Techno-Economic Assessment of Wheat-derived hybrid evaporator with energy-confinement network for high-efficiency solar water purification andCarbon-Neutral concurrent electricity generation

As power systems shift towards increasing wind and solar electricity generation, inter-annual variability (IAV) of wind and solar resource and generation will pose increasing challenges to power system planning and operations. To help gauge these challenges to the power system, we quantify IAV of wind and solar resource and electricity generation across the Electric Reliability Council of Texas (ERCOT) power system, then assess the IAV of wind and solar electricity generation during peak-load hours (i.e. IAV of wind and solar capacity values) for the current ERCOT wind and solar generator fleet. To do so, we leverage the long timespan of four reanalysis datasets with the high resolution of grid integration datasets. We find the IAV (quantified as the coefficient of variation) of wind generation ranges from 2.3%–11% across ERCOT, while the IAV of solar generation ranges from 1.7%–5% across ERCOT. We also find significant seasonal and regional variability in the IAV of wind and solar generation, highlighting the importance of considering multiple temporal and spatial scales when planning and operating the power system. In addition, the IAV of the current wind and solar fleets’ capacity values (defined as generation during peak-load hours) are larger than the IAV of the same fleets’ capacity factors. IAV of annual generation and capacity values of wind and solar could impact operations and planning in several ways, e.g. through annual emissions, meeting emission reduction targets, and investment needs to maintain capacity adequacy.

Climate change is driving the energy sector with significant impacts on renewable electricity generation systems. The purpose of this study is to analyse the impact of climate change on solar and wind electricity generation in Aotearoa New Zealand projected to 2050. To have realistic and tangible results and reduce the uncertainties, climate change is modelled with three climate and economic scenarios. The annual electricity generation projected for 2050 is estimated through simulations conducted with SAM (System Advisor Model), using data provided by NIWA. The projected energy output in 2050 is compared with the electricity production of two solar farms and five wind farms in 2024. The results show that solar electricity generation will be similar to the data that the Electricity Authority captured for 2024, with some slight seasonal variations. Wind-generated electricity is likely to be more affectedby climate change, with a substantial increase in average wind speed in winter and spring, especially on the southern island. A decrease in wind in summer and autumn reduces wind-generated electricity. The risks to the reliability and stability of solar and wind power generation systems are particularly amplified by the increase in extreme weather events, such as intensified storms, atmospheric rivers, and floods. This study highlights the vulnerability of Aotearoa New Zealand’s energy sector to climate change, and the need for adaptation strategies. The recommendations include flexibility of power grid management with alternative sustainable electricity generation solutions and storage strategies and strengthening solar and wind farm infrastructure to make them more resilient and durable against extreme weather events.

A Systematic Literature Review on big data for solar photovoltaic electricity generation forecasting

Problems with erroneous forecasts of electricity production from solar farms create serious operational, technological, and financial challenges to both Solar farm owners and electricity companies. Accurate prediction results are necessary for efficient spinning reserve planning as well as regulating inertia and power supply during contingency events. In this work, the impact of several climatic conditions on solar electricity generation in Amherst. Furthermore, three machine learning models using Lasso Regression, ridge Regression, ElasticNet regression, and Support Vector Regression, as well as deep learning models for time series analysis include long short-term memory, bidirectional LSTM, and gated recurrent unit along with their variants for estimating solar energy generation for every five-minute interval on Amherst weather power station. These models were evaluated using mean absolute error root means square error, mean square error, and mean absolute percentage error. It was observed that horizontal solar irradiance and water saturation deficiency had a highly proportional relationship with Solar PV electricity generation. All proposed machine learning models turned out to perform well in predicting electricity generation from the analyzed solar farm. Bi-LSTM has performed the best among all models with 0.0135, 0.0315, 0.0012, and 0.1205 values of MAE, RMSE, MSE, and MAPE, respectively. Comparison with the existing methods endorses the use of our proposed RNN variants for higher efficiency, accuracy, and robustness. Multistep-ahead solar energy prediction is also carried out by exploiting hybrids of LSTM, Bi-LSTM, and GRU.

