Planning Of Power Plants Research Articles

Combined photovoltaic and wind power plant planning for the production and transportation of liquefied green hydrogen: A case study of Egypt

For the production of renewable hydrogen in Egypt and its transport to growing hydrogen demand centers such as Germany, relatively low costs are given in the literature. These low costs are attracting investors. However, due to the scope of the respective studies, not all techno-economic aspects of the supply-chain that influence levelized costs of hydrogen are considered. This work represents a systematical analysis of the levelized costs of hydrogen in Egypt by using wind and solar power for producing 40 kt per year of green hydrogen. A maximal investment volume of four billion euros, and an available area of 37 km2 in central Egypt is assumed. The techno-economic evaluation of the system, from the production site to the selling location, is conducted by a mixed-integer linear optimization model. Depending on the system configuration and production site, the study reveals energy losses of 52–61% of the harvested energy and a levelized costs of hydrogen of 7.17–12.31 €/kg.

Optimal planning of the barrage type tidal power plants equipped to the hydro-pumps

Optimal planning of the barrage type tidal power plants equipped to the hydro-pumps

Global Horizontal Irradiance in West Africa: Evaluation of the WRF-Solar Model in Convection-Permitting Mode with Ground Measurements

Abstract The number of solar power plants has increased in West Africa in recent years. Reliable reanalysis data and short-term forecasting of solar irradiance from numerical weather prediction models could provide an economic advantage for the planning and operation of solar power plants, especially in data-poor regions such as West Africa. This study presents a detailed assessment of different shortwave (SW) radiation schemes from the Weather Research and Forecasting (WRF) Model option Solar (WRF-Solar), with appropriate configurations for different atmospheric conditions in Ghana and the southern part of Burkina Faso. We applied two 1-way nested domains (D1 = 15 km and D2 = 3 km) to investigate four different SW schemes, namely, the Community Atmosphere Model, Dudhia, RRTMG, Goddard, and RRTMG without aerosol and with aerosol inputs (RRTMG_AERO). The simulation results were validated using hourly measurements from different automatic weather stations established in the study region in recent years. The results show that the RRTMG_AERO_D01 generally outperforms the other SW radiation schemes to simulate global horizontal irradiance under all-sky condition [RMSE = 235 W m−2 (19%); MAE = 172 W m−2 (14%)] and also under cloudy skies. Moreover, RRTMG_AERO_D01 shows the best performance on a seasonal scale. Both the RRTMG_AERO and Dudhia experiments indicate a good performance under clear skies. However, the sensitivity study of different SW radiation schemes in the WRF-Solar model suggests that RRTMG_AERO gives better results. Therefore, it is recommended that it be used for solar irradiance forecasts over Ghana and the southern part of Burkina Faso.

GIS-based multicriteria decision analysis for spatial planning of solar photovoltaic power plants in Çanakkale province, Turkey

A comprehensive site selection analysis is essential for implementing cost-effective and efficient SPPs. We selected optimal sites for SPPs by considering different criteria and restrictions using the GIS-based MCDA. Highly suitable and most suitable sites were determined by GIS and AHP for establishing PV SPP in the Çanakkale province. Furthermore, the potential of these sites to meet the energy demand was examined. Next, the potential power capacity and payback period of each of the five alternative SPP project sites selected from the most suitable sites were analysed. The results demonstrated that 96% of the study area was restricted, 3‰ was most suitable and 3% was highly suitable for PV SPPs. The most suitable sites exhibited the potential to meet all electrical energy needs of the province of Çanakkale during installation. The potential power capacities of the five selected alternatives were between approximately 0.9 and 1.6 MWp, whereas potential payback periods were between 12.1 and 14.7 years.

