Hasil untuk "Electricity"

Loan Thi Do, Trung Ngoc Nguyen, Quynh T. Tran et al.

Batteries energy storage systems (BESS) are becoming a common trend worldwide supporting an increase in the power system's renewable energy (RE). Storing energy is not applied and has been in the research process in Vietnam. This study aims to evaluate the economic performance of a solar power plant (SPP) in Vietnam both before and after integrating a BESS through key metrics including the levelized cost of electricity (LCOE), net present value (NPV), and electrical productivity. Furthermore, this study incorporates sensitivity analysis to the metrics under variations in transmission line limitation (TLL), capital expenditure (CAPEX), and subsidies in this project. The results show that the solar photovoltaic (PV) system in the chosen SPP has an LCOE of 6.13 cents/kWh and an NPV of 7.52 million USD. The NPV will decrease to zero in the TLL from 22.2 MW. If this SPP is installed with a BESS of 2 MW 2 MWh, the LCOE increases to 6.38 cents/kWh, the NPV decreases to 5.5 million USD, and the levelized storage cost (LCOS) of 126.61 cents/kWh. The PV-BESS system is no longer economically efficient when the BESS reaches 12 MWh or larger. When TLL falls below 24 MW, BESS holds a significant role in improving the system's output. CAPEX's reductions of BESS have a negligible impact on LCOE, but a significant on LCOS. Investment-based incentives (IBI) starting at 7 %, or Capital-based incentives (CBI) starting at 4 cents/W can enhance the financial attractiveness of the PV-BESS system.

devan Rozali, Rachma Prilian Eviningsih, Novie Ayub Windarko

Abstract- Short Term Load Forecasting (STLF) is becoming very important as the use of distributed energy sources, renewable energy, and demand side management increases. Electrical energy is one of the most widely used energy, especially in households. To avoid excessive electricity consumption, we propose a household electricity consumption forecasting system using Convolutional Neural Network (CNN) method. The input of CNN is the power of several household loads measured for one week at 10-minute intervals. This data is used to train the model and predict household electricity consumption for the next week. Forecasting results for a week show a difference in consumption of 3.623 kWh, while with the load management method the difference is 3.439 kWh. With an electricity tariff of Rp1.352/kWh, the estimated electricity cost for the following week is Rp4.892.00, and with load management, the cost drops to about Rp4.649.52 (5% savings).The testing method is done by comparing the forecasting results and actual data for one week. The results show an average difference of only 1.57W with an average error of 0.07%. The CNN method is also compared with the Long Short-Term Memory (LSTM) method. As a result, CNN has better performance with CNN RMSE value of 3.688, CNN management of 3.354, while LSTM RMSE of 12.603, and LSTM management of 13.132. CNN is proven to be more accurate for household short-term electricity load forecasting..

Oleksii Havrylenko, Iuliia Myroshnychenko

In the context of escalating geopolitical instability and the global decarbonisation agenda, understanding the strategic role of renewable energy in enhancing national energy security has become increasingly urgent, especially for transition economies with legacy fossil fuel dependencies. This study aims to investigate whether the expansion of renewable energy contributes to measurable improvements in energy security in these countries. The analysis draws on annual panel data from 19 countries spanning the years 2000 to 2023. Renewable energy development is measured using three indicators: the share of renewables in total installed electricity capacity, electricity generation, and gross final energy consumption. Energy security is captured via the World Energy Council’s Trilemma Index, a multidimensional composite score. All renewable indicators were transformed using Box-Cox procedures to correct right-skewed distributions. The panel fixed-effects Granger causality models were estimated to have two-year lags to detect temporal causality while controlling for unobserved heterogeneity. The empirical results consistently show a significant unidirectional causal relationship running from renewable energy to energy security. Specifically, lagged increases in renewable electricity capacity are associated with subsequent improvements in energy security scores (β = 0.36, p < 0.01), as are increases in renewable electricity generation share (β = 0.28, p < 0.01) and in renewable energy's share of final energy consumption (β = 0.38, p < 0.05). Reverse causality tests, examining whether improvements in energy security predict renewable expansion, yield either insignificant or marginal results. These findings underscore that renewable energy acts as a proactive driver of systemic energy resilience, rather than merely a passive beneficiary of a secure energy environment.

Martha Fernanda Mohedano-Castillo, Carlos Díaz-Delgado, Boris Miguel López-Rebollar et al.

