Journal articles on the topic 'Scenarios generation strategy'
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1
Gao, Feng, Jianli Duan, Yingdong He, and Zilong Wang. "A Test Scenario Automatic Generation Strategy for Intelligent Driving Systems." Mathematical Problems in Engineering 2019 (January 15, 2019): 1–10. http://dx.doi.org/10.1155/2019/3737486.
Abstract:
In this paper, a methodology of automatic generation of test scenarios for intelligent driving systems is proposed, which is based on the combination of the test matrix (TM) and combinatorial testing (CT) methods together. With a hierarchical model of influence factors, an evaluation index for scenario complexity is designed. Then an improved CT algorithm is proposed to make a balance between test efficiency, condition coverage, and scenario complexity. This method can ensure the required combinational coverage and at the same time increase the overall complexity of generated scenarios, which is not considered by CT. Furthermore, the way to find the best compromise between efficiency and complexity and the bound of scenario number has been analyzed theoretically. To validate the effectiveness, it has been applied in the hardware-in-the-loop (HIL) test of a lane departure warning system (LDW). The results show that the proposed method can ensure required coverage with a significantly improved scenario complexity, and the generated test scenario can find system defects more efficiently.
2
Tian, Zhe, Chuang Ye, Jie Zhu, Jide Niu, and Yakai Lu. "Accelerating Optimal Control Strategy Generation for HVAC Systems Using a Scenario Reduction Method: A Case Study." Energies 16, no. 7 (March 24, 2023): 2988. http://dx.doi.org/10.3390/en16072988.
Abstract:
Learning an optimal control strategy from the optimized operating dataset is a feasible way to improve the operational efficiency of HVAC systems. The operation dataset is the key to ensuring the global optimality and universality of the operation strategy. Currently, the model-based method is commonly used to generate datasets that cover all operating scenarios throughout the cooling season. However, thousands of iterative optimizations of the model also lead to high computational costs. Therefore, this paper proposed a scenario reduction method in which similar operating scenarios were grouped into clusters to significantly reduce the number of optimization calculations. First, k-means clustering (with dry-bulb temperature, wet-bulb temperature, and cooling load as features) was used to select typical scenarios from operating scenarios for the entire cooling season. Second, the model-based optimization was performed with the typical scenarios to generate the optimal operating dataset. Taking a railway station in Beijing as a case study, the results show that the optimization time for the typical scenarios was only 1.4 days, which was reduced by 93.1% compared with the 20.6 days required to optimize the complete cooling season scenario. The optimal control rules were extracted, respectively, from the above datasets generated under the two schemes, and the results show that the deviation of energy saving rate was only 0.45%. This study shows that the scenario reduction method can significantly speed up the generation of the optimal control strategy dataset while ensuring the energy-saving effect.
3
Cai, Jinkang, Weiwen Deng, Ying Wang, Haoran Guang, Jiangkun Li, and Juan Ding. "Boundary Scenario Generation for HAVs Based on Classification and Local Sampling." Machines 11, no. 4 (March 27, 2023): 426. http://dx.doi.org/10.3390/machines11040426.
Abstract:
High-level Automated Vehicles (HAVs) are expected to improve traffic safety significantly. However, verifying and evaluating HAVs remains an open problem. Scenario-based testing is a promising method for HAV testing. Boundary scenarios exist around the performance boundary between critical and non-critical scenarios. Testing HAVs in these boundary scenarios is crucial to investigate why collisions cannot be avoided due to small changes in scenario parameters. This study proposes a methodology to generate diverse boundary scenarios to test HAVs. First, an approach is proposed to obtain at least one High-Performance Classifier (HPC) based on two classification algorithms that iteratively guide each other to find uncertain scenarios to improve their performance. Then, the HPC is exploited to find candidate scenarios highly likely to be boundary scenarios. To increase the efficiency of candidate scenario generation, a strategy based on local sampling is presented to find more diverse candidate scenarios based on a small number of them. Numerical experiments show that the HPCs acquired by the method proposed in this study can achieve a classification accuracy of 98% and 99% for random car-following and cut-in scenarios, respectively. Moreover, more than 86% of 271,744 candidate cut-in scenarios derived by local sampling are near the performance boundary.
