SOLAR POWER

This study presents a modern version of the economic load dispatch (MELD) problem with the contribution of renewable energies and conventional energy, including wind, solar and thermal power plants. In the study, reduction of electricity generation cost is the first priority, while the use of multiple fuels in the thermal power plant is considered in addition to the consideration of all constraints of power plants. Two meta-heuristic algorithms, one conventional and one recently published, including Particle swarm optimization (PSO) and Equilibrium optimizer (EO), are applied to determine the optimal solutions for MELD. A power system with ten thermal power plants using multiple fossil fuels, one wind power plant, and three solar power plants is utilized to evaluate the performance of both PSO and EO. Unlike other previous studies, this paper considers the MELD problem with the change of load demands over one day with 24 periods as a real power system. In addition, the power generated by both wind and solar power plants varies at each period. The results obtained by applying the two algorithms indicate that EO is completely superior to PSO, and the solutions found by EO can satisfy all constraints. Particularly in Case 1 with different load demand values, EO achieves better total electricity production cost (TEGC) than PSO by 0.75%, 0.87%, 0.13%, and 0.45% for the loads of 2400 MW, 2500 MW, 2600 MW and 2700 MW. Moreover, EO also provides a faster response capability over PSO through the four subcases although EO and PSO are run by the same selection of control parameters. In Case 2, the high efficiency provided by EO is still maintained, though the scale of the considered problem has been substantially enlarged. Specifically, EO can save $51.2 compared to PSO for the minimum TEGC. The savings cost is equal to 0.33% for the whole schedule of 24 hours. With these results, EO is acknowledged as a favourable search method for dealing with the MELD problem. Besides, this study also points out the difference in performance between a modern meta-heuristic algorithm (EO) and the classical one (PSO). The modern metaheuristic algorithm with special structure is highly valuable for complicated problem as MELD.

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.

An Information System for Multiple Criteria Assessment of Renewable Energy Power Plants

Comprehending the complementarities between a country’s renewable power plants can help strategically plan for the future of its power system. Furthermore, complementarity can reduce the system’s vulnerabilities and its dependency on a single energy source. The Colombian power system significantly depends on hydroelectricity, which fulfills approximately 65% of the country’s electricity demand, making it vulnerable to droughts, particularly those caused by the El Nino–Southern Oscillation. Using either gas or coal power plants during a drought to back up the power system is expensive and polluting. This paper assesses the complementarities of Colombia’s hydro, wind, and solar power plants, with the purpose of identifying the weaknesses and strengths of the system and suggest ways for redressal. Complementarity is assessed via Pearson’s correlation analysis and Beluco et al.’s (2019) complementarity dimensionless indexes. The results suggest that the Colombian power system lacks complementarity, but it can be enhanced by investing in solar and wind power plants.

One way to mitigate the variability of wind and solar power generation is to install the corresponding plants in nearby locations. For example, in Kuwait, the facility at Shagaya Renewable Energy Park is located in a desert area with both photovoltaic panels and wind turbines that allow the continuous generation of renewable energy throughout the day. The National Center for Atmospheric Research (NCAR) has developed a system to generate probabilistic wind and solar predictions for the Shagaya facility. These predictions are based on the analog ensemble technique that post-processes the wind speed and solar irradiance predictions based on a combination of multiple models including the Weather Research and Forecasting (WRF) numerical model. The ensemble forecasts have 20 members and are generated independently at each wind and solar power production facility. Here we present a method based on the Schaake Shuffle (SS) technique to pair the ensemble members from the independent systems to obtain a unique ensemble prediction of the aggregated wind and solar generation. After reordering through the SS technique, the corresponding paired solar and wind power members can be summed to build a unique ensemble of combined generation that is statistically consistent, as verified by the presented metrics.

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.

