WePWEP: web-based participatory wind energy planning [1]. Background information on wind energy and wind farm siting.

This document has been prepared in the frame of a PhD research project, which aim is to develop and test a learning-enhancing website design to involve the public in spatial planning. The application focused is the strategic planning of wind farms location. The website developed is named WePWEP – Web-based Participatory Wind Energy Planning and is available at hppt://ernie.ge.ucl.ac.uk:8080/WePWEP/. Being the purpose of the website to contribute to learning and engage the public in the strategic planning of wind farms, it provides some background information on wind energy and wind farm siting. This document compiles the information that is available in the website. With regard to wind energy, the section dedicated to the debate surrounding wind energy should be of particular relevance for those interested in an overview of the arguments pro and against wind energy development. Under the wind farm siting topic, the factors that need consideration during the site selection process are introduced, and subsequently the involvement of the public in wind farms planning is reviewed and discussed. The document concludes with the author supporting a more participative role of the public in the wind energy planning process and suggesting that the WePWEP website is a means that can contribute to this achievement

Real-time Modelling, Diagnostics and Optimised MPPT for Residential PV Systems

The work documented in the thesis has been focused into two main sections. The first part is centred around Maximum Power Point Tracking (MPPT) techniques for photovoltaic arrays, optimised for fast-changing environmental conditions, and is described in Chapter 2. The second part is dedicated to diagnostic functions as an additional tool to maximise the energy yield of photovoltaic arrays (Chapter 4). Furthermore, mathematical models of PV panels and arrays have been developed and built (detailed in Chapter 3) for testing MPPT algorithms, and for diagnostic purposes.In Chapter 2 an overview of the today’s most popular MPPT algorithms is given, and, considering their difficulty in tracking under variable conditions, a simple technique is proposed to overcome this drawback. The method separates the MPPT perturbation effects from environmental changes and provides correct information to the tracker, which is therefore not affected by the environmental fluctuations. The method has been implemented based on the Perturb and Observe (P&O), and the experimental results demonstrate that it preserves the advantages of the existing tracker in being highly efficient during stable conditions, having a simple and generic nature, and has the benefit of also being efficient in fast-changing conditions. Furthermore, the algorithm has been successfully implemented on a commercial PV inverter, currently on the market. In Chapter 3, an overview of the existing mathematical models used to describe the electrical behaviour of PV panels is given, followed by the parameter determination for the five-parameter single-exponential model based on datasheet values, which has been used for the implementation of a PV simulator taking in account the shape, size ant intensity of partial shadow in respect to bypass diodes.In order to eliminate the iterative calculations for parameter determinations, a simplified three-parameter model is used throughout Chapter 4, dedicated to diagnostic functions of PV panels. Simple analytic expressions for the model important parameters, which could reflect deviations from the normal (e.g. from datasheet or reference measurement) I −V characteristic, is proposed.A considerable part of the thesis is dedicated to the diagnostic functions of crystalline photovoltaic panels, aimed to detect failures related to increased series resistance and partial shadowing, the two major factors responsible for yield-reduction of residential photovoltaic systems.Combining the model calculations with measurements, a method to detect changes in the panels’ series resistance based on the slope of the I − V curve in the vicinity of open-circuit conditions and scaled to Standard Test Conditions (STC) , is proposed. The results confirm the benefits of the proposed method in terms of robustness to irradiance changes and to partial shadows.In order to detect partial shadows on PV panels, a method based on equivalent thermal voltage (Vt) monitoring is proposed. Vt is calculated using the simplified three-parameter model, based on experimental curve. The main advantages of the method are the simple expression for Vt, high sensitivity to even a relatively small area of partial shadow and very good robustness against changes in series resistance.Finally, in order to quantify power losses due to different failures, e.g. partial shadows or increased series resistance, a model based approach has been proposed to estimate the panel rated power (in STC). Although it is known that the single-exponential model has low approximation precision at low irradiation conditions, using the previously determined parameters it was possible to achieve relatively good accuracy. The main advantage of the method is that it relies on already determined parameters (Rsm, Vt) based on measurements, therefore reducing the errors introduced by the limitation of the single-exponential model especially at low irradiation conditions

Assessing the role of fluctuating renewables in energy transition: Methodologies and tools

Due to the environmental impacts brought by current energy schemes, the energy transition, a new paradigm shift from fossil fuels to renewable energy, has been widely accepted and is being realized through collective international, regional, and local efforts. Electricity, as the most direct and effective use of renewable energy sources (RES), plays a key role in the energy transition. In this paper, we first discuss a viable pathway to energy transition through the electricity triangle, highlighting the role of RES in electricity generation. Further, we propose methodologies for the planning of wind and solar PV, as well as how to address their uncertainty in generation expansion problems. Finally, by using a web-based tool, “RES-PLAT”, we demonstrate the scheme in a case study in Egypt, which evaluates the impacts and benefits of a large-scale RES expansion

Methods and tools to evaluate the availability of renewable energy sources

The recent statements of both the European Union and the US Presidency pushed in the direction of using renewable forms of energy, in order to act against climate changes induced by the growing concentration of carbon dioxide in the atmosphere. In this paper, a survey regarding methods and tools presently available to determine potential and exploitable energy in the most important renewable sectors (i.e., solar, wind, wave, biomass and geothermal energy) is presented. Moreover, challenges for each renewable resource are highlighted as well as the available tools that can help in evaluating the use of a mix of different sources