Micro-hydropower technologies are increasingly attracting attention due to their potential to contribute to sustainable energy generation. With the growing global demand for electricity, it is essential to research and innovate in the development of devices capable of harnessing hydroelectric potential through such technologies. In this context, the Archimedes screw generator (ASG) stands out as a device that potentially offers significant advantages for micro-hydropower generation. This study aimed, through a simplified yet effective method, to analyze and determine the simultaneous effects of the number of blades, inclination angle, and flow rate on the torque, mechanical power, and efficiency of an ASG. Computational Fluid Dynamics (CFD) was employed to obtain the torque and perform the hydrodynamic analysis of the devices, in order to compare the results of the optimal geometric and operational characteristics with previous studies. This proposal also helps guide future work in the preliminary design and evaluation of ASGs, considering the geometric and flow conditions that take full advantage of the available water resources. Under the specific conditions analyzed, the most efficient generator featured three blades, a 20° inclination, and an inlet flow rate of 24.5 L/s, achieving a mechanical power output of 117 W with an efficiency of 71%.

Alberto Boretti

Mohammad Hadi Dehghani, Nabi Shariatifar, Parisa Sadighara et al.

Objectives: The COVID-19 pandemic has had a profound impact on human social and economic structures, making it one of the most significant crises of the 21st century. The production and consumption of energy sources, such as electricity, oil, and gas, have also been affected by the pandemic. Therefore, this study aims to systematically review the effects of the COVID-19 pandemic on energy consumption.Methods: This study presents a systematic review of the impact of the coronavirus on energy consumption. Initially, relevant keywords were selected, and a systematic search of studies was conducted in databases including Web of Science, PubMed, Scopus, and ScienceDirect from 2020 to May 2022. Following screening, articles meeting the eligibility criteria were included in the study.Results: Through this systematic review, a total of 19,551 studies were identified, of which 18 met the eligibility criteria. All included studies investigated electricity consumption, while two studies focused on gas and fuel consumption, and only one study examined heating oil consumption. The most significant decreases in electricity, natural gas, and transportation fuel consumption were 55.4%, 32.4%, and 75.9%, respectively. On the other hand, heating oil consumption saw an increase of 12%.Conclusion: The results of this systematic review indicate that the COVID-19 pandemic has resulted in reduced energy consumption across various sectors, including electricity, transportation fuel, and natural gas, leading to a decrease in greenhouse gas emissions. However, some studies reported an increase in electricity consumption in the residential sector due to prolonged periods of staying at home and remote work during quarantine.

Seyid Abdellahi Ebnou Abdem, Rida Azmi, El Bachir Diop et al.

Access to waste management services is crucial for urban sustainability, impacting public health, environmental well-being, and overall quality of life. This study employs logistic regression analysis on survey data collected from 1,032 household heads residing in Nouakchott, the capital of Mauritania. The survey investigated key household factors that determine access to waste management services. The findings reveal a significant interplay among waste service provision, the presence of cisterns, housing type and size, and access to electricity. Socioeconomic disparity in service access, with poorer housing formats like shacks receiving substandard services. In contrast, areas with robust electrification report better service access, although inconsistencies remain amid power outages. The research highlights the challenges faced by Riyadh municipality, particularly rapid growth and inadequate infrastructure, which hinder waste management efficiency. Overall, the results not only illuminate Nouakchott’s unique challenges in service provision but also propose actionable recommendations for a sustainable urban future. These recommendations aim to inform and guide targeted policies for improving living conditions and environmental sustainability in urban Mauritania.

Lifang FENG, Lei HUANG, Hanfei ZHOU et al.

This paper proposes a radar signal transceiver framework that combines single-bit sampling and time division multiplexing receivers to satisfy the application requirements of low-cost lightweight radars. Firstly, this paper explains the advantages of saving the number of receivers by introducing the working principle of the framework. From the perspective of radar resource allocation, the importance of single-bit sampling in this framework was analyzed; additionally, the proposed framework can achieve better performance than a classical linear frequency modulation continuous wave radar using time and space exchange. Subsequently, the formulas for range, velocity and angle measurement were derived, along with the Cramér-Rao bound for estimating target parameters. Accordingly, the performance advantages of the proposed framework were verified, and the signal-to-noise ratio conditions for its stable operation were determined. Finally, this paper verifies the accuracy of the target acquisition principle of the proposed framework and the reliability of the performance analysis by using a velocity dimensional pairing algorithm based on single-bit two-dimensional multiple signal classification.

Haoran PAN, Hui MA, Dunfa HU et al.