Large-scale deployment of wind and solar power is essential for climate change mitigation but requires substantial land and offshore area due to their relatively low installed capacity densities. However, wide variation in installed capacity density estimates creates major uncertainty in projecting future spatial requirements. Here, we apply multiple spatial methods to satellite imagery to estimate installed capacity densities for 60 onshore wind, 24 offshore wind, and 54 utility-scale solar PV plants (including 36 fixed-tilt and 18 single-axis tracking) across six countries: Australia, China, Germany, India, United Kingdom, and the United States. For wind power plants, mean estimates of installed capacity density using the 8 D buffer method (eight times the rotor diameter), the convex hull method with a 5 D buffer, and the median Voronoi polygon (bounded by an 8 D buffer) method are similar (4.0, 4.4, and 4.9 MW km −2 for onshore wind and 4.6, 5.2, and 5.4 MW km −2 for offshore wind, respectively) but the 5 D buffer method provides a relatively larger estimate because of unaccounted areas within power plant boundaries. Single-axis tracking solar PV power plants have an approximately 33% lower mean installed capacity density than fixed-tilt power plants, with estimates of 36 and 29 MW km −2 (without and with a 50 m buffer, respectively), compared to 54 and 43 MW km −2 for fixed-tilt systems. Estimates vary more within countries and across methods than between countries, underscoring the importance of methods and assumptions in estimating spatial requirements for large-scale wind and solar deployment.

The peculiarity of the operation of solar photovoltaic power plants is the dependence of the generation power on weather conditions, which leads to the maximum production of electrical energy at noon hours of the day. Due to a decrease in electricity consumption, insufficient unloading capacity of pumped storage power plants in the integrated energy system of Ukraine and the specifics of electricity production at solar photovoltaic power plants, dispatching restrictions on the level of generation power are already taking place. To transfer volumes of electrical energy in the world, electrical energy storage systems are used, which operate based on lithium-ion storage batteries. Such systems have a number of advantages over other battery energy systems, which allows their implementation in almost any power generation facility. With the help of energy storage systems, it is possible to make a profit through the purchase of electric energy during a period of low prices and its release during a period of high prices, allowing consumers to save money on its payment. In this paper, we simulate the use of a battery energy storage system for storing electrical energy to transfer excess electrical energy from a solar photovoltaic power plant. To conduct a study and identify excess capacity of a solar photovoltaic power plant, the daily schedule of electrical load is equalized to the capacity of a separate power plant Because of the study, the optimal time for charging and discharging the battery was determined, from which it can be seen that the need to transfer excess electricity to a solar photovoltaic power plant occurs at lunchtime, and their discharge at the peak is the graph of the electrical load of the power system. The aggregate operation of a solar power plant with a total installed capacity of photovoltaic power at the level of 10 MW (DC) and a battery energy storage system for accumulating electric energy with a capacity of 3.75 MWh was simulated. For the study day, the required capacity of a battery system for accumulating electric energy at the level of 1.58 MW was determined. Using the methodology of the levelized cost of electricity and storage, a technical and economic assessment of the transfer of excess capacity of a solar photovoltaic power plant using a battery system for storing electrical energy was carried out. When calculating the cost of storage, the cost of the transferred electrical energy from the solar power plant was taken into account. From the results of technical and economic calculations, it can be seen that, in terms of the cost of equipment, as of 2020, the cost of supplying excess electrical energy from the battery energy storage system is growing when compared with the supply from a solar photovoltaic power plant. Taking into account some forecast assumptions, the cost of electricity supply from the battery energy storage system was calculated for the mode of transferring excess capacity of a solar photovoltaic power plant for 2025 and 2030 years. Keywords: modeling, power system, load demand curve, solar photovoltaic power plant, electric energy storage system, cost