4
Sapitang, Michelle, Wanie M. Ridwan, Khairul Faizal Kushiar, Ali Najah Ahmed, and Ahmed El-Shafie. "Machine Learning Application in Reservoir Water Level Forecasting for Sustainable Hydropower Generation Strategy." Sustainability 12, no. 15 (July 30, 2020): 6121. http://dx.doi.org/10.3390/su12156121.
Abstract:
The aim of this study is to accurately forecast the changes in water level of a reservoir located in Malaysia with two different scenarios; Scenario 1 (SC1) includes rainfall and water level as input and Scenario 2 (SC2) includes rainfall, water level, and sent out. Different time horizons (one day ahead to seven days) will be investigated to check the accuracy of the proposed models. In this study, four supervised machine learning algorithms for both scenarios were proposed such as Boosted Decision Tree Regression (BDTR), Decision Forest Regression (DFR), Bayesian Linear Regression (BLR) and Neural Network Regression (NNR). Eighty percent of the total data were used for training the datasets while 20% for the dataset used for testing. The models’ performance is evaluated using five statistical indexes; the Correlation Coefficient (R2), Mean Absolute Error (MAE), Root Mean Square Error (RMSE), Relative Absolute Error (RAE), and Relative Squared Error (RSE). The findings showed that among the four proposed models, the BLR model outperformed other models with R2 0.998952 (1-day ahead) for SC1 and BDTR for SC2 with R2 0.99992 (1-day ahead). With regards to the uncertainty analysis, 95PPU and d-factors were adopted to measure the uncertainties of the best models (BLR and BDTR). The results showed the value of 95PPU for both models in both scenarios (SC1 and SC2) fall into the range between 80% to 100%. As for the d-factor, all values in SC1 and SC2 fall below one.
5
Freitas, Patrícia F. S., Leonardo H. Macedo, and Rubén Romero. "A strategy for transmission network expansion planning considering multiple generation scenarios." Electric Power Systems Research 172 (July 2019): 22–31. http://dx.doi.org/10.1016/j.epsr.2019.02.018.
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Azad, Abdus Salam, Edward Kim, Qiancheng Wu, Kimin Lee, Ion Stoica, Pieter Abbeel, Alberto Sangiovanni-Vincentelli, and Sanjit A. Seshia. "Programmatic Modeling and Generation of Real-Time Strategic Soccer Environments for Reinforcement Learning." Proceedings of the AAAI Conference on Artificial Intelligence 36, no. 6 (June 28, 2022): 6028–36. http://dx.doi.org/10.1609/aaai.v36i6.20549.
Abstract:
The capability of a reinforcement learning (RL) agent heavily depends on the diversity of the learning scenarios generated by the environment. Generation of diverse realistic scenarios is challenging for real-time strategy (RTS) environments. The RTS environments are characterized by intelligent entities/non-RL agents cooperating and competing with the RL agents with large state and action spaces over a long period of time, resulting in an infinite space of feasible, but not necessarily realistic, scenarios involving complex interaction among different RL and non-RL agents. Yet, most of the existing simulators rely on randomly generating the environments based on predefined settings/layouts and offer limited flexibility and control over the environment dynamics for researchers to generate diverse, realistic scenarios as per their demand. To address this issue, for the first time, we formally introduce the benefits of adopting an existing formal scenario specification language, SCENIC, to assist researchers to model and generate diverse scenarios in an RTS environment in a flexible, systematic, and programmatic manner. To showcase the benefits, we interfaced SCENIC to an existing RTS environment Google Research Football (GRF) simulator and introduced a benchmark consisting of 32 realistic scenarios, encoded in SCENIC, to train RL agents and testing their generalization capabilities. We also show how researchers/RL practitioners can incorporate their domain knowledge to expedite the training process by intuitively modeling stochastic programmatic policies with SCENIC.
7
Düspohl, Meike, and Petra Döll. "Causal networks and scenarios: participatory strategy development for promoting renewable electricity generation." Journal of Cleaner Production 121 (May 2016): 218–30. http://dx.doi.org/10.1016/j.jclepro.2015.09.117.