Vortex Electromagnetic Waves (VEMWs) have unique wavefront phase modulation characteristics. As a new degree of freedom in the diversity of radar transmitters, the VEMW Radar (VEMWR) provides Radar Cross-Section (RCS) diversity and improves signal and information processing dimensions and performances. The detection and imaging performances of VEMWR have been verified in various radar systems. This article focuses on the applying background of forward-looking radar imaging and proposes a time-division multiplemode scanning imaging method based on a Uniform Circular Array (UCA) system with multiple transmitters and a single receiver at the UCA center. First, we establish the forward-looking VEMWR imaging mode and corresponding signal mode. Next, an improved three-Dimensional (3D) back-projection and range-Doppler algorithm is proposed, which utilizes the magnitude difference at various elevation angles of multimode VEMW, phase difference at different azimuth angles, and Doppler effect resulting from the relative motion of the radar and target to achieve 3D imaging of the target. As the elevation angle increases, the beam pattern gain of the high-mode VEMW decreases sharply due to the energy divergence of the VEMW. The proposed method can maintain stability at low or high elevation angles using the energy distribution of multiple modes in the spatial domain. Imaging results of point targets revealed that the normalized gain of target-imaging results is equivalent either at low or high elevation angles within the multimode VEMW field of view. The proposed method is validated through experiments with an aircraft target. Based on the imaging results, it is verified that the proposed method can accurately reconstruct the 3D structure of complex targets.

Nicolas Schneider

Sanggono Adisasmito, Anggit Raksajati, Alfin Ali et al.

The high consumption of coal in the power generation sector results in high greenhouse gas (GHG) emissions in Indonesia. Indonesian Government still needs to reduce its GHG emissions to below 662 MtCO2e in order to meet the Intergovernmental Panel on Climate Change (IPCC) scenario. This condition encourages the government to develop a strategy for decarbonization as stated in the Long-term Strategy on Low Carbon and Climate Resilience 2050 document. The retrofitting potential of Indonesian coal power plant was evaluated. Several factors such as Levelized Cost of Electricity (LCoE), CO2 emission intensity prior to capture, energy penalty, and the presence of installed flue gas desulfurizer (FGD) were used as determining parameters in selecting priority power plants to be retrofitted. The mass and energy balance of the CCS process was modelled using Aspen HYSYS V12. Based on simulation and techno-economic calculations results, it can be concluded that the LCoE value of CCS-retrofitted coal-fired power plants are influenced by the plant's capacity and the existence of FGD units. The implementation of CCS technology through retrofitting in Indonesia shall be prioritized for 1,000 MW ultra-supercritical power plants that already have existing seawater FGD technology. The increase in costs, together with a decrease in power production, results in an increase in LCoE values of up to USD 0.11/kWh for 1,000 MW power plants. This result is expected to be used as a consideration for the Indonesian government in mapping out a decarbonization strategy in the energy generation sector.

Francesca Ceglia, Elisa Marrasso, Chiara Martone et al.

In Europe, the recast of Directive 2018/2001 defined Renewable Energy Communities as innovative configurations for renewable energy sharing between different end user types. In this regard, this work aims to assess the benefits following the constitution of a Renewable Energy Community in the industrial area of Benevento (South of Italy), involving a mixed-use building and an industrial wastewater treatment plant. The alternative single end users’ configuration has been also examined, and both solutions have been compared with the current state where the users’ electric energy requests are fully met by the power grid. The users have been equipped with a 466 kW_p photovoltaic plant, modelled in HOMER Pro^®, providing in input experimental meteorological data (global solar radiation and air temperature) collected by one of the weather control units in Benevento. Real data about users’ electric energy demand have been gathered from their electricity bills, and when unavailable their electric load profiles on an hourly basis have been reconstructed based on the aggregated monthly data. Energy sharing has been proven to increase energy self-consumption and the users’ self-sufficiency. Annually, the primary energy demand is reduced by 577 MWh (1.2 MWh/kW_p), carbon dioxide emissions by 84 tCO₂ and operative costs by 101 kEUR.

