A decision support system for wind power production

Renewable energy production is constantly growing worldwide, and some countries produce a relevant percentage of their daily electricity consumption through wind energy. Therefore, decision support systems that can make accurate predictions of wind-based power production are of paramount importance for the traders operating in the energy market and for the managers in charge of planning the nonrenewable energy production. In this paper, we present a decision support system that can predict electric power production, estimate a variability index for the prediction, and analyze the wind farm (WF) production characteristics. The main contribution of this paper is a novel system for long-term electric power prediction based solely on the weather forecasts; thus, it is suitable for the WFs that cannot collect or manage the real-time data acquired by the sensors. Our system is based on neural networks and on novel techniques for calibrating and thresholding the weather forecasts based on the distinctive characteristics of the WF orography. We tuned and evaluated the proposed system using the data collected from two WFs over a two-year period and achieved satisfactory results. We studied different feature sets, training strategies, and system configurations before implementing this system for a player in the energy market. This company evaluated the power production prediction performance and the impact of our system at ten different WFs under real-world conditions and achieved a significant improvement with respect to their previous approach

Hydroelectric management on the Rio Chama: Balancing competing ecological priorities through non-consumptive flow management between the El Vado and Abiquiu reservoirs

Management of dammed river systems is a complex problem. Spatial and temporal impacts result in complex system trade-offs, and shareholders have competing objectives. Dynamic modeling can provide improved information as decision-makers attempt to optimize the value of river flows. This paper models the direct and indirect economic impacts of a small reservoir-dam-river system and applies this framework to an existing Bureau of Reclamation dam and generator in the upper Rio Grande basin. Over past decades, concerns for river habitat preservation have reduced the production of peak-demand energy from hydroelectric plants. Over the same period, as U.S. power markets incorporate solar and wind generation, the demand for flexible, quick-ramping energy during evening hours is increasing. Hydroelectric power can reduce greenhouse gas emissions by making grid integration of solar and wind power less costly and by directly substituting for dirtier alternative power sources. Economic modelling of market and non-market values associated with the system permits optimization of hydroelectric power to reduce emissions and support intermittent renewable integration without sacrificing ecological goals. A system dynamics model of the dam allows a cost-benefit analysis of dispatchable energy production in the presence of constraining daily, weekly or monthly ecological flow requirements. The case study suggests that constrained economical dispatch of existing small hydropower generators may be optimal both economically and ecologically. This model provides a scalable framework for incorporating the ecological benefits of hydropower flexibility into the cost-benefit analysis that drives maintenance, upgrade and decommissioning decisions for existing U.S. hydroelectric dams

Hydroelectric management on the Rio Chama: Balancing competing ecological priorities

Management of dammed river systems is a complex problem. Spatial and temporal impacts result in complex system trade-offs, and shareholders have competing objectives. Dynamic modeling can provide improved information as decision-makers attempt to optimize the value of river flows. This research models the direct and indirect economic impacts of a small reservoir-dam-river system and applies this framework to an existing Bureau of Reclamation dam and generator in the upper Rio Grande basin. Over past decades, concerns for river habitat preservation have reduced the production of peak-demand energy from hydroelectric plants. Over the same period, as U.S. power markets incorporate solar and wind generation, the demand for flexible, quick-ramping energy during evening hours is increasing. Hydroelectric power can reduce greenhouse gas emissions by making grid integration of solar and wind power less costly and by directly substituting for dirtier alternative power sources. Economic modelling of market and non-market values associated with the system permits optimization of hydroelectric power to reduce emissions and support intermittent renewable integration without sacrificing ecological goals. A system dynamics model of the dam allows a cost-benefit analysis of dispatchable energy production in the presence of constraining daily, weekly or monthly ecological flow requirements. The case study suggests that constrained economical dispatch of existing small hydropower generators may be optimal both economically and ecologically. This model provides a scalable framework for incorporating the ecological benefits of hydropower flexibility into the cost-benefit analysis that drives maintenance, upgrade and decommissioning decisions for existing U.S. hydroelectric dams