8
Chen, Wengang, Ying Zhang, Jiajia Chen, and Bingyin Xu. "Pricing Mechanism and Trading Strategy Optimization for Microgrid Cluster Based on CVaR Theory." Electronics 12, no. 20 (October 18, 2023): 4327. http://dx.doi.org/10.3390/electronics12204327.
Abstract:
With the increasing penetration rate of renewable energy generation, the uncertainty of renewable energy output in microgrid cluster (MGC) leads to significant fluctuations in transaction volume, which may lead to the risk of transaction default. This paper proposes a day-ahead two layer trading model for microgrid cluster based on price trading mechanism and Conditional value-at-risk (CVaR) theory. Firstly, the upper-layer establishes an objective to minimize the overall power fluctuation of the microgrid cluster using Demand response (DR) with a penalty mechanism. The microgrid cluster adopts an internal pricing mechanism and adjusts transaction prices based on internal supply-demand conditions to guide microgrids’ participation in intracluster trading, thereby encouraging the microgrid to use the flexible resources to reduce power fluctuation. Secondly, the lower-layer optimization establishes an optimization model with the objective of minimizing the comprehensive operating cost of the microgrid cluster. The model employs backward scenario reduction techniques to obtain multiple sets of typical scenarios for renewable energy generation, and the CVaR theory is introduced to quantify the potential risk of transaction default. Finally, the effectiveness of the proposed models is verified through case studies considering various application scenarios.
9
Pašičko, Robert, Zoran Stanić, and Nenad Debrecin. "Modelling Sustainable Development Scenarios of Croatian Power System." Journal of Electrical Engineering 61, no. 3 (May 1, 2010): 157–63. http://dx.doi.org/10.2478/v10187-010-0022-7.
Abstract:
Modelling Sustainable Development Scenarios of Croatian Power SystemThe main objective of power system sustainable development is to provide the security of electricity supply required to underpin economic growth and increase the quality of living while minimizing adverse environmental impacts. New challenges such as deregulation, liberalization of energy markets, increased competition on energy markets, growing demands on security of supply, price insecurities and demand to cut CO2 emissions, are calling for better understanding of electrical systems modelling. Existing models are not sufficient anymore and planners will need to think differently in order to face these challenges. Such a model, on the basis on performed simulations, should enable planner to distinguish between different options and to analyze sustainability of these options. PLEXOS is an electricity market simulation model, used for modeling electrical system in Croatia since 2005. Within this paper, generation expansion scenarios until 2020 developed for Croatian Energy Strategy and modeled in PLEXOS. Development of sustainable Croatian energy scenario was analyzed in the paper - impacts of CO2 emission price and wind generation. Energy Strategy sets goal for 1200 MW from wind power plants in 2020. In order to fully understand its impacts, intermittent nature of electricity generation from wind power plant was modeled. We conclude that electrical system modelling using everyday growing models has proved to be inevitable for sustainable electrical system planning in complex environment in which power plants operate today.
10
Langholtz, Matthew, Ingrid Busch, Abishek Kasturi, Michael R. Hilliard, Joanna McFarlane, Costas Tsouris, Srijib Mukherjee, et al. "The Economic Accessibility of CO2 Sequestration through Bioenergy with Carbon Capture and Storage (BECCS) in the US." Land 9, no. 9 (August 27, 2020): 299. http://dx.doi.org/10.3390/land9090299.
Abstract:
Bioenergy with carbon capture and storage (BECCS) is one strategy to remove CO2 from the atmosphere. To assess the potential scale and cost of CO2 sequestration from BECCS in the US, this analysis models carbon sequestration net of supply chain emissions and costs of biomass production, delivery, power generation, and CO2 capture and sequestration in saline formations. The analysis includes two biomass supply scenarios (near-term and long-term), two biomass logistics scenarios (conventional and pelletized), and two generation technologies (pulverized combustion and integrated gasification combined cycle). Results show marginal cost per tonne CO2 (accounting for costs of electricity and CO2 emissions of reference power generation scenarios) as a function of CO2 sequestered (simulating capture of up to 90% of total CO2 sequestration potential) and associated spatial distribution of resources and generation locations for the array of scenario options. Under a near-term scenario using up to 206 million tonnes per year of biomass, up to 181 million tonnes CO2 can be sequestered annually at scenario-average costs ranging from $62 to $137 per tonne CO2; under a long-term scenario using up to 740 million tonnes per year of biomass, up to 737 million tonnes CO2 can be sequestered annually at scenario-average costs ranging from $42 to $92 per tonne CO2. These estimates of CO2 sequestration potential may be reduced if future competing demand reduces resource availability or may be increased if displaced emissions from conventional power sources are included. Results suggest there are large-scale opportunities to implement BECCS at moderate cost in the US, particularly in the Midwest, Plains States, and Texas.