Theoretical study of a downdraft gasifier, microturbine, and organic Rankine cycle fuelled by date molasses waste for distributed generation applications in Iraq

AbstractThe main objective of this work is the modeling of a combined heat and power (CHP) system that transforms waste from manufacturing date molasses in the Babylon province in central Iraq into electrical and thermal energy. A compact CHP facility that uses date waste (pomace and pits) as a feedstock is the subject of theoretical modeling and simulation research. The planned power plant consists of a downdraft gasifier, a microturbine (MT), and an organic Rankine cycle engine (ORC) as a bottom unit to maximize electric generation by recovering the MT exhaust gas heat and achieving a maximum electric output. The net electric production is 361.68 kW (280 kW from the MT unit and 81.68 kW from the ORC unit). To determine the best‐working fluid for the ORC system as a whole, three working fluids, including isopentane, benzene, and R113, were investigated. The temperature difference between the heat source and the fluid's boiling point is a key factor in choosing a more appropriate ORC working fluid. Isopentane is the most suitable working fluid for CHP applications, according to the findings of the simulation. The electric efficiency achieved is 30%, and the overall efficiency of the CHP plant was 56.87% from the gasification of date pits. The simulation results showed a satisfactory producer gas lower calorific value of 3.92 MJ k‐g‐1 for date pomace and 4.65 MJ k‐g‐1 for date pits, and cold gasification efficiencies of 73% and 76% for pomace and pits, respectively. © 2023 Society of Industrial Chemistry and John Wiley & Sons Ltd.

An integrated decision support method for strategic planning and tactical management of regional biomass power plants under uncertainties

In the context of global climate change and energy security pressures, biomass power generation has been regarded as an effective way to supplement the energy supply, reduce fossil fuel consumption, and alleviate environmental pollutants. This paper presents an integrated modeling framework to address the regional biomass power generation planning to facilitate biomass utilization in developing countries. By combining the geographic information system, multi-criteria decision-making approach, as well as the type-2 fuzzy inexact programming method, the proposed integrated framework could assist the optimal decision-making for the specific site and size selection of biomass power plants in the whole region as well as the optimal operation strategies under deep uncertainties. The proposed modeling framework is applied to the particular context of Henan, an agricultural province in China with great biomass potential. The results suggested that biomass power plants with a total capacity of 2.4 GW could be established, which could satisfy 6% of local electricity consumption and avoid 18.51 Mt of carbon emissions. The sensitivity analysis of the main economic parameters indicated that lower investment and operation costs were essential to stimulate the utilization of local biomass energy. A higher feed-in tariff is not encouraged due to the limited incentive effect but is a heavy burden for the government. Furthermore, the optimal strategies for biomass power generation planning under uncertainties could be suggested for different decision makers with various risk preferences to achieve a tradeoff between system cost and risk.

Climate Change, Demand Uncertainty, and Firms’ Investments: Evidence from Planned Power Plants

Climate Change, Demand Uncertainty, and Firms’ Investments: Evidence from Planned Power Plants

Deep-learning optimization and environmental assessment of nanomaterial's boosted hydrogen and power generation system combined with SOFC

Renewable energy sources are unquestionably required, given that the world's energy consumption is anticipated to increase significantly in the next decades. Besides, fuel cell converts chemical energy directly into electricity, making it the most efficient and environmentally friendly power source. This study examines the viability of producing electricity using a supercritical CO2 cycle, a methane digester, and a gas turbine. Applying the principles of exergetic balance, mass, energy conservation, and component-specific exergetic balance to each part of the power unit allow for a simulation of the power unit's efficiency. Furthermore, a parametric analysis is performed to establish how the relevant factors will affect the unit's functioning. The optimization algorithms are generated with the help of deep learning methods. It was found that the ratio of the compressor's pressure to the current density was the most crucial element in determining output power. Nanoparticles are used in fuel cells to enhance heat transfer and, thus, the generated power. Through optimization, one might find a product that is both the most cost-effective and energy-efficient option available. Artificial neural networks may be used as a mediator to help expedite the process of reaching optimum performance across several criteria. The optimal settings for the planned power plant are now being determined by studying the correlation between design factors and objective functions. Therefore, the second law's efficiency at its sweet spot was calculated at 61.8%. In the end, the system at its optimum point was found to have an exergo-environmental index of 0.436.

How Dependent Are European Power Systems and Economies on Natural Gas?—A Macroeconomic Optimization for Security of Electricity Supply

How dependent are European power systems and economies on natural gas? To answer this pressing question, we coupled a simulation model for assessing security of electricity supply and an economic optimization model. With this, we were able to analyze different reduction scenarios of the amount of gas utilized in the power sector. Our results show that reducing the amount of natural gas in the European power sector by up to 30% has a relatively moderate impact on the security of electricity supply. Restrictions of 40% or more result in substantially higher reductions in electricity demand shortfall and are associated with economic costs of more than EUR 77 billion. Furthermore, we demonstrate that a close coordination of gas distribution on a European level would be instrumental in mitigating negative economic consequences. Finally, it can be deduced that a coordinated delay of planned power plant shutdowns could effectively compensate for reduced gas volumes in the electricity sector.

