainties due to the location and behavior of loads and low carbon technologies, resulting in a much more computationally demanding environment. This work investigates the adoption of High Performance Computing (HPC) to accelerate probabilistic impact and control analyses carried out on residential low voltage (LV) networks with electric vehicles (EVs). First, the impacts of uncontrolled charging of EVs are quantified using a Monte Carlo-based approach using 1,000 time-series daily simulations per penetration level (i.e., 0–100%). Then, to mitigate these impacts, the coordinated management of the on-load tap changer and EVs is proposed considering a preventive control approach that caters for the uncertainties ahead (1,000 scenarios). Two real residential, underground UK LV networks considering realistic demand and EV load profiles (1-min resolution) are analyzed. Results show that the processing time for the impact analysis is reduced almost proportionally to the number of cores. From the control perspective, it is demonstrated that HPC can be a feasible and implementable alternative in the management of future Smart Grids.

The lack of interactivity in the classroom has been identified as an important barrier to make the learning processes more effective. Although several instructional strategies and dynamics have been proposed, including the use of supporting technology, the problem remains open. This paper explores the feasibility of using touchless hand gestures (THG) during lectures in order to promote and ease the students' participation, and thus increasing their interactivity. Particularly, this work focuses on supporting the students' participation from their own spot in the classroom, as a strategy to help them feel comfortable and increase their participation during lectures. After conducting a literature review, no proposals or guidelines helping developers create THG-based applications to support such a participation strategy for the classroom scenario were found. Therefore, the authors used the literature review and their own experience to define a set of design aspects that should be considered when developing this type of applications. The suitability of this design guideline was evaluated with a user study with two applications. The quantitative and qualitative results show the students' participation from their spot could be feasible of being used in practice and it could increase the interaction during lectures. Moreover, the proposed design guidelines are shown to be suitable to support the development of these applications.

The effectiveness of Conservation Voltage Reduction (CVR) in reducing energy consumption depends on the instantaneous load composition in the network which is affected by the types of customers. This work investigates the benefits from adequately considering residential customer types and their corresponding time-varying models in obtaining realistic CVR estimations. A Monte Carlo-based methodology for CVR assessment considering demand uncertainties is presented and applied to two real UK residential LV networks which have two types of customers: Domestic Unrestricted (no storage heaters) and Economy 7 (with a meter providing fixed hours of supply for storage heaters). The results show that the adequate modelling of Economy 7 customers, i.e., considering the resistive nature and fixed period of operation of the corresponding storage heaters, leads to larger energy reductions. Consequently, considering only Domestic Unrestricted models in LV networks with high proportions of Economy 7 customers can significantly underestimate the potential CVR benefits.

The large volumes of data that will be produced by ubiquitous sensors and meters in future smart distribution networks represent an opportunity for the use of data analytics to extract valuable knowledge and, thus, improve Distribution Network Operator (DNO) planning and operation tasks. Indeed, applications ranging from outage management to detection of non-technical losses to asset management can potentially benefit from data analytics. However, despite all the benefits, each application presents DNOs with diverse data requirements and the need to define an adequate approach. Consequently, it is critical to understand the different interactions among applications, monitoring infrastructure and approaches involved in the use of data analytics in distribution networks. To assist DNOs in the decision making process, this work presents some of the potential applications where data analytics are likely to improve distribution network performance and the corresponding challenges involved in its implementation.

Understanding the interactions between power and communication networks is becoming increasingly important, particularly in the context of Smart Grids. Therefore, simulation tools that allow the evaluation of these interactions will be vital in the near future. Co-simulators emerge as powerful alternatives that make it possible to integrate widely used power and communication network simulators. However, the process of building a co-simulator is a challenging task that involves knowledge from different areas such as power and energy systems, communication systems, and computer science. To support the development of advanced co-simulators and related studies, this paper presents the implementation details of a simple co-simulator that uses OpenDSS to simulate the power network, OMNeT++ to simulate the communication network, and a PHP Server and Python scripts to establish the interoperation of these software tools. The IEEE 4-bus test system integrated with a communication network is used to validate the implementation.

Many mobile collaborative applications use resource positioning to provide services to end-users. Depending on the features of the collaboration activity to be supported, some positioning methods are more suitable than others. Despite ample research in this area, it is not clear which positioning method is more appropriate to support a particular collaboration activity. This paper presents a survey of positioning methods that can be used to support mobile collaboration. It also characterizes and compares these methods in order to determine which ones are more suitable, depending on the type of activity to be supported. This information helps software designers make this decision when they are conceiving a new mobile collaborative system.