Intelligent Data Fusion for Applied Decision Support

Data fusion technologies are widely applied to support a real-time decision-making in complicated, dynamically changing environments. Due to the complexity in the problem domain, artificial intelligent algorithms, such as Bayesian inference and particle swarm optimization, are employed to make the decision support system more adaptive and cognitive. This dissertation proposes a new data fusion model with an intelligent mechanism adding decision feedback to the system in real-time, and implements this intelligent data fusion model in two real-world applications. The first application is designing a new sensor management system for a real-world and highly dynamic air traffic control problem. The main objective of sensor management is to schedule discrete-time, two-way communications between sensors and transponder-equipped aircraft over a given coverage area. Decisions regarding allocation of sensor resources are made to improve the efficiency of sensors and communications, simultaneously. For the proposed design, its loop nature takes account the effect of the current sensor model into the next scheduling interval, which makes the sensor management system able to respond to the dynamically changing environment in real-time. Moreover, it uses a Bayesian network as the mission manager to come up with operating requirements for each region every scheduling interval, so that the system efficiently balances the allocation of sensor resources according to different region priorities. As one of this dissertation\u27s contribution in the area of Bayesian inference, the resulting Bayesian mission manager is shown to demonstrate significant performance improvement in resource usage for prioritized regions such as a runway in the air traffic control application for airport surfaces. Due to wind\u27s importance as a renewable energy resource, the second application is designing an intelligent data-driven approach to monitor the wind turbine performance in real-time by fusing multiple types of maintenance tests, and detect the turbine failures by tracking the turbine maintenance statistics. The current focus has been on building wind farms without much effort towards the optimization of wind farm management. Also, under performing or faulty turbines cause huge losses in revenue as the existing wind farms age. Automated monitoring for maintenance and optimizing of wind farm operations will be a key element in the transition of wind power from an alternative energy form to a primary form. Early detection and prediction of catastrophic failures helps prevent major maintenance costs from occurring as well. I develop multiple tests on several important turbine performance variables, such as generated power, rotor speed, pitch angle, and wind speed difference. Wind speed differences are particularly effective in the detection of anemometer failures, which is a very common maintenance issue that greatly impacts power production yet can produce misleading symptoms. To improve the detection accuracy of this wind speed difference test, I discuss a new method to determine the decision boundary between the normal and abnormal states using a particle swarm optimization (PSO) algorithm. All the test results are fused to reach a final conclusion, which describes the turbine working status at the current time. Then, Bayesian inference is applied to identify potential failures with a percentage certainty by monitoring the abnormal status changes. This approach is adaptable to each turbine automatically, and is advantageous in its data-driven nature to monitor a large wind farm. This approach\u27s results have verified the effectiveness of detecting turbine failures early, especially for anemometer failures

On the use of MPT to derive optimal RES electricity generation mixes

The use of modern portfolio theory (MPT) is a common practice to derive efficient frontiers and support portfolio decision making in financial markets. Although real projects present different characteristics and technical restrictions, the general objective of the decision maker is the same: to maximize the expected return minimizing the portfolio risk. Long term electricity generation decision making is characterized by high uncertainty, high impact on social welfare and a large set of diversified technologies that may be included in future scenarios. The possibility of applying MPT approach to define efficient electricity generation portfolios is explored in this paper focusing on particular in renewable energy sources (RES technologies). The use of MPT for building RES scenarios is demonstrated for the particular case of Portugal. One year hourly data concerning power output from wind, hydro and solar plants along with the power demand was collected and included in the analysis. Three different approaches were considered for designing the efficient frontiers aiming at maximizing the RES electricity generation, minimizing deviation between the demand and the RES production and minimizing the levelised cost of the RES system. The results demonstrate how this approach can be an effective tool to support decision making but put also in evidence the need to build modified MPT models in order to take into account the technical restrictions of the system.QREN, COMPETE, FCT, under Project FCOMP-01-0124-FEDER-01137

GIS-Based Site Suitability Analysis for Wind and Solar Photovoltaics Energy Plants in Central North Region, Namibia

Increasing urbanisation and population growth are making it difficult for governments to achieve sustainable development. Provision of clean energy is among the seventeen sustainable development goals, as it reduces reliance on fossil fuels. In recent years, Namibia has rapidly increased her reliance on sustainable energy. The renewable energy sources (RESs), including wind and solar energy, can be described as clean sources which have lesser negative environmental impact compared to conventional energy sources. Amongst the pressing challenges today is finding solutions on efficient solar and wind energy production. It is imperative to work out the optimum location of RESs before installing them. This can significantly improve performance and establishes the foundation for studying both solar and wind power in a site selection problem. This study aims to determine potential locations for wind and solar photovoltaic (PV) energy plants installation using one of the multi-criteria decision-making (MCDM) methods, the analytical hierarchy process (AHP), and a geographic information system (GIS) within the Central North Regional Electricity Distributor (CENORED) supply area. Combining GIS with MCDM results in a powerful technique for selecting potential sites, since GIS provides effective analysis, manipulation, and visualization of geospatial data, whereas MCDM provides consistent weighing of criteria. In the evaluations of the location: topographical, environmental, climatic and regulations constraints were considered as factors that may facilitate or hinder the deployment of solarwind energy power plants. For solar PV energy plant, the highest potential areas are in the north-west, south-west and study area's southern regions, whereas for the wind power plant, only the northwest part is a highly suitable location for wind energy plants installation. These findings can be used to determine most favourable location of interest for solar PV and wind power plant development or to support the integration of electrical grid expansion and off-grid electrification strategies