A Dynamic Social Network Matching Model for Virtual Power Plants and Distributed Energy Resources with Probabilistic Linguistic Information

Virtual power plants (VPPs) offer an effective means to address the imbalance issue between electricity supply and demand to advance the world’s low-carbon development. To fully utilize the limited resources in the virtual power plant planning stage, a two-sided match between VPPs and distributed energy companies is needed to better implement resource aggregation management. Because of the vagueness in this matching environment, the probabilistic linguistic term set (PLTS) is necessary to apply to express the decision makers’ preference. Considering the complex social relationships and intense competition among companies, a dynamic social network two-sided matching model is proposed for solving the multi-attribute two-sided matching decision-making problem. Firstly, we present a matching satisfaction degree described by PLTS. A dynamic social trust degree based on the sliding time concept is proposed. Secondly, the social trust network relationships are built based on the direct and indirect dynamic trust degree among companies. This relationship is then combined with an improved trust rank algorithm to identify the most authoritative and the most trusted company to provide the target company with a recommendation for the next moment. Besides, given that companies compete for limited resources, we further define the competitive satisfaction degree and apply the two-sided matching model. Additionally, then a two-sided matching model is developed. Finally, our model is tested numerically to ensure its accuracy and reliability.

Planning of Photovoltaic (PV) Type Solar Power Plant as An Alternative Energy of the Future in Indonesia

Indonesia receives a lot of sunlight throughout the year. This then causes large profits to be obtained with the Development of a Solar Power Plant Planning (PLTS) that uses a photovoltaic (PV) system. This research then aims to look at how the use of PV systems as a solar power generation system becomes alternative energy in the future. The approach used in this research is a qualitative approach through a literature study of various previous studies and studies that discuss the planning of PV-type solar PV in Indonesia. This study found that the PLN network can be integrated and benefit PLTS that are on-grid and use solar modules. Then various public facilities with a flat surface and wide enough can be a good enough place to be a PV type PV mini-grid. Then the ratio of power that PLN has issued for PJU is 76.66%, while the ratio of solar panels is 23.3%. Unfortunately, the main ingredients for making PV mini-grid are still imported, which makes it challenging to develop PV-type PV mini-grid.

Analysis of Reservoir Water Discharge at Solar Power Plant Tanjung Raja Village as a Basis for Pico Hydro Power Plant Planning in Paddy-Field Area

Energy is an important aspect of life as a whole. The fossil-based energy sources have dwindled over time, in line with population growth and economic growth. One approach to meeting the energy demand is to use renewable energy. According to Presidential Regulation Number 22 of 2017 on National Energy General Plan (RUEN) targets, the share of New and Renewable Energy in total national energy in 2025 will be 25%. The construction of a Hydro Power Plant is expected to reach 3.000 MW by 2025. According to NREEC statistical data from 2016, the potential for constructing a Hydro Power Plant in South Sumatera is approximately 448 MW. This paper investigates the possibility and potential of building Hydro Power Plant from the reservoir of the irrigation water system generated by a Solar Power Plant in Tanjung Raja Village, Muara Enim District, South Sumatra. The water discharge magnitude is measured, and the potential of electricity generated from the discharge is presented. The experimental data shows that water discharge from the reservoir can generate maximum electricity of 806,6488 watts with a discharge rate of 0.0653 m3/s, and power is 36,7245 watts, with a discharge of 0,0030 m3/s. The average electricity potential is approximately 375.6782 watts, with a discharge average of 0.0304 m3/s. Therefore, the experimental data shows the possibility of hybridizing the Solar Power Plant with Hydro Power Plant, which will be beneficial for the residential area in Tanjung Raja Village, Muara Enim.