Due to the increasing adoption of domestic photovoltaic (PV) systems, the use of technologies such as on-load tap changer (OLTC)-fitted transformers, capacitor banks, and remote monitoring are being considered to mitigate voltage issues, particularly in European-style low voltage (LV) networks. Depending on the control strategy, however, the effects on customer voltages and control actions (e.g., tap changes or capacitor switching) can vary significantly. This work presents a framework to assess the performance of different OLTC-based control strategies in terms of voltage compliance with the standard BS EN50160 and the number of control actions. Three control strategies are proposed: constant set-point (CSC), time-based (TBC) and remote monitoring-based (RMC). A week-long Monte Carlo analysis is carried out considering a real UK LV network, three-phase power flows, different PV penetrations and seasonality. Results show that the TBC outperforms the CSC but at the expense of more tap changes. Due to the enhanced visibility, the RMC significantly increases the PV hosting capacity whilst limiting tap operations. Finally, when feeders have contrasting voltage issues, capacitor banks are found to provide additional flexibility and allow higher PV penetrations. These findings are expected to help the industry determining the benefits from OLTC-based solutions in future LV networks.

Collaborative learning activities have shown to be useful to address educational processes in several contexts. Monitoring these activities is mandatory to determine the quality of the collaboration and learning processes. Recent research works propose using Social Network Analysis techniques to understand students' collaboration learning process during these experiences. Aligned with that, this paper proposes the use of the indirect blockmodeling network analytic technique for monitoring the behaviour of different social roles played by students in collaborative learning scenarios. The usefulness of this technique was evaluated through a study that analysed the students' interaction network in a collaborative learning activity. Particularly, we tried to understand the structure of the interaction network during that process. Preliminary results suggest that indirect blockmodeling is highly useful for inferring and analysing the students' social roles, when the behaviour of roles are clearly different among them. This technique can be used as a monitoring service that can be embedded in collaborative learning applications.

The software engineering community recognizes that human factors of developers directly influence the behavior and performance of software teams. Typically, cohesive teams tend to develop software in a more coordinated and effective way, resulting in suitable products. Although these cohesive teams could be designed to try reaching such benefits, there is no recipe that allows us to do it in a simple way. Therefore, most of the software teams are formed using any other criteria, by losing thus the potential benefits of using cohesive teams. The problem of designing these teams is not only present in the software industry, but also in the academia; particularly in capstone courses, where the students run software projects in real or simulated scenarios. In order to help find a solution to this problem, particularly for academic settings, this paper proposes a heuristic to form software development teams, considering the psycho-social profile of the students. This heuristic was used to design teams for a software engineering project course at the University of Chile, and these teams' performance was compared to those from teams designed using random allocation of student to teams. The results indicate that teams designed using the heuristic were more effective in terms of internal communication and coordination than the other teams. Moreover, the former obtained products with a quality higher than the latter.

In this paper the statistical consistency of the single-phase Admittance Matrix-Based State Estimator (AMB SE) originally proposed for distribution systems is for the first time assessed and compared to that of the widely used Traditional SE (TSE). The AMB SE is an interesting alternative for distribution systems given the use of a constant Gain matrix, thus simplifying its implementation and significantly reducing the computational time. However, it is crucial to statistically assess its consistency so as to ensure estimated values and variances (outputs) are in agreement with the specified measurements and variances (inputs). This is carried out using a Monte Carlo analysis and a generic 16-bus UK distribution system. Despite the conversion from actual to equivalent measurements required by the AMB SE, as well as the approximations regarding the variances of the equivalent measurements, the results demonstrate that the single-phase AMB SE is as consistent and precise as the TSE.

To truly quantify the impact of electric vehicles (EVs) on the electricity network and their potential interactions in the context of Smart Grids, it is crucial to understand their charging behavior. However, as EVs are yet to be widely adopted, these data are scarce. This work presents results of a thorough statistical analysis of the charging behavior of 221 real residential EV users (Nissan LEAF, i.e., 24kWh, 3.6 kW) spread across the UK and monitored over one year (68,000+ samples). Probability distribution functions (PDFs) of different charging features (e.g., start charging time) are produced for both weekdays and week-ends. Crucially, these unique PDFs can be used to create stochastic, realistic and detailed EV profiles to carry out impact and/or Smart Grid-related studies. Finally, the effects of the EV demand on future UK distribution networks are discussed.