Oleh Pylypchuk, Oleh Strelko, Yuliia Berdnychenko

We are delighted to welcome you to the new issue of the journal on the history of science and technology! This issue is unique as it explores diverse aspects of the development of science and technology in various countries and historical periods. We invite you on an exciting journey through the pages of this issue, where you will find works by distinguished scientists such as Maryna Gutnyk, Florian Nürnberger, Tetiana Karmadonova, Natalya Pasichnyk, Renat Rizhniak, Нanna Deforzh, Liudmyla Zhuravlova, and many others. Their research covers various facets of history and technology. The collaborative work by Maryna Gutnyk and Florian Nürnberger presents a comprehensive exploration of the evolution of the Fe-C diagram, tracing its historical development through the lenses of various scientific contributions over time. Their analysis underscores the rich history behind this diagram, highlighting the foundational studies dating back to the early 19th century, marking crucial milestones in understanding the carbon content in steel and its implications for industrial applications. The authors' meticulous use of comparative analysis, synthesis, and chronological examination sheds light on the gradual refinement and evolution of the Fe-C diagram. From the initial recognition of graphite as pure carbon to the establishment of phase diagrams through collaborative efforts at international congresses, the Fe-C diagram's progression intertwines with the advancements of the industrial revolution. Tetiana Karmadonova's work on the migration trends of Ukrainian researchers from 1991 to 2023 provides a comprehensive analysis of the multifaceted factors driving the migration of scientists from Ukraine to various destination countries, particularly against the backdrop of recent events in the country. The study delves into the intricate landscape of migration among Ukrainian researchers across different historical periods. Natalya Pasichnyk, Renat Rizhniak, and Нanna Deforzh's meticulous study on the publications in the "Bulletin of Experimental Physics and Elementary Mathematics" from 1886 to 1917 offers invaluable insights into the organization, proceedings, and outcomes of domestic and international congresses of mathematicians and natural scientists during that period. Their research, focused on a comprehensive and quantitative analysis of these journal publications, sheds light on the pivotal role of these gatherings in the scientific and pedagogical realms Liudmyla Zhuravlova's research on the evolution of techno-nationalism and the pivotal role of space in this phenomenon from the 1980s to the 2020s offers a compelling exploration into the intricate dynamics of technological advancements and their influence on international relations and national strategies. The article delves deeply into the theoretical comprehension of techno-nationalism, particularly examining its relationship with space policy and its relevance within the context of US-China relations. Employing an interdisciplinary approach, drawing from historical, economic, political sciences, and international relations theory, the research unravels the dichotomous evolution of techno-nationalism juxtaposed against techno-globalism. Zhuravlova's work accentuates the ongoing power struggle between the US and China within the space industry, amplifying the techno-nationalist dimensions within innovation systems. Artemii Bernatskyi and Mykola Sokolovskyi's research presents a comprehensive review of the evolution of additive manufacturing (AM) processes within the realm of metallurgy, spanning from the foundational theories of layer-by-layer manufacturing to the contemporary landscape of AM technologies. This work illuminates the rapid advancements within the AM sector, capturing the profound interest of the scientific community. It underscores the dual significance of AM technologies - not only as an alternative manufacturing method for existing structures but also as a gateway to crafting new, intricately complex structures unattainable through traditional methodologies. Through meticulous analysis and classification of prior studies focusing on technological advancements and implementations, the research establishes a structured approach towards comprehensively mapping the development of additive manufacturing technologies in various trajectories. As a result, the research proposes a systematic approach to formulate a comprehensive scheme for AM technology development, thereby offering a framework that navigates the intricate landscape of technological advancements in various directions. Mykhailo Klymenko's meticulous study offers a comprehensive evaluation of Professor Tomasz Nikodem Ścibor-Rylski's pioneering contributions to the development of agricultural machinery testing during the latter half of the 19th century. This research sheds new light on Rylski's scientific endeavors and their significant impact on the evolution of agricultural equipment testing. Employing principles of historicism, scientific rigor, and objectivity, Klymenko utilizes historical-scientific methodologies, archival analysis, and generalization to present a nuanced understanding of Rylski's work. For the first time, archival documents are introduced, unveiling insights into the scientist's activities in advancing the field of agricultural machinery testing. Mohamad Khairul Anuar Mohd Rosli, Ahmad Kamal Ariffin Mohd Rus, and Suffian Mansor's insightful study delves into the overlooked yet pivotal role of electricity, specifically facilitated by the Perak River Hydro-Electric Power Company (PRHEPC), in the tin-mining industry within Kinta Valley during the period of 1927 to 1940. The research illuminates the historical emergence of electricity as a dominant power source in the tin-mining industry of Colonial Malaya, a topic that has received minimal attention in Malaysian historiography. Sana Simou, Khadija Baba, and Abderrahman Nounah's research represents a profound call to action amidst the urgent need to safeguard Morocco's cultural heritage, notably exemplified by the Marinid Madrasa within the Chellah archaeological site in Rabat. This research intricately weaves advanced technologies with a profound appreciation for the historical, social, and cultural significance of these sites. It charts a course that not only conserves architectural brilliance but also honors the profound stories encapsulated across epochs. Ultimately, it emerges as a blueprint for harmonizing the past with the present, ensuring the preservation of cultural heritage while embracing the imperatives of progress. In his article, Oleh Strelko shows that the history of bridge construction is an important part of historical knowledge. Developments in bridge construction technology reflect not only engineering advances, but also social, economic and cultural aspects of society. Engineers and scientists faced unique challenges when designing and building bridges depending on the technological level of the era, available materials and the needs of society. This process may reflect technological progress, changes in transportation needs, and cultural and social changes. The purpose of this article is to briefly review key moments and stages in the history of metal bridge construction using welding technology in the 20th century. We invite you on this exciting journey with our authors exploring the history of science, technology, and cultural heritage. May this issue broaden your knowledge and inspire new research endeavors!

