Modeling and Simulation of a Solar Farm Using Homer Pro Software for University of Maiduguri Senate Complex

Energy supply, its demand and security issues for developed and emerging economies

SummaryFloating photovoltaic (FPV) plants present several benefits in comparison with ground-mounted photovoltaics (PVs) and could have major positive environmental and technical impacts globally. FPVs do not occupy habitable and productive areas and can be deployed in degraded environments and reduce land-use conflicts. Saving water through mitigating evaporation and improving water security in arid regions combined with the flexibility for deployment on different water bodies including drinking water reservoirs are other advantages of FPVs. They also have higher efficiency than ground-mounted PV solar and are compatible with the existing hydropower infrastructures, which supports diversifying the energy supply and its resilience. Despite the notable growth of FPVs on an international scale, lack of supporting policies and development roadmaps by the governments could hinder FPVs’ sustainable growth. Long-term reliability of the floating structures is also one of the existing concerns that if not answered could limit the expansion of this emerging technology.

<p>This research accounts for the outcome of a machine learning algorithm project to predict short term solar and wind power in Ontario. A Long Short Term Memory (LSTM) model was developed to monitor short term power output predictions from 4 hours ahead to 6 hours ahead. This study demonstrates a unique approach of utilizing nearby publicly available meteorological data to predict renewable energy power output that could potentially be used for wind and solar farm scheduling to prevent curtailment of clean energy. The wind power and the solar power was predicted 6 and 4 hours ahead with a coefficient of correlation (R2) of 0.817 and 0.738 respectively. It has demonstrated the usage of a LSTM network as a reliable tool for the prediction of renewable energy that can be implemented to power output on systems that require accurate prediction of wind and solar power within the 4 to 6 hours. </p>

<p>This research accounts for the outcome of a machine learning algorithm project to predict short term solar and wind power in Ontario. A Long Short Term Memory (LSTM) model was developed to monitor short term power output predictions from 4 hours ahead to 6 hours ahead. This study demonstrates a unique approach of utilizing nearby publicly available meteorological data to predict renewable energy power output that could potentially be used for wind and solar farm scheduling to prevent curtailment of clean energy. The wind power and the solar power was predicted 6 and 4 hours ahead with a coefficient of correlation (R2) of 0.817 and 0.738 respectively. It has demonstrated the usage of a LSTM network as a reliable tool for the prediction of renewable energy that can be implemented to power output on systems that require accurate prediction of wind and solar power within the 4 to 6 hours. </p>

An Ecological Context That Scars: Reflections on Three Nature Documentaries of 2020

In order to adapt and lead the new norm of economic development, the structural reform of the supply side is a major strategic step in the face of the new situation of economic development both at home and abroad. The development of wind power, solar energy and other clean energy is the right direction of the energy supply side reform, and the characteristics of electricity production and consumption determine the central position of clean energy in the structural reform of the energy supply side. On the basis of summarizing the existing methods of evaluating the capacity of wind power (photovoltaic power station) consumption, this paper calculates the number of wind power (photovoltaic power station)consumption in the whole year by means of the balance of the whole cycle and the hourly power balance and the rational operation mode of pumped storage. The method calculates the change of the quantity of wind power (photovoltaic power station) consumption in the whole year in the case of different wind power(photovoltaic power station) installed capacity and determines the reasonable scale of wind power (photovoltaic power station) installed capacity, which can greatly improve the accuracy of wind power (photovoltaic power station) acceptance. Through the empirical test, it proves that this method has strong guiding significance for power grid planning and improves the consumption capacity of wind power (photovoltaic power station).

Multi-Objective Short-Term Integration of Hydrothermal Operation with Wind and Solar Power using Nonlinear Programming.

This study presents a technoeconomic analysis of a hybrid wind‐PV (photovoltaic) power plant (HPP) compared to onshore wind power plants (WPPs) and photovoltaic power plants (PVPPs) in the Nordic electricity market, focusing on locations in Finland and Sweden. Wind power capacity has recently increased significantly in the Nordics, increasing the profit cannibalization of wind power. Renewable energy subsidies have been phased out in Finland and Sweden, thus new wind and PV power value creation is formed from the power market. The PV power capacity has also encountered significant growth in the Nordics. However, the capacity is still relatively low, allowing more revenue for produced PV power compared to wind power. The lower PV power profit cannibalization has increased interest in HPPs instead of WPPs. This contribution studies the economic feasibility of wind and PV power in changing market conditions in the Nordic electricity market. The market operation is modeled with three different configurations including selling all the power into the day ahead spot market and baseload or pay‐as‐produced power purchase agreement (PPA). In addition, a battery energy storage system (BESS) investment is analyzed using the operating strategy of shifting production to more profitable spot price hours. This study shows that due to the profit cannibalization and high cost of capital, the power plants are currently not profitable in the Nordic electricity market except when the bidding area has high average spot prices. The worst profitability was with WPPs when exposed to the market shape risk and with PVPPs when pay‐as‐produced PPA was agreed upon due to the higher levelized cost of electricity. However, the PV power profit cannibalization is expected to increase in the future as more PVPPs operate in the Nordic power market. Thus, the PVPP shape risk may increase in the future as well.