Knowing the position of a device at a given time is fundamental to provide services tailored to the current user location. However, not all devices are able to perform positioning by their own, even when positioning systems are available in the environment where the user is located. This article presents a context-aware positioning model that allows devices participating in mobile ad-hoc networks, to sense their context and exchange positioning information with their peers. Thus, nodes knowing their own position could share it with their neighbors, allowing the latter to perform positioning. We discuss the performance of the model in a simulated mobile outdoor environment, populated with different types of devices. Our experiment shows that nodes without positioning capabilities are able estimate their position, although with reduced accuracy compared to those using a direct positioning strategy, such as GPS.

The increasing adoption of residential-scale PV units raises concerns about the potential interactions with MV networks as large volumes of generation connected to one voltage level can limit the hosting capacity of the other. This work investigates such interactions from an integrated MV-LV perspective by analyzing the impacts that different PV penetrations in LV networks have on the hosting capacity of a real UK MV network. Realistic time-series load and PV profiles for a summer day are considered. It was found that the major limitation on the hosting capacity of MV-connected PV units is due to voltage issues of LV customers. In addition, results show that the largest MV-LV aggregated hosting capacity can be achieved by having most of the generation connected to LV rather than MV, highlighting the benefits of small-scale PV. These findings demonstrate the need to adopt more advanced, integrated MV-LV models in future distribution network studies.

Network losses are often a key metric in evaluating the performance of planning and/or operational strategies. However, their assessment is traditionally carried out assuming a constant power load model that does not reflect the actual voltage-demand dependency, thus leading to inaccurate results. This work quantifies, in a real UK medium voltage (MV) network, the impact of three classic load models (constant power, current and impedance) on the quantification of energy and power network losses. A time-varying ZIP model designed for the UK residential demand is used as benchmark. Results indicate that the constant power load model, although underestimates the network losses throughout the year, outperforms the other models during summer (maximum error of 3% on power and 1.5% in energy). However, during winter the constant current model showed the best performance. The constant impedance model led to the highest errors and, consequently, should be in general avoided.

(accepted) The significant decline of reactive power (Q) demand in the UK is considered one of the factors that led in 2011 to over-voltages in the 400kV network. To examine this, a distribution network known to have this decline is modelled in detail (from the transmission interface, 132kV, to 33kV) and validated using monitoring data. In particular, the effects from replacing overhead lines with cables as well as P and Q demand trends (at primary substations) on Q exchanges at the GSP are investigated. Results for this particular distribution network show that high cable penetrations in 132kV circuits lead, during minimum demand, to Q gains many times larger than its 2013 level. In addition, the identified P and Q demand trends at primary substations highlight the future larger Q injections through the transmission interface. These findings can be used to coordinate operational and planning actions between networks operators.

This work proposes a statistical top-down methodology for energy loss estimation in medium voltage (MV) distribution systems. A statistical model is used to adjust the load parameters (i.e., ZIP coefficients) of the aggregated load allocated to each secondary transformer along the MV feeder. This adjustment process also results in the estimation of the corresponding energy losses. The information required by the proposed methodology is limited to the feeder topology, conductors, rated capacity of the transformers, and the voltage and power measurements at the primary substation during the period of analysis. If available, additional information from meters installed along the feeder can be used to improve the estimation. To illustrate the approach, a real Brazilian 13.8kV feeder is used. The results, compared with other methodologies available in the literature, demonstrate the benefits of the proposed methodology.

Smartphones are powerful sensing devices that can help us capture not only information from the environment, but also the users' activities. Based on that information, mobile systems can identify the user's behavioural patterns and adjust their services to better fit his needs. This paper presents a software infrastructure, named Collaborative Sensing Platform (CoSP), which facilitates the automatic collection and collaborative distribution of the data gathered from smartphone sensors. CoSP also helps to save energy during the sensing and data sharing processes. This platform was evaluated empirically in a laboratory experiment. The preliminary results indicate that this infrastructure is useful not only for data gathering and sharing, but also for reducing the battery consumption involved in the sensing tasks.

The increasing adoption of domestic-scale photovoltaic (PV) systems in the UK is likely to bring significant technical voltage rise issues in low voltage (LV) networks. This work investigates the techno-economic benefits from using on-load tap changers (OLTC)-fitted transformers to cope with high penetrations of PV. Two voltage regulation approaches are considered: local (busbar) and remote (furthest point). Results are contrasted with traditional network reinforcements. High resolution profiles for residential load and PV systems are used on a real UK LV network. The findings show that the OLTC-fitted transformer increases the hosting capacity of the network. The remote approach combined with adequate OLTC design performs better than the local one but the former is only needed for high penetration levels (from 70%). Finally, it is shown that the reinforcement alternative is more cost-effective for smaller penetration levels (up to 60%) in the network studied and for current prices.