Techno-economic analysis of solar PV and wind power plants with hydrogen energy storage systems as a frequency regulation provider

Renewable energy has emerged as a crucial component in the transition towards decarbonizing the power sector, offering a clean and sustainable alternative to fossil fuels. However, the integration of these variable renewable electricity sources diminishes system inertia and creates frequency regulation challenges. To address these operational and technical difficulties, energy storage devices are critical in support- ing grid stability. In particular, hydrogen storage can act as an intermediary for intermittent electricity production and thus contribute to a more balanced energy system in the coming years [2]. Therefore, this thesis presents a comprehensive techno-economic analysis of integrating solar and wind power plants with a hydrogen storage system to provide frequency regulation services. The study focuses on the profitability of participating in the Day-ahead Spot Market and explores the impact of fre- quency regulation provision on project profitability, over the project’s lifetime of 20 years. A case study in Sweden is used to evaluate the results obtained from an optimization model developed using the Gurobi solver. The findings demonstrate the significance of revenue from frequency regulation services in achieving overall profitability. Wind scenarios outperform solar scenarios due to higher renewable energy avail- ability. In the wind scenario, the analysis revealed a payback period of 16 years and a total hydrogen (H2) production of 582 tonnes. Notably, approximately 62% of the total revenue was attributed to participation in the Down-regulation frequency market, with the wind power plant emerging as the primary revenue- generating component. On the other hand, in the solar scenario, the payback period was extended to 19 years, accompanied by a total hydrogen production of 584 tonnes. In this case, the solar power plant accounted for the majority of revenue, and 51% of the total revenue of the project was attained through participation in the Down-regulation market. The future scenarios indicate that market landscapes and technological advancements will enhance economic indicators and make renewable power plants inte- grated with hydrogen energy storage systems projects more attractive. Additionally, a sensitivity analysis reveals the key factors influencing the Levelized Cost of Electricity, Net Present Value, and Payback Period in different scenarios. Finally, these research outcomes provide valuable insights for decision-making, emphasizing the im- portance of considering various factors, such as market prices, system size, system costs, and discount rates, in the techno-economic analysis of renewable energy projects. This study contributes to the existing knowledge in the field and offers guidance for stakeholders involved in renewable energy system planning and implementatio

Bidding decision of wind-thermal GenCo in day-ahead market

This paper deals with the self-scheduling problem of a price-taker having wind and thermal power production and assisted by a cyber-physical system for supporting management decisions in a day-ahead electric energy market. The self-scheduling is regarded as a stochastic mixed-integer linear programming problem. Uncertainties on electricity price and wind power are considered through a set of scenarios. Thermal units are modelled by start-up and variable costs, furthermore constraints are considered, such as: ramp up/down and minimum up/down time limits. The stochastic mixed-integer linear programming problem allows a decision support for strategies advantaging from an effective wind and thermal mixed bidding. A case study is presented using data from the Iberian electricity market. (C) 2016 Published by Elsevier Ltd.info:eu-repo/semantics/publishedVersio

Power and wind power: exploring experiences of renewable energy planning processes in Scotland.

Energy use and production have become highly salient within both national and international policy. This reflects an international recognition of the need to cut emissions in order to mitigate the threats of climate change. Within the UK there is significant policy support for renewable energy development generally, and wind power in particular. Nevertheless, the UK is not expected to meet its targets for renewable energy production. This is often portrayed as being the result of localised public opposition to particular proposed developments. However, this thesis challenges the notion that local objectors are powerful actors within renewable energy deployment. A detailed, multi-method case study of one planning application for a wind power development was conducted in order to explore how the planning process is experienced and perceived by various different actors involved (i.e. representatives of the developers, local objectors, local supporters). The findings refute the assertion that localised opposition presents significant obstacles for the development of renewable energy; they instead highlight the limited influence of objectors. In order to understand the many different forms of power which may be exercised the research employs Lukes three-dimensional view of power as a framework of how the concept is to be understood. Through this framework, the thesis does not only consider the power of objectors, but also of prospective developers and the forms of power that are found within the structures of the planning system. Power is considered to be visible not only in the outcomes of decision-making processes but also in the processes themselves. It is shown that whilst planning processes are presented as being public and democratic, considerable power is exercised in controlling the participation that is allowed and ultimately the range of outcomes which can be achieved