Integrating Technology Into Wildlife Surveys

Technology is rapidly improving and being incorporated into field biology, with survey methods such as machine learning and uncrewed aircraft systems (UAS) headlining efforts. UAS paired with machine learning algorithms have been used to detect caribou, nesting waterfowl and seabirds, marine mammals, white-tailed deer, and more in over 19 studies within the last decade alone. Simultaneously, UAS and machine learning have also been implemented for infrastructure monitoring at wind energy facilities as wind energy construction and use has skyrocketed globally. As part of both pre-construction and regulatory compliance of newly constructed wind energy facilities, monitoring of impacts to wildlife is assessed through ground surveys following the USFWS Land-based Wind Energy Guidelines. To streamline efforts at wind energy facilities and improve efficiency, safety, and accuracy in data collection, UAS platforms may be leveraged to not only monitor infrastructure, but also impacts to wildlife in the form of both pre- and post-construction surveys. In this study, we train, validate, and test a machine learning approach, a convolutional neural network (CNN), in the detection and classification of bird and bat carcasses. Further, we compare the trained CNN to the currently accepted and widely used method of human ground surveyors in a simulated post-construction monitoring scenario. Last, we establish a baseline comparison of manual image review of waterfowl pair surveys with currently used ground surveyors that could inform both pre-construction efforts at energy facilities, along with long-standing federal and state breeding waterfowl surveys. For the initial training of the CNN, we collected 1,807 images of bird and bat carcasses that were split into 80.0% training and 20.0% validation image sets. Overall detection was extremely high at 98.7%. We further explored the dataset by evaluating the trained CNN’s ability to identify species and the variables that impacted identification. Classification of species was successful in 90.5% of images and was associated with sun angle and wind speed. Next, we performed a proof of concept to determine the utility of the trained CNN against ground surveyors in ground covers and with species that were both used in the initial training of the model and novel. Ground surveyors performed similar to those surveying at wind energy facilities with 63.2% detection, while the trained CNN fell short at 28.9%. Ground surveyor detection was weakly associated with carcass density within a plot and strongly with carcass size. Similarly, detection by the CNN was associated with carcass size, ground cover type, visual obstruction of vegetation, and weakly with carcass density within a plot. Finally, we examined differences in breeding waterfowl counts between ground surveyors and UAS image reviewers and found that manual review of UAS imagery yielded similar to slightly higher counts of waterfowl. Significant training, testing, and repeated validation of novel image data sets should be performed prior to implementing survey methods reliant upon machine learning algorithms. Additionally, further research is needed to determine potential biases of counting live waterfowl in aerial imagery, such as bird movement and double counting. While our initial results show that UAS imagery and machine learning can improve upon current techniques, extensive follow-up is strongly recommended in the form of proof-of-concept studies and additional validation to confirm the utility of the application in new environments with new species that allow models to be generalized. Remotely sensed imagery paired with machine learning algorithms have the potential to expedite and standardize monitoring of wildlife at wind energy facilities and beyond, improving data streams and potentially reducing costs for the benefit of both conservation agencies and the energy industry

Strategic environmental assessment (sea) supporting the transition to renewable energy in South Africa

Abstract : This study addresses the criticism that environmental impact assessment in South Africa, may pose a threat to achieving the development objectives of the National Development Plan, thereby impacting on the ability of the country to deal with the challenges of poverty, inequality and joblessness. The purpose of this study is to identify possible contributions that implementing sector specific Strategic Environmental Assessments (SEAs) can make to improving the efficiency and effectiveness of the project-level impact assessment process, using the renewable energy sector as a pilot. The experience of implementing the energy SEAs will be used to enhance the existing SEA design criteria to enable SEAs to be more easily utilised by the government to influence decision-making and contribute to sustainable development. Possible successes in this sector could translate to other sectors and improve the ability to meet the countries development objectives while promoting sustainability. The energy focus of this study relates to the global concern relating to the impacts of climate change and the commitment of the South African government to transition the country towards a low carbon economy. As part of realising this objective, renewable energy technologies are being advanced through the implementation of the Renewable Energy Independent Power Producers Procurement Programme (REI4P). This programme, which is based on competitive bidding, will see 17.8 GW of renewable energy introduced into the energy mix by 2030. As a pre-bid requirement, prospective bidders must undertake a project level Environmental Impact Assessment (EIA) and be in possession of an Environmental Authorisation. This requirement resulted in over 900 applications for environmental authorisation being submitted for consideration for the first phase of bidding, of which only 9% proceeded to construction. These statistics point to inefficiencies within the procurement and authorisation processes and highlights the need to move to a strategic approach when implementing large scale priority development projects. Data gathering included the review and evaluation of four commercial scale wind-energy environmental impact assessments and two energy sector strategic environmental assessments against previously researched EIA and SEA effectiveness criteria. This research contributes to the debate on the effectiveness of SEA with an emphasis on designing SEAs for implementation. The research would be of interest to environmental practitioners, government and scholars of integrated environmental management.D.Phil. (Geography