This paper presents a methodology for mid-term load forecasting using Artificial Neural Networks (ANN). The inputs to ANN are real time data available from Supervisory Control and Data Acquisition and Distribution Management Systems (SCADA/DMS) databases. Due to a number of reasons, historical data stored in SCADA/DMS databases is affected by distorted measurements that can jeopardize the load forecasting results. This paper explores mid-term load demand forecasting using ANN considering distorted measurements in SCADA/DMS database. Proposed technique was applied to real-world measurements acquired from a 8.3 kV substation in Venezuela. ANN's forecasted results are compared with an exponential smoothing load forecasting procedure.

Background: Software engineering is a core subject in computing education. Today, there seems to be a consensus that teaching software engineering requires students to perform practical experiences that simulate the work in the software industry. This represents a challenge for universities and instructors, because these experiences are complex to setup and involve considerable time and effort. Although there are several experiences and proposals reported in the literature, there is no clear solution to address this challenge. Aim: Being knowledgeable about the several approaches reported in the literature for dealing with this challenge is the first step to proposing a new solution. Counting on this knowledge allows instructors to reuse lessons learned from other universities. In order to address this challenge, we conducted a systematic mapping study that intends to answer the following questions: What are the main approaches used to address the practical experiences in software engineering education? Is there an emerging tendency to address this challenge? Which software process models are used to support the practical experiences in software engineering courses? Have the universities changed the way of conducting these experiences over the years? What are the main forums to seek information on practical approaches for teaching software engineering? Method: We used a systematic mapping study to identify and classify available research papers that report the use of practical experiences in software engineering education. Results: There were 173 papers selected, analyzed and classified. The results indicate that universities have realized the value of including practical experiences as part of the software engineering teaching process. However, few proposals indicate how to address that challenge. The practical approaches identified in this study were game learning, case studies, simulation, inverted classrooms, maintenance projects, service learning, and open source development. Only one recent report on the use of traditional approaches (i.e., teaching using expositive lectures) was found. The use of a development process to support these practical experiences seems not to be a concern for software engineering instructors. Only 40% of these studies report the use of a development process to guide the process experience. The reported processes are mainly agile methods. Conferences are the most used forum to publish studies in this area (72%). One third of these studies have been published over the last five years. Conclusion: There is a clear concern for teaching software engineering involving practical experiences, and there are several initiatives exploring how to do it. The map gives us an overview of the different proposals to address this challenge, and also allows us to make some preliminary conclusions about the preferred approaches.

The installation of small-scale photovoltaic (PV) systems in European low voltage (LV) networks is continuously increasing given the attractive incentives adopted by countries such as the UK and Germany. In many cases, these installations are clustered in the same feeder or LV network, potentially resulting in voltage rise issues. To address this in a flexible way, here it is proposed the real-time intelligent control of LV on-load tap changer (OLTC)-fitted transformers to regulate voltages at the LV feeders. The performance of the proposed control logic is investigated adopting remote voltage monitoring of the end points as well as estimated values. The proposed control logic is applied to a real UK LV network considering one-minute simulations for a day. The results of both the monitoring and estimation-based approaches are compared for different PV penetrations. The control logic with remote monitoring successfully kept voltages of customers within the EN50160 standard. A strong performance was also shown when using estimated values. This highlights the potential applicability of such an approach.

The recent growth of social networking and sensing applications has enabled people to perform crowd computing. In turn, this activity has opened several opportunities to address people's needs in various application areas, such as tourism, security, entertainment and emergency response. However, the design of these applications has also brought several challenges to software designers. Since users of these systems interact among them in heterogeneous physical scenarios, their interactions should be formally considered in the design of these applications in order to determine whether or not the services embedded in the system are suitable to support those interactions. The lack of guidelines to address this modeling issue not only jeopardizes the suitability of these applications, but also implies the services provided by the system can be evaluated only after implementation, which is clearly risky and expensive. This article proposes an initial modeling language that allows software designers to address this challenge through the representation of the interaction among users of a system. The interaction model represented using the proposed language allows designers to evaluate, at the design time, the information flow and the availability of interaction supporting services in the system. The usability and usefulness of the proposal are shown using a running example.