Haibo ZHAO, Zhijun XIANG, Linsong XIAO

The power system is in a transition towards a more intelligent, flexible and interactive system with higher penetration of renewable energy generation, load forecasting, especially short-term load forecasting for individual electric customers plays an increasingly essential role in future grid planning and operation.A big data framework for short-term power load forcasting using heterogenous was proposed, which collected the data from smart meters and weather forecast, pre-processed and loaded it into a NoSQL database that was capable to store and further processing large volumes of heterogeneous data.Then, a long short-term memory (LSTM) recurrent neural network was designed and implemented to determine the load profiles and forecast the electricity consumption for the residential community for the next 24 hours.The proposed framework was tested with a publicly available smart meter dataset of a residential community, of which LSTM’s performance was compared with two benchmark algorithms in terms of root mean square error and mean absolute percentage error, and its validity has been verified.

Ritu Kandari, Neeraj Neeraj, Alexander Micallef

Energy security and the resilience of electricity networks have recently gained critical momentum as subjects of research. The challenges of meeting the increasing electrical energy demands and the decarbonisation efforts necessary to mitigate the effects of climate change have highlighted the importance of microgrids for the effective integration of renewable energy sources. Microgrids have been the focus of research for several years; however, there are still many unresolved challenges that need to be addressed. Energy storage systems are essential elements that provide reliability and stability in microgrids with high penetrations of renewable energy sources. This study provides a systematic review of the recent developments in the control and management of energy storage systems for microgrid applications. In the early sections, a summary of the microgrid topologies and architectures found in the recent literature is given. The main contributions and targeted applications by the energy storage systems in the microgrid applications is defined for each scenario. As various types of energy storage systems are currently being integrated for the reliable operation of the microgrids, the paper analyses the properties and limitations of the solutions proposed in the recent literature. The review that was carried out shows that a hybrid energy storage system performs better in terms of microgrid stability and reliability when compared to applications that use a simple battery energy storage system. Therefore, a case study for a DC microgrid with a hybrid energy storage system was modelled in MATLAB/Simulink. The presented results show the advantages of hybrid energy storage systems in DC microgrids.

Riza Salar, Umit Turgut

The learning characteristics of each student are different. Differentiated instruction considers individual differences, as such guides the learning journey rather than seeing these differences as a challenge. The purpose of this research was to compare the effects of differentiated instruction and 5E learning cycle in physics classes on the students' academic achievement and self-efficacy. We used the matching - pre-test/post-test - control group design to address the research questions. We conducted the study in three different schools, performed three experiments, and had three control groups. 162 10th grade students participated in the study. We used the ‘Electricity Prior Knowledge Test’, ‘Electricity Achievement Test’, and the ‘Physics Self-Efficacy Scale’ to collect data. SPSS version 20 software was used to analyse the obtained quantitative data. Independent samples t-test was used to determine whether there was a significant difference between the control and experimental group students’ level of prior knowledge regarding the subject of electricity. The analysis of covariance was used to determine whether there was a significant difference between the control and experimental group students’ course achievements after the implementation. Two-factor mixed-measures ANOVA was used to determine whether the experimental and control group students’ pre-test and post-test scores on self-efficacy differed. Based on the results, it can be concluded that differentiated instruction improved the academic achievement of the low- and mid-achieving students. When the self-efficacy scores of the students were analysed, no significant difference was found between the groups. Based on the results of the research, researchers or teachers who want to use differentiated teaching in their classrooms may be recommended to create level groups in the classroom.

Shuai Chen, Chengpeng Jiang, Jinglin Li et al.