The paper made an extensive analysis of power generation in the territory of the Republic of Crimea. A special issue is the use of renewable energy sources (RES), in particular wind (WPP) and solar power plants (SPP). The current power generation capacities of the republic are determined and the power deficit is shown, which is currently covered by the electricity of the energy system of the Krasnodar Territory. One of the most important features of the development of the modern world is the increased attention of the world community to the problems of rationality and efficiency in the use of energy resources, the introduction of energy saving technologies and the search for renewable energy sources. In 2021, the countries of the Middle East and Europe increased the capacity of wind and solar power plants several times over. In India, installed renewable energy capacity has exceeded 100 GW. In June-July 2021, solar power generation in the European Union reached a record level and amounted to 39 TWh, an increase of 28%. For most European countries, renewable energy accounts for more than 15% of electricity generation in general. This is due to the fact that in the main markets, renewable, in particular solar electricity, is significantly cheaper than the energy of gas and diesel power plants.For Russia, the southern districts and coastal zones are effective geographical areas for the use of WPPs and SPPs. Thus, the Republic of Crimea is a geographically favorable place for the development of alternative energy.The technical and economic characteristics of SPP and WPP are determined, the current own wind and solar generation is considered in detail. The ways of development and permissible capacities of renewable energy sources in the Crimea are determined. According to the requirements for the systems of regulation, control and protection of renewable energy sources (RES) during their operation as part of the Unified Energy System of Russia, the use of alternative sources of electricity together with the main grid is permissible with their total capacity equal to 20% of the maximum load of the region. Therefore, the development and distribution of renewable energy in the Crimea has a huge potential. The applied systems for integrating RES into the overall energy system are considered, their shortcomings are identified.For the further development of RES in the Crimea, it is proposed to use a model of intelligent integration of SPPs and WPPs into the energy system of the peninsula using solid-state transformers. The use of power transformers for the transmission of electricity from RES to the network significantly reduces the flexibility of the system and eliminates the control of energy parameters. To increase the control capabilities of the output power to the network, as well as to reduce energy costs during voltage transformation, it is proposed to use solid-state transformers (SST) at the inverting stage. The use of SST changes the general principle of power transfer from RES to the network to the following: the DC voltage accumulated in the batteries of WPP and SPP is fed through a high-frequency inverter to a pulse transformer, where it rises to the voltage of the power line. Then it is rectified and inverted to the required mains frequency. Modern power semiconductor components may well provide such a model for integrating RES into the grid. The proposed model is distinguished by the presence of effective control of energy parameters, as well as high efficiency.

<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>

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.

This paper explores how the increasing demand for renewable energy sources has resulted in the development of innovative technologies to harness solar and wind power. The paper evaluates the potential of solar wind hybrid power generation as a solution to address energy reliability, cost, and environmental sustainability challenges. The paper presents a system that generates electricity using wind and solar power, wherein an external high-speed fan rotates the rotor of a dynamo, producing magnetic flux that creates a DC voltage, and a solar panel absorbs photons of light, creating another DC voltage by freeing electrons. Both outputs are connected to a charge controller, a battery for energy storage, and a load, a motor with a propeller and an LED light. Two diodes ensure that the currents from the wind turbine and solar panel do not oppose each other. The paper also discusses various aspects such as pre-feasibility analysis, optimal sizing, modeling, control, and reliability issues related to the system.