Problems with erroneous forecasts of electricity production from solar farms create serious operational, technological, and financial challenges to both Solar farm owners and electricity companies. Accurate prediction results are necessary for efficient spinning reserve planning as well as regulating inertia and power supply during contingency events. In this work, the impact of several climatic conditions on solar electricity generation in Amherst. Furthermore, three machine learning models using Lasso Regression, ridge Regression, ElasticNet regression, and Support Vector Regression, as well as deep learning models for time series analysis include long short-term memory, bidirectional LSTM, and gated recurrent unit along with their variants for estimating solar energy generation for every five-minute interval on Amherst weather power station. These models were evaluated using mean absolute error root means square error, mean square error, and mean absolute percentage error. It was observed that horizontal solar irradiance and water saturation deficiency had a highly proportional relationship with Solar PV electricity generation. All proposed machine learning models turned out to perform well in predicting electricity generation from the analyzed solar farm. Bi-LSTM has performed the best among all models with 0.0135, 0.0315, 0.0012, and 0.1205 values of MAE, RMSE, MSE, and MAPE, respectively. Comparison with the existing methods endorses the use of our proposed RNN variants for higher efficiency, accuracy, and robustness. Multistep-ahead solar energy prediction is also carried out by exploiting hybrids of LSTM, Bi-LSTM, and GRU.

95/04900 Events in New Zealand

Financial constraints on the development of solar energy— and suggested government action to mitigate such constraints

95/04897 Diffuse solar radiation correlations: Applications to Turkey and Australia

Tension structures for solar electricity generation

The purpose of this study was to investigate ways to accelerate the commercialization of solar electric-generating plants and their expected market penetration into the electric utility network of the southwestern US through year 2000. The study was conducted primarily from a utility perspective and included the utility view of the technical, legal, economic, and institutional considerations necessary to make central-station generation of electricity from solar power commercially successful. No dispersed uses of solar electric generation were addressed. The report provides a basis from which periodic updates can be made to analyze the effect of economic trends and technology developments on utilization of solar and/or conventional electric generation as technology continues to progress in future years. The basis established in this report utilized current state-of-the-art technology for solar and conventional electric generating plants. Also, 1977 costs of conventional electric generating plants were utilized. The cost of first generation commercial solar electric generating plants was based on assumptions reflecting large-scale manufacturing of components by a mature industry.

Economical, stand-alone, solar microgrids can be quickly implemented in most un-electrified regions of the world. Solar resource maps developed by the National Aeronautics and Space Administration (NASA) and the National Renewable Energy Laboratory (NREL), together with HOMER Pro software, have been widely used to determine the optimal design of microgrids. Repercussions of imprecise predictions of solar resources and electrical power generation lead to an increased likelihood of energy shortage in a stand-alone microgrid, or increases overall project costs. Actual solar energy productions of two sites in Kerala, India, were compared with solar electricity generation, predicted by the HOMER Pro tool. For both test sites, the results showed that the simulated, solar production was within 8.1% of the actual annual production. However, monthly variations in solar production led to unanticipated energy shortages in the simulated microgrids. These findings reaffirm the inexorable need of the following actions for cost-effective design of microgrids: a) systematic analysis of reliability of electrical supply, and b) deployment of schemes for demand response and flexible load. Statistical analysis of temporal variation of solar irradiance in the United States were used to inform recommendations for stand-alone microgrid design.

Water is a vital human need that must be met for human survival and carrying out daily activities. However, the condition of natural resources in each region is different, not all regions have sufficient water availability. One of them is in Serut, Gunungkidul which is an area with hilly geography. During the dry season, clean water sources in Serut District become scarce and very limited. Therefore, the Serut District Government cooperates with the Community Self-Help Group (KKM Tirta Abadi Jaya) to drill deep wells to distribute water to residents. However, along the way, the operational costs are very large, especially for water pump electric pulses. In addition, the geographical condition of Serut which is hilly and has many trees causes frequent power outages, especially in extreme weather. This affects the resistance of the water pump because it is often on and off. The power outage can also stop the distribution of water to residents. This community service provides a solution to these problems, namely the installation of solar electricity generation. With this solar electricity generation, it can reduce the operational costs of electric pulses. In addition, using solar electricity generation can increase the durability of the tool because there is no on-off. Residents also still have their water needs met even though there is a power outage because the electricity needs for water pumps are supplied from solar electricity generation.