Analysis of Reservoir Water Discharge at Solar Power Plant Tanjung Raja Village as a Basis for Pico Hydro Power Plant Planning in Paddy-Field Area

Energy is an important aspect of life as a whole. The fossil-based energy sources have dwindled over time, in line with population growth and economic growth. One approach to meeting the energy demand is to use renewable energy. According to Presidential Regulation Number 22 of 2017 on National Energy General Plan (RUEN) targets, the share of New and Renewable Energy in total national energy in 2025 will be 25%. The construction of a Hydro Power Plant is expected to reach 3.000 MW by 2025. According to NREEC statistical data from 2016, the potential for constructing a Hydro Power Plant in South Sumatera is approximately 448 MW. This paper investigates the possibility and potential of building Hydro Power Plant from the reservoir of the irrigation water system generated by a Solar Power Plant in Tanjung Raja Village, Muara Enim District, South Sumatra. The water discharge magnitude is measured, and the potential of electricity generated from the discharge is presented. The experimental data shows that water discharge from the reservoir can generate maximum electricity of 806,6488 watts with a discharge rate of 0.0653 m3/s, and power is 36,7245 watts, with a discharge of 0,0030 m3/s. The average electricity potential is approximately 375.6782 watts, with a discharge average of 0.0304 m3/s. Therefore, the experimental data shows the possibility of hybridizing the Solar Power Plant with Hydro Power Plant, which will be beneficial for the residential area in Tanjung Raja Village, Muara Enim.

The quantity-quality transition in the value of expanding wind and solar power generation

SummaryWind and solar photovoltaic generators are projected to play important roles in achieving a net-zero-carbon electricity system that meets current and future energy needs. Here, we show potential advantages of long-term site planning of wind and solar power plants in deeply decarbonized electricity systems using a macro-scale energy model. With weak carbon emission constraints and substantial amounts of flexible electricity sources on the grid (e.g., dispatchable power), relatively high value is placed on sites with high capacity factors because the added wind or solar capacity can efficiently substitute for running natural gas power plants. With strict carbon emission constraints, relatively high value is placed on sites with high correlation with residual demand because resource complementarity can efficiently compensate for lower system flexibility. Our results suggest that decisions regarding long-term wind and solar farm siting may benefit from consideration of the spatial and temporal evolution of mismatches in electricity demand and generation capacity.

Short term electricity load forecasting using hybrid prophet-LSTM model optimized by BPNN

Electrical load forecasting plays a vital role in the operation and planning of power plants for the utility companies and policy makers to design stable and reliable energy infrastructure. Load forecasting is categorized in long-term, mid-term and short-term. Among them, short term load forecasting that monitors weekly, daily, hourly and even sub-hourly operations is gaining a lot of attention which saves time and cost while satisfying consumers’ needs without interruption. Different models such as conventional, Artificial Intelligence (AI) and hybrid models have been developed to investigate short-term load forecasting. However, these models suffers various issues such as low speed convergence (conventional), high complexity (AI) and so on. Consequently, this work proposes a hybrid method using Prophet and Long Short Term Memory (LSTM) models to overcome the above limitations in an effort to predict accurate load. The Prophet model utilize linear as well as non-linear data to predict original load data but still some of the residuals are left which are regarded as non-linear data. Here, these residuals (non-linear data) are trained by employing LSTM, and finally both the forecasted data from Prophet and LSTM are trained by Back Propagation Neural Network (BPNN) to further enhance prediction accuracy. Elia Grid real time quarter hour based electrical load data from 2014 to 2021 has been utilized to verify working performance of proposed hybrid technique by computing Mean Absolute Percentage Error (MAPE), Root Mean Square Error (RMSE), and Mean Average Error (MAE). Results substantiate that proposed hybrid models outperforms the standalone models of Autoregressive Integrated Moving average (ARIMA), LSTM and Prophet model on the basis of reduced errors with least computation time.

A multi‐period optimization model for power sector withCO2emission considerations

A multi-period mixed-integer linear programming model was developed to minimize the cumulative cost associated with electricity generation and reduce the carbon dioxide emissions. Fuel balancing and carbon capture and sequestration were investigated for carbon dioxide mitigation. The developed model was implemented on the power sector of Pakistan for a period of 11 years starting from 2020. All the existing and new planned power plants were included, and capacity expansion was only considered where required. Low, normal, and high gross domestic product growth rate-based electricity demands were considered, keeping summer and winter demands separately. Fuel balancing can mitigate 38.5% average carbon dioxide emissions for both summer and winter, whereas carbon capture and sequestration can mitigate 26.2% and 2.2% average carbon dioxide emissions for summer and winter, respectively. Carbon capture and sequestration can mitigate 23.4% average carbon dioxide emissions (for normal gross domestic product summer) compared to 16.5% mitigation in fuel balancing (for normal gross domestic product summer and winter). Carbon dioxide emissions mitigation was negligible in carbon dioxide case (ie, 1.1%) for normal gross domestic product winter. For high gross domestic product case, fuel balancing could not mitigate carbon dioxide emissions, and carbon capture and sequestration was the only option for carbon dioxide mitigation, which resulted in 23.4% and 6.3% mitigation for summer and winter, respectively.

