Impact Evaluation of Solar Photovoltaic Electrification: Indigenous Community Case Study in Brazilian Amazon

Despite efforts to promote universal access to electrification, the Brazilian Amazon basin has around 82,000 families without electricity. The basin is huge, with few roads, many rivers, and conservative areas, which is an enormous challenge in terms of logistics and electrification costs. This paper describes a case study at the Nova Esperança community site in the Cuieiras River, Brazil. The community received stand-alone solar photovoltaic systems in 2018 and 2019. The process started with a survey and finished with an interview with each dweller that received a 975 W and 2-day autonomy photovoltaic system. A monitoring system was developed and deployed, and weather monitoring was performed to evaluate the impact of high temperatures on the equipment. The community does not have cell phone coverage and it is far from the main cities. We claim that the model created and adopted in the case study has interesting outcomes, even considering a small budget. Some houses, after 1 year of deployment, had their electrical demand rise by 300%, and 50% improved their income. We estimate the number of greenhouse gases annually avoided after electrification, replacing the consumed fossil fuel. The project also estimates the expenditure on energy sources that residents used due to the lack of electricity, which they stopped doing after electrification. The avoided expense can cover maintenance costs over the years. The goals of the SDG that were covered by the project are good health and well-being, accessible and clean energy, sustainable cities and communities, combating climate change, and partnerships for the goals

CPS Data Streams Analytics based on Machine Learning for Cloud and Fog Computing: A Survey

Cloud and Fog computing has emerged as a promising paradigm for the Internet of things (IoT) and cyber-physical systems (CPS). One characteristic of CPS is the reciprocal feedback loops between physical processes and cyber elements (computation, software and networking), which implies that data stream analytics is one of the core components of CPS. The reasons for this are: (i) it extracts the insights and the knowledge from the data streams generated by various sensors and other monitoring components embedded in the physical systems; (ii) it supports informed decision making; (iii) it enables feedback from the physical processes to the cyber counterparts; (iv) it eventually facilitates the integration of cyber and physical systems. There have been many successful applications of data streams analytics, powered by machine learning techniques, to CPS systems. Thus, it is necessary to have a survey on the particularities of the application of machine learning techniques to the CPS domain. In particular, we explore how machine learning methods should be deployed and integrated in cloud and fog architectures for better fulfilment of the requirements, e.g. mission criticality and time criticality, arising in CPS domains. To the best of our knowledge, this paper is the first to systematically study machine learning techniques for CPS data stream analytics from various perspectives, especially from a perspective that leads to the discussion and guidance of how the CPS machine learning methods should be deployed in a cloud and fog architecture

Incorporating forecasting and peer-to-peer negotiation frameworks into a distributed model predictive control approach for meshed electric networks

The continuous integration of renewable energy sources into a power network has caused a paradigm shift in energy generation and distribution. The intermittent nature of renewable sources affects the prices at which energy can be sold or purchased. In addition, the network is subject to operational constraints, voltage limits at each node, rated capacities for the power electronic devices, current bounds for distribution lines; these constraints coupled with intermittent renewable injections may pose a threat to system stability and performance. We propose a distributed predictive controller to handle operational constraints while minimising generation costs, and an agent based market negotiation framework to obtain suitable pricing policies, agreed among participating agents, that explicitly considers availability of energy storage in its formulation. The controller handles the problem of coupled constraints using information exchanges with its neighbours to guarantee their satisfaction. We study the effect of different forecast accuracy have on the overall performance and market behaviours. We provide a convergence analysis for both the negotiation iterations, and its interaction with the predictive controller. Lastly, We assess the impact of the information availability with the aid of testing scenarios

The Energy Revolution : Cyber Physical Advances and Opportunities for Smart Local Energy Systems

We have designed a two-stage, 10-step process to give organisations a method to analyse small local energy systems (SLES) projects based on their Cyber Physical System components in order to develop future-proof energy systems. SLES are often developed for a specific range of use cases and functions, and these match the specific requirements and needs of the community, location or site under consideration. During the design and commissioning, new and specific cyber physical architectures are developed. These are the control and data systems that are needed to bridge the gap between the physical assets, the captured data and the control signals. Often, the cyber physical architecture and infrastructure is focused on functionality and the delivery of the specific applications. But we find that technologies and approaches have arisen from other fields that, if used within SLES, could support the flexibility, scalability and reusability vital to their success. As these can improve the operational data systems then they can also be used to enhance predictive functions If used and deployed effectively, these new approaches can offer longer term improvements in the use and effectiveness of SLES, while allowing the concepts and designs to be capitalised upon through wider roll-out and the offering of commercial services or products

Incorporating forecasting and peer-to-peer negotiation frameworks into a distributed model predictive control approach for meshed electric networks

The continuous integration of renewable energy sources into a power network has caused a paradigm shift in energy generation and distribution. The intermittent nature of renewable sources affects the prices at which energy can be sold or purchased. In addition, the network is subject to operational constraints, voltage limits at each node, rated capacities for the power electronic devices, current bounds for distribution lines; these constraints coupled with intermittent renewable injections may pose a threat to system stability and performance. We propose a distributed predictive controller to handle operational constraints while minimising generation costs, and an agent based market negotiation framework to obtain suitable pricing policies, agreed among participating agents, that explicitly considers availability of energy storage in its formulation. The controller handles the problem of coupled constraints using information exchanges with its neighbours to guarantee their satisfaction. We study the effect of different forecast accuracy have on the overall performance and market behaviours. We provide a convergence analysis for both the negotiation iterations, and its interaction with the predictive controller. Lastly, We assess the impact of the information availability with the aid of testing scenarios