The modern world is facing a double energy crisis: on the one hand, fossil fuel reserves are being depleted, and on the other hand, the level of environmental pollution is growing. In countries where coal and oil remain the main sources of energy, this problem is particularly acute, as dependence on traditional energy resources limits economic development and threatens environmental safety. The dual energy crisis is a serious challenge for the modern world, but it can be overcome through the active development of renewable energy. Investing in clean technologies, reducing fossil fuel consumption, and international cooperation in environmental security will help create a sustainable and environmentally friendly energy system of the future. For example, the metallurgical plants of the Brazilian mining company Vale S.A. located in Indonesia are fully powered by hydropower; the mining multinational Rio Tinto Group plans to invest an additional $600 million in the construction of two 100 MW solar power plants in Pilbara by 2026. The situation in Ukraine's mining and metals sector is currently extremely difficult. The sector, which until February 24, 2022 accounted for the majority of the country's exports, is on the brink of survival. With the loss of Azovstal and Ilyich Iron and Steel Works of Mariupol, the economy simultaneously lost more than 40% of its metal production. Other metallurgical enterprises reduced production due to the fighting, operating at only 20%. Inhulets, Pivdennyi and Pivdennyi mining and processing plants (MPPs) in Kryvyi Rih periodically suspend their operations, and production of MPPs of the Ferrexpo group, which includes Poltava, Bilanivske and Yeristovo MPPs, decreased by 47% [1]. Blocked seaports, logistical restrictions, and a 70% increase in Ukrzaliznytsia's freight tariffs have worsened the already difficult situation of the industry. Along with the extractive industry, the energy sector of Ukraine has been under terrorist attacks for three years. World Bank experts estimate the country's losses at over UAH 4.4 trillion. 80% of thermal generation, almost 100% of hydropower, most wind power plants, and half of photovoltaic power plants have been destroyed [2]. The minimum safety margin is provided by the nuclear power industry, which also suffered a significant loss - the seizure of the largest nuclear power plant in Ukraine and Europe, the Enerhodar NPP, by nuclear terrorists. In such critical conditions for the country, the issue of further development of nuclear generation, dispersal (decentralization) of electricity generation and protection against destruction of energy infrastructure facilities is more acute than ever. The main sources of raw materials for the Ukrainian iron ore industry are three basins: Bilozersky Iron Ore District (Zaporizhzhia region); Kerch Iron Ore Basin; and Kryvyi Rih Iron Ore Basin. The latter includes not only the iron ore mines of Kryvyi Rih. Kryvyi Rih, as well as a number of GOKs located in Horishni Plavni (producing ore within the Kryvyi Rih-Kremenchuk structural and metallogenic zone). Taking into account the fact that the contribution of the enterprises of the Bilozersky and Kerch basins to the total ore production is less than 10%, and that the enterprises of Kryvyi Rih and Horishni Plavni operate, in fact, within the same basin, the general analysis in this paper is based on the production data of the Ferrexpo Poltava Mining Group. This will allow us to formalize the conclusions for all iron ore enterprises with an open-pit mining method with sufficient reliability. This paper presents a comparative analysis of the potential of renewable energy sources in the context of PJSC Poltava Mining and Processing Plant based on the calculations performed. It is established that the most promising direction for the development of distributed generation is wind and solar power plants. Taking into account the peculiarities of operation, it is recommended to focus on the construction of photovoltaic stations in the considered conditions

This paper studies the economics of carbon-neutral synthetic fuel production from renewable electricity in remote areas where high-quality renewable resources are abundant. To this end, a graph-based optimisation modelling framework directly applicable to the strategic planning of remote renewable energy supply chains is proposed. More precisely, a hypergraph abstraction of planning problems is introduced, wherein nodes can be viewed as optimisation subproblems with their own parameters, variables, constraints and local objective. Nodes typically represent a subsystem such as a technology, a plant or a process. Hyperedges, on the other hand, express the connectivity between subsystems. The framework is leveraged to study the economics of carbon-neutral synthetic methane production from solar and wind energy in North Africa and its delivery to Northwestern European markets. The full supply chain is modelled in an integrated fashion, which makes it possible to accurately capture the interaction between various technologies on an hourly time scale. Results suggest that the cost of synthetic methane production and delivery would be slightly under 150 €/MWh (higher heating value) by 2030 for a system supplying 10 TWh annually and relying on a combination of solar photovoltaic and wind power plants, assuming a uniform weighted average cost of capital of 7%. A comprehensive sensitivity analysis is also carried out in order to assess the impact of various techno-economic parameters and assumptions on synthetic methane cost, including the availability of wind power plants, the investment costs of electrolysis, methanation and direct air capture plants, their operational flexibility, the energy consumption of direct air capture plants, and financing costs. The most expensive configuration (around 200 €/MWh) relies on solar photovoltaic power plants alone, while the cheapest configuration (around 88 €/MWh) makes use of a combination of solar PV and wind power plants and is obtained when financing costs are set to zero.

Correlations between the electricity generated by concentrating solar thermal power (CSP) plants, as well as cross-correlations between CSP, wind power and electricity demand, have significant impacts on decisions for how much and where to build utility-scale CSP capacity, the optimal amount of thermal storage in the CSP plants, reserve capacity needed to back-up the system, as well as the expected levels of curtailed renewable power. Accurately estimating these correlations is vital to performing detailed analyses of high renewable penetration scenarios. This study quantifies the degree of correlation between geographically dispersed CSP, as well as the correlation between CSP and wind power, and CSP and electricity demand in 356 discrete regions in the contiguous US. Correlations are calculated using hourly data on an annual basis. Maps of the correlations will be presented to illustrate the degree of correlation between solar power and the demand it is serving, as well as the synergies between the negatively-correlated wind power and solar power serving the same region.