In recent years, wind power and photovoltaic power generation have developed rapidly, and the installed proportion of wind power and photovoltaic power will further increase in the future. Aiming at the strong uncertainty of wind power and photovoltaic power, a scene generation method of wind and solar active power output based on k-medoids clustering and generative adversarial networks is proposed to avoid problems of model convergence and gradient disappearance, meanwhile preserving the diversity of generated results. Using the active power output data of two wind power plants and six photovoltaic power plants in Western China for 365 days a year, the data samples are clustered into ten scenarios by k-mediods method, which are used as training sets under different typical scenarios. The distribution characteristics of active power output data are effectively fitted by the generative adversarial networks, and its generation effect is analyzed. The results show that the scene generation method of wind and solar active power output based on k-medoids clustering and generative adversarial networks has the advantages of fast convergence and high precision.

Power density is the rate of energy generation per unit of land surface area occupied by an energy system. The power density of low-carbon energy sources will play an important role in mediating the environmental consequences of energy system decarbonization as the world transitions away from high power-density fossil fuels. All else equal, lower power densities mean larger land and environmental footprints. The power density of solar and wind power remain surprisingly uncertain: estimates of realizable generation rates per unit area for wind and solar power span 0.3–47 We m−2 and 10–120 We m−2 respectively. We refine this range using US data from 1990–2016. We estimate wind power density from primary data, and solar power density from primary plant-level data and prior datasets on capacity density. The mean power density of 411 onshore wind power plants in 2016 was 0.50 We m−2. Wind plants with the largest areas have the lowest power densities. Wind power capacity factors are increasing, but that increase is associated with a decrease in capacity densities, so power densities are stable or declining. If wind power expands away from the best locations and the areas of wind power plants keep increasing, it seems likely that wind’s power density will decrease as total wind generation increases. The mean 2016 power density of 1150 solar power plants was 5.4 We m−2. Solar capacity factors and (likely) power densities are increasing with time driven, in part, by improved panel efficiencies. Wind power has a 10-fold lower power density than solar, but wind power installations directly occupy much less of the land within their boundaries. The environmental and social consequences of these divergent land occupancy patterns need further study.

BackgroundLand use for the conversion of energy from renewable sources into electrical energy is increasingly competing with cultural landscapes and natural areas. It is anticipated that by 2050, solar energy generation will have increased by a factor of 15, which will result in a considerable expansion of the land area required for photovoltaic (PV) power plants on a global scale. An increase in the efficiency of PV modules and an optimisation of the space usage for PV power plant construction will contribute to a reduction in the expected environmental impact on land use. This study represents an empirical investigation into the European development of specific energy and area-relevant key performance indicators of free-field PV power plants. It employs a comprehensive sample drawn from diverse European geographical locations from different installation years.MethodsThis study investigated the evolution of various location-independent and location-dependent system parameters over time, using a sample of 107 free-field PV power plants across diverse European regions from 2006 to 2022 related to the fenced area. The investigations concentrated on the land use per installed power, land use per module area, land use per generated electrical energy, generated electrical energy per PV module area, energy density, capacity factor, and power density. The determined data provide the first European average life cycle inventory data, disaggregated by year and location, for environmental life cycle assessment. To facilitate a comparison of the system parameters of PV power plants with those of other renewable energy technologies, a further database was employed, including 89 power plants from the biomass, wind power, geothermal energy, solar thermal energy, and photovoltaic sectors. The selected samples were compiled from this database to compare the area-specific energy yields of both data sources.ResultsThe European trends for free-field PV power plants demonstrate a 60% reduction in specific land use per installed power and land use per generated electrical energy over the study period. In 2022, the median values were 14 m2/kW and 0.011 m2.a/kWh, respectively. The analysis indicates that three significant technological advances have occurred at approximately 5-year intervals. At the mounting design level, the land use per module area for conventional fixed-tilt row systems decreased by 30%. Overall, the mean land usage of all the considered PV power plants is threefold greater than the module area over the entire study period. Likewise, the results show that the high land usage caused by tracking systems is entirely compensated for by a relatively high energy yield, which presents an opportunity to develop innovative designs for multiple-use systems. A comparison of PV power plants with other renewable energy power plants reveals that solar thermal heat is distinctly superior in terms of the energy yield received per unit area.ConclusionsTo minimise land use, it is recommended that minimum energy efficiency requirements should be defined for new free-field PV power plants in addition to an optimised mounting design within the fenced area. The high energy yield of tracking systems, which have comparatively large row/pole distances, provides the opportunity for multiple uses of the ground area. Furthermore, the discrepancy in energy yield between northern and southern Europe underscores the need for a more comprehensive European planning strategy with regard to the future location of free-field PV power plants. To realise energy transition in the future, it will also be essential to consider all energy potentials together rather than to focus on isolated and small-scale initiatives. The policy changes require Europe-wide coordination, coupled with tailored national and regional definitions. Integrated spatial and energy planning could be a potential avenue for achieving this challenging aim.