11
Li, He Peng, Chuan Zhi Zang, Peng Zeng, Hai Bin Yu, and Zhong Wen Li. "Scenario-Based Stochastic Programming Strategy for Microgrid Energy Scheduling Considering Uncertainties." Applied Mechanics and Materials 672-674 (October 2014): 1322–28. http://dx.doi.org/10.4028/www.scientific.net/amm.672-674.1322.
Abstract:
The inherent random and intermittence of the renewable energy resources pose a huge challenge to the Microgrid (MG) energy management systems (EMS). In order to mitigate the effects of uncertainties, we propose a novel two-stage stochastic programming model for the energy scheduling optimization by considering the uncertainties in solar and wind generation, and the plug-in electric vehicles (EV). The random nature of uncertainty is characterized by a scenarios generation approach based on autoregressive moving average (ARMA) model according to probability density function of each random variable. By use of the strategy of scenarios simulation, the stochastic problem is decomposed into the deterministic equivalent problem. The firefly algorithm (FA) is used to solve the equivalent model. The effectiveness and robust of proposed stochastic energy scheduling optimization strategy for MG is valid by comparison with the simulation results of deterministic method.
12
Aten, Coree, Andrew Michalowski, Maurice Williams, Cody Stamm, and Paul Evangelista. "Soldier Power Operational Benefit Analysis." Industrial and Systems Engineering Review 3, no. 2 (July 16, 2015): 82–90. http://dx.doi.org/10.37266/iser.2015v3i2.pp82-90.
Abstract:
An operational benefit analysis of military small unit power (SUP) equipment is presented in detail. SUP equipment is designed to improve power generation, conservation, and overall power management strategies for dismounted military units. The operational benefit analysis examines four tactical scenarios and considers a naïve power management strategy and a SUP enabled power management strategy. The major findings and conclusions discussed in this paper include: specific conservation and generation strategies for select dismounted tactical scenarios; the importance of proper solar blanket employment; identification of a capability gap between 100W and 1000W in the power generation spectrum; the benefits of using conformal batteries; and the impact of inefficient PRC 154 battery swaps in the naïve case.
13
Shang, Xiaoyu, Siqi Song, and Jingwei Yang. "Comparative Environmental Evaluation of Straw Resources by LCA in China." Advances in Materials Science and Engineering 2020 (February 24, 2020): 1–16. http://dx.doi.org/10.1155/2020/4781805.
Abstract:
Crop straw, as a widely used biomass resource, can be processed to produce renewable energy and green products. However, about 70% of straw were burned in the open air, causing serious environmental pollution and air pollution. In order to reduce the burden on the environment, the different straw management strategies are comparatively studied and evaluated by applying the life-cycle assessment (LCA) method. Within the system boundary from cradle to grave, three alternative scenarios, including straw particleboard, straw cement-bonded particleboard, and straw direct combustion power generation, are compared with the current common treatment (straw open burning). The comprehensive inventory analysis on each treatment scenario is carried out in-depth, and the corresponding resource consumption and environmental impact of each treatment scenario are calculated, respectively. The LCA results showed that the environmental impacts of reusing crop straw to produce straw particleboard and cement-bonded particleboard (Scenario 1 and Scenario 2) is significantly reduced by 6% and 10%, by comparison with the use of wood resource. Greenhouse gas emissions from straw direct combustion power generation (Scenario 3) processes are reduced by 30% compared with coal power generation. From the environmental point of view, the scenario of straw particleboard product has the smallest impact on the environment, while straw open burning is not an advisable way due to the highest environmental impact from the highest greenhouse gas emissions and acidification effects. From the perspective of energy consumption, the energy consumption for the combustion power generation is the smallest in all scenarios. It is suggested that governments are supposed to construct reasonable scenarios for the straw disposition based on the local development status and condition. The research results can provide scientific guidance for the management strategy of the comprehensive utilization of straw resources.