Battery energy storage technology is an important part of the industrial parks to ensure the stable power supply, and its rough charging and discharging mode is difficult to meet the application requirements of energy saving, emission reduction, cost reduction, and efficiency increase. As a classic method of deep reinforcement learning, the deep Q-network is widely used to solve the problem of user-side battery energy storage charging and discharging. In some scenarios, its performance has reached the level of human expert. However, the updating of storage priority in experience memory often lags behind updating of Q-network parameters. In response to the need for lean management of battery charging and discharging, this paper proposes an improved deep Q-network to update the priority of sequence samples and the training performance of deep neural network, which reduces the cost of charging and discharging action and energy consumption in the park. The proposed method considers factors such as real-time electricity price, battery status, and time. The energy consumption state, charging and discharging behavior, reward function, and neural network structure are designed to meet the flexible scheduling of charging and discharging strategies, and can finally realize the optimization of battery energy storage benefits. The proposed method can solve the problem of priority update lag, and improve the utilization efficiency and learning performance of the experience pool samples. The paper selects electricity price data from the United States and some regions of China for simulation experiments. Experimental results show that compared with the traditional algorithm, the proposed approach can achieve better performance in both electricity price systems, thereby greatly reducing the cost of battery energy storage and providing a stronger guarantee for the safe and stable operation of battery energy storage systems in industrial parks.

İsmail Hakkı Çavdar, Vahit Feryad

One of the basic conditions for the successful implementation of energy demand-side management (EDM) in smart grids is the monitoring of different loads with an electrical load monitoring system. Energy and sustainability concerns present a multitude of issues that can be addressed using approaches of data mining and machine learning. However, resolving such problems due to the lack of publicly available datasets is cumbersome. In this study, we first designed an efficient energy disaggregation (ED) model and evaluated it on the basis of publicly available benchmark data from the Residential Energy Disaggregation Dataset (REDD), and then we aimed to advance ED research in smart grids using the Turkey Electrical Appliances Dataset (TEAD) containing household electricity usage data. In addition, the TEAD was evaluated using the proposed ED model tested with benchmark REDD data. The Internet of things (IoT) architecture with sensors and Node-Red software installations were established to collect data in the research. In the context of smart metering, a nonintrusive load monitoring (NILM) model was designed to classify household appliances according to TEAD data. A highly accurate supervised ED is introduced, which was designed to raise awareness to customers and generate feedback by demand without the need for smart sensors. It is also cost-effective, maintainable, and easy to install, it does not require much space, and it can be trained to monitor multiple devices. We propose an efficient BERT-NILM tuned by new adaptive gradient descent with exponential long-term memory (Adax), using a deep learning (DL) architecture based on bidirectional encoder representations from transformers (BERT). In this paper, an improved training function was designed specifically for tuning of NILM neural networks. We adapted the Adax optimization technique to the ED field and learned the sequence-to-sequence patterns. With the updated training function, BERT-NILM outperformed state-of-the-art adaptive moment estimation (Adam) optimization across various metrics on REDD datasets; lastly, we evaluated the TEAD dataset using BERT-NILM training.

Jasvinder Kumar, Ihsan Ullah Khalil, Azhar Ul Haq et al.

In Pakistan, hydroelectric power is one of the reliable sources of electricity with a capacity of 8,713 MW, which is 29% of the total energy mix. Hence, with such a vast resource capacity of hydroelectric power, its optimization and dispatch planning will have a great significance. This research work discusses the importance of hydroelectric power generation planning for both storage and run-of-river (ROR) hydropower plants, as well a; a solver-based optimization technique is proposed for the first time to resolve the intricate job of generation planning for hydroelectric power plants in MATLAB. A mathematical-optimization model is also developed, which uses a Mixed-Integer Linear-Programming (MILP) algorithm, based on the objective function of profit maximization, which considers a random varying revenue plan as model input. Three hydropower generators of different capacities and efficiencies are considered for the optimization problem. MILP based solution is proposed for both storage and ROR hydropower plants with two dispatch schedules, i.e., Normal dispatch schedule and optimum dispatch schedule. The objective functions are solved, and the profit (in dollars) from each dispatch schedule is calculated and compared. The preliminary optimization results show an increase of $22,000 and $29,130 in the profit for storage and ROR hydropower plants, respectively, which is 19% more than average income. Hence, ensuring the credibility of the proposed algorithm for maximizing the revenue ($), is aimed to facilitate and assist better planning for electric power producers.

Matt Golden, Adam Scheer, Carmen Best