Wind and solar generation are intermittent generation sources. System integration costs include the costs of spinning reserves, increased transmission costs and storage costs. The overarching research problem examines evaluation of different policies that lead to high penetration of intermittent renewable electricity sources. The first research question examined the emissions reduction benefits and system integration costs of policy mandates for high penetration of intermittent renewable electricity technologies for Midcontinent Independent System Operator (MISO). The second research question examines the total systems costs of mandates for renewable electricity generation and a carbon tax using a TIMES model for MISO. The third research question examined the emissions and costs of policy mandates for high penetration of wind and solar electricity generation technologies for MISO when short-term operational constraints are considered. TIMES minimizes the total system cost subject to constraints of capacity activity, commodity use, satisfying demand, and peaking reserve. The US EPA 9 region model contains end use technologies for commercial, industrial, residential and transport sectors. The technologies that do not serve end use demands with electricity have been removed. The number of time slices which are the time divisions of the year was increased to 288 to help capture wind and solar generation dynamics at higher levels of penetration and help better understand spinning reserves requirements and costs. Based on the candidate sites for solar and wind generation, the costs include expected transmission costs, and any investment and production costs specific to the candidate sites costs. The results show that as the level of the mandate for wind and solar generation increases, their costs increased. Emissions saving from the mandates were converted to reductions in the Social Costs of Emissions (SCE) (See Section 2.4.4 for the definition) to compare system cost to with the savings in SCE. The savings in the SCE increase as the level of the mandate increases. However, the savings in SCE do not justify the system cost increases associated with the mandates. The carbon tax and mandate policies implemented held the overall emission reductions constant where a 35% reduction of CO2e emissions from 2020 levels by 2050 in compared to the reference scenario. The carbon tax (Policy I) had the lower of Levelized Marginal Cost of Electricity (LMCOE) (discounted value of generation for a year based on the generation weighted Marginal Cost of Electricity), while the mandate (Policy II) had the higher of LMCOE. Similarly, Policy I had the lowest of discounted total system cost and Policy II had higher discounted total system cost. The cost to society is underestimated when short-term operational constraints are ignored. The addition of short-term operational constraints led to increased total systems cost and greater emissions savings as the level of the mandate increased. Adding short-term operating constraints also gives a more complete understating of CO2e emissions savings for the different scenarios as there is a decrease in coal generation and increase in natural gas generation led to increased CO2e emissions savings. The addition of short-term operational constraints shows on one hand the impact of the policy and on the other hand the consequences of not including some of the cost realities.

While human population has been multiplied by four in the last hundred years, the world energy consumption was multiplied by ten. The common method of using fossil fuels to provide energy and electricity has dangerously disturbed nature's and climate's balance. It has become urgent and crucial to find sustainable and eco-friendly alternatives to preserve a livable environment with unpolluted air and water. Renewable energy is the unique eco-friendly opportunity known today. The main challenge of using renewable energy is to ensure the constant balance of electricity demand and generation on the electrical grid. This paper investigates whether the solar electricity generation is correlated with the urban electricity consumption in hot climates. The solar generation and total consumption have been compared for three cities in Florida. The hourly solar generation has been found to be highly correlated with the consumption that occurs 6 h later, while the monthly solar generation is correlated with the monthly energy consumption. Producing 30% of the electricity using solar energy has been found to compensate partly for the monthly variation in the urban electricity demand. In addition, if 30% of the world electricity is produced using solar, global CO2 emissions would be reduced by 11.7% (14.6% for India). Thus, generating 30% solar electricity represents a valuable asset for urban areas situated in hot climates, reducing the need for electrical operating reserve, providing local supply with minimal transmission losses, but above all reducing the need for fossil fuel electricity and reducing global CO2 emission.

Synergy of photothermal effect in integrated 0D natural melanin /2D reduced graphene oxide for effective solar steam generation and water purification