14
العجمي, Reema Ghazi. "Waste-to-energy and electricity generation: Analysis for Saudi Arabia." مجلة العلوم الإقتصادية و الإدارية و القانونية 6, no. 24 (October 30, 2022): 164–80. http://dx.doi.org/10.26389/ajsrp.m180722.
Abstract:
Saudi Arabia is a developing country experiencing both rapid economic growth and vast urbanization. These have led to waste disposal problems. Therefore, waste-to-energy (WTE) processes, a source of renewable energy, are expected to play an increasingly important role in sustainable management of municipal solid waste (MSW). The purpose of this paper is to estimate the energy produced from the WTE method, to assist policymakers to develop a strategy that enhances the environmental quality, through reducing energy generation in the electricity sector. This study begins by presenting Saudi's current MSW management situation and its waste disposal process as well as the major challenges associated with Saudi's WTE incineration before discussing the economic and environmental benefits of the WTE method. Since waste can be used to produce energy for electricity and heat, this study estimated energy generated from WTE technology through employing estimated baseline scenario and future scenarios. Findings indicated that future scenarios are better than the business-as-usual scenario. The paper concludes that the WTE method can produce energy for electricity generation which in turn reduces the volume and the production of greenhouse gas (GHG) emissions produced from MSW. The waste-to energy approach with supportive policies could lead the country to achieve sustainable development.
15
Demirel, O. F., and T. R. Willemain. "Generation of simulation input scenarios using bootstrap methods." Journal of the Operational Research Society 53, no. 1 (January 2002): 69–78. http://dx.doi.org/10.1057/palgrave/jors/2601251.
16
STILMAN, BORIS, VLADIMIR YAKHNIS, and OLEG UMANSKIY. "KNOWLEDGE ACQUISITION AND STRATEGY GENERATION WITH LG WARGAMING TOOLS." International Journal of Computational Intelligence and Applications 02, no. 04 (December 2002): 385–410. http://dx.doi.org/10.1142/s1469026802000713.
Abstract:
This paper describes a wargaming software kit, which includes two software tools, LG-CONSTRUCTOR and LG-STRATEGIST. Both are based on the new type of game theory called Linguistic Geometry (LG) for solving higher-dimensional Abstract Board Games (ABG). LG-CONSTRUCTOR will supply warfighters at all levels with the patterns of war games. With LG-CONSTRUCTOR, a warfighter will be able to consider and visualize the most viable patterns and quickly convert them into the LG hypergame, most adequate to the current mission. The adequacy of construction will be tested on the "fly" by playing and re-playing semi-finished hypergames. Employing this hypergame, LG-STRATEGIST will generate the best strategies for the military operation for all the contesting sides of the conflict. It will plan the entire operation, assess possible courses of actions (COA) for all sides, select counteractions, control operation by re-planning in real time, and play and re-play various "what-if" scenarios.
17
Islam, K. M. Nazmul. "Municipal Solid Waste to Energy Generation in Bangladesh: Possible Scenarios to Generate Renewable Electricity in Dhaka and Chittagong City." Journal of Renewable Energy 2016 (2016): 1–16. http://dx.doi.org/10.1155/2016/1712370.
Abstract:
Increased generation of methane (CH4) from municipal solid wastes (MSW) alarms the world to take proper initiative for the sustainable management of MSW, because it is 34 times stronger than carbon dioxide (CO2). Mounting land scarcity issue around the world brands the waste to energy (WtE) strategy for MSW management in urban areas as a promising option, because WtE not only reduces the land pressure problem, but also generates electricity, heat, and green jobs. The goal of this study is to evaluate the renewable electricity generation potential and associated carbon reduction of MSW management in Bangladesh using WtE strategies. The study is conducted in two major cities of Bangladesh: Dhaka and Chittagong. Six different WtE scenarios are evaluated consisting of mixed MSW incineration and landfill gas (LFG) recovery system. Energy potential of different WtE strategy is assessed using standard energy conversion model and subsequent GHGs emissions models. Scenario A1results in highest economic and energy potential and net negative GHGs emission. Sensitivity analysis by varying MSW moisture content reveals higher energy potential and less GHGs emissions from MSW possessing low moisture content. The study proposes mixed MSW incineration that could be a potential WtE strategy for renewable electricity generation in Bangladesh.
18
Ahmadi, Hasia, Yos Sunitiyoso, and Agung Wicaksono. "Scenario Planning of PLN Indonesia Power in 2030: To Be a Leading Green and Sustainable Power Generation Company." European Journal of Business and Management Research 8, no. 4 (July 27, 2023): 129–39. http://dx.doi.org/10.24018/ejbmr.2023.8.4.2016.
Abstract:
Following the company's transformation in 2023, PLN IP's generating capacity now reached 21 GW, establishing it as the largest power generation company in Southeast Asia. However, most of PLN IP's power plants rely on fossil fuels. Therefore, the purpose of this study is to formulate a strategy that enables PLN IP to become a leading sustainable company and support Indonesia's target of achieving a renewable energy mix of 24.8% by 2030. The scenario planning method is used in this study to anticipate and prepare for future critical uncertainties that may impact the business of PLN IP. Critical uncertainties are obtained from analyzing the company's business, which is then elaborated with the results of semi-structured interviews from internal and external stakeholders. Five scenarios have been identified, namely: Parking the Bus, Kick & Rush, Tiki Taka, Gegenpressing, and Catenaccio. Each scenario is driven by a different critical uncertainty, consisting of electricity supply and demand, fuel prices, regulations, technological advancements, and investments. Key leading indicators of driving factors have been determined to detect potential scenario trends as early warning signals. This study is expected to serve as a reference for policymakers and stakeholders in the electricity sector to apply a balanced strategy for providing clean, reliable, and affordable electricity in Indonesia.
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Viegas, Luís Eduardo Alonso, and Marcelo Augusto Vieira Graglia. "Local platform as a means of socioeconomic development and generation of well-being." Journal on Innovation and Sustainability RISUS 15, no. 1 (April 29, 2024): 26–36. http://dx.doi.org/10.23925/2179-3565.2023v15i1p26-36.
Abstract:
The Digital Age has been established in the 21st century, generating profound changes in society and has its multidisciplinarity especially perceived with Artificial Intelligence. The concentration of economic power and income are important concerns. Predictions about the future of work range from alarming scenarios to optimistic scenarios. A complex scenario like this requires sophisticated approaches and strong coordination between the parties involved. The true legacy depends on how society will face the challenges and seek opportunities in the new era. Collective action is essential to promote people's well-being. The objective of this article is to propose the adoption of an ecosystem supported by a digital platform to promote socioeconomic development, integrating the different local agents to strengthen the community and expand opportunities for representatives of different social strata. This requires cooperation between public authorities, companies, and the community to create strategy, structure, and actions that meet local needs. The research was based on a literature review and application of concepts found in the literature on well-being, economic development, organizational models, networks, digital platforms and smart cities.
20
He, Shuaijia, and Junyong Liu. "Optimal Allocation Stochastic Model of Distributed Generation Considering Demand Response." Energies 17, no. 4 (February 7, 2024): 795. http://dx.doi.org/10.3390/en17040795.
Abstract:
Demand response (DR) can improve the accommodation of renewable energy and further affect the distributed generation (DG) allocation strategy. In this context, this paper proposes a stochastic optimal allocation model of DG, considering DR. Firstly, to address the uncertainty of wind and solar power outputs, a large number of scenarios of wind and solar power are generated based on the scenario method, which are then clustered into 10 typical scenarios by the k-means method. Secondly, with the goal of maximizing the total cost, the DR cost and corresponding constraints are introduced. Then, the stochastic planning model for DG is established, where the planning level aims to minimize the investment cost while the operation level minimizes the total operation expectation cost. For the non-linear term in the DR cost and power flow constraint, the Taylor expansion method and second-order conic relaxation method are both adopted to transform the original mixed-integer non-linear model to the mixed-integer second-order conic planning model. Finally, the whole planning model for DG is solved by CPLEX 12.6.0. The results show that DR can reduce the total cost and improve the accommodation of renewable energy in the DG planning process, which should be paid more attention to in the DG planning model.
21
Fogarassy, Csaba, and Attila Kovacs. "