Deep hybrid neural net (DHN-Net) for minute-level day-ahead solar and wind power forecast in a decarbonized power system

Accurate weather forecasts enhance sustainability by facilitating decision making across a broad range of endeavors including public safety, transportation, energy generation and management, retail logistics, emergency preparedness, and many others. This paper presents a method for combining multiple scalar forecasts to obtain deterministic predictions that are generally more accurate than any of the constituents. Exponentially-weighted forecast bias estimates and error covariance matrices are formed at observation sites, aggregated spatially and temporally, and used to formulate a constrained, regularized least squares regression problem that may be solved using quadratic programming. The model is re-trained when new observations arrive, updating the forecast bias estimates and consensus combination weights to adapt to weather regime and input forecast model changes. The algorithm is illustrated for 0-72 hour temperature forecasts at over 1200 sites in the contiguous U.S. based on a 22-member forecast ensemble, and its performance over multiple seasons is compared to a state-of-the-art ensemble-based forecasting system. In addition to weather forecasts, this approach to consensus may be useful for ensemble predictions of climate, wind energy, solar power, energy demand, and numerous other quantities.

The residual load is defined by energy demand minus energy production from renewable sources (wind and solar power). High values of residual load correspond to a high energy demand that cannot be covered by renewable energies and must be compensated with fossil energies. On the opposite, low/negative values of residual load correspond to a surplus of renewable energies with a too low demand. In this case, energy must be exported, or wind turbines and solar panels curtailed.We compute the residual load using a demand model and a solar and wind power model. For the solar power model, we use the CAMS solar energy dataset interpolated to the position of all single solar panels installed in Germany and model the solar power output with the PVlib Python package. The wind power model is using ERA5 windspeed and air density interpolated to turbine location and turbine hub height to be later fed in the power curve of all single turbines in Germany. The demand model is a linear regression of 2m-temperature from ERA5, industry production index, day-of-week, public holidays and installed solar capacity. The residual load is then given by: ResLoad = Demand - (Wind + Solar)We look at both tails of the distribution of the residual load. High values of residual load are mostly observed in winter when temperatures and renewable energy production are low (mostly during blocked regimes). These events correspond to high energy prices. Negative values of residual load are mostly observed on Sundays, public holidays and in summer, where demand is low and temperatures and solar and wind power production are high. They usually correspond to negative energy prices. Both events described above are critical for the energy industry. Skillful forecasts of these events in the sub-seasonal range help to reduce stress to the energy market. The results presented herein lay the foundation for a more comprehensive, statistical analysis of the predictability characteristics of residual load extremes.

<p>Low production of renewable energy for up to several days can cause stress on the electricity system, especially when such events coincide with high demand. Meteorological conditions leading to extremely low production of wind and PV power are known. These are periods of relatively little irradiance and anomalously weak winds. Past work underlined the importance of high-pressure systems over Central Europe during winter for causing such conditions. However, there is less known about low-production periods in other seasons and their regional scale characteristics from a climatological perspective. Despite being probably less severe in summer, low-production events can nevertheless be problematic. One example is the higher electricity demand for cooling systems in summer, which is especially relevant in the context of climate change.</p><p>Here we statistically analyse the synoptic meteorological conditions causing low-production events of wind and solar power in Europe for winter and summer. To this end, we use a daily weather regime classification for Europe from the German Weather Service, known as “Grosswetterlagen” (GWL). Our simulation of the production of wind and PV power is based on the reanalysis data COSMO-REA6 for Europe with a horizontal resolution of 6 km, and an established wind and PV power model, developed in the research area “Climate Monitoring and Diagnostics” of the Hans-Ertel Centre for Weather Research. Scenarios of gridded installed capacity of PV and wind power plants in Europe for 2050 are taken from the CLIMIX model to calculate hourly power production. Our composite analysis of the PV and wind power production associated with the different GWLs highlight (1) the regional differences in the power production in Europe across country borders and (2) different regional patterns of anomalies in power production depending on  GWLs. Based on our simulations, we derive an atlas of potential PV and wind production associated with different GWLs. This atlas will be helpful to understand which GWLs lead to regionally low-production events, and to what extend the severity of such events can be forecasted in different seasons. Such knowledge is important since the share of wind and solar power continues to increase in the European electricity grid.</p>