Phasing out the blast furnace to meet global climate targets

Phasing out the blast furnace to meet global climate targets

Optimization of supercritical carbon dioxide based combined cycles for solid oxide fuel cell-gas turbine system: Energy, exergy, environmental and economic analyses

Among various supercritical carbon dioxide cycles, the supercritical recompression carbon dioxide cycle can well adapt to the high temperature of the exhaust gas of the solid oxide fuel cell-gas turbine system to augment power generation. Nevertheless, even after the recovery by the supercritical recompression carbon dioxide cycle, the exhaust gas still contains a large amount of unutilized waste energy. Few studies introduce low-temperature cycles to build cascade cycle systems, which are very likely to address this issue effectively. From the perspectives of energy, exergy, environmental and economic indexes, this article analyzes and compares the improvement potential of integrating four common low-temperature cycles, including organic Rankine cycle, transcritical carbon dioxide cycle, Kalina cycle, and organic flash cycle. Different key operating parameters are considered in-depth and optimized by a genetic algorithm. The results illustrate that in terms of efficiency, the introduction of the organic Rankine cycle is the most outstanding since it can reach the highest energy efficiency of 72.74–73.55% (exergy efficiency of 70.22–71.01%) across wide operation conditions. In terms of cost, the coupling of Kalina cycle is suggested due to the lowest capital cost of 19.94 $/h. The environmental penalty of the four systems all accounts for 14.73% of the total cost. As a consequence, the pros and cons of four common low-temperature cycles are fully demonstrated, which can provide references for the power plant planning.

A Modified Artificial Bee Colony Algorithm Using Accept–Reject Method: Theory and Application in Virtual Power Plant Planning

In this paper, the modified artificial bee colony optimization algorithm, in which the accept–reject method is used as a part of the solution process in order to limit the random search space, is proposed for solving the optimization problems. The accept–reject method can find points of desirable quality and high accuracy with the help of probabilistic functions and limited the random search space, which increases the speed of the algorithm for the next steps and also reduces the number of iterations of the artificial bee algorithm. Then, the next phases of the bee algorithm will be executed in the interval determined by the accept–reject method. One of the most important issues in intelligent algorithms, especially the bee algorithm, is that in problems with several local optimal points, sometimes the algorithm gets stuck in the trap of local optimal points and does not reach the global optimal points. By using the motion step determination parameter in the employed bee phase, the algorithm can be guided to reach the global optimal points while correctly adjusting the movement of the bees for searching in random food sources, which will help improving the problem-solving process. This algorithm is tested by the basic benchmark functions as well as the benchmark functions of CEC 2005 and CEC2017 and CEC2019 and an engineering problem related to the planning of a virtual power plant. The results show that the proposed algorithm is superior in comparison to other algorithms.

Wind speed modeling based on measurement data to predict future wind speed with modified Rayleigh model

The development of modeling wind speed plays a very important in helping to obtain the actual wind speed data for the benefit of the power plant planning in the future. The wind speed in this paper is obtained from a PCE-FWS 20 type measuring instrument with a duration of 30 minutes which is accumulated into monthly data for one year (2019). Despite the many wind speed modeling that has been done by researchers. Modeling wind speeds proposed in this study were obtained from the modified Rayleigh distribution. In this study, the Rayleigh scale factor (<em>C<sub>r</sub></em>) and modified Rayleigh scale factor (<em>C<sub>m</sub></em>) were calculated. The observed wind speed is compared with the predicted wind characteristics. The data fit test used correlation coefficient (R<sup>2</sup>), root means square error (RMSE), and mean absolute percentage error (MAPE). The results of the proposed modified Rayleigh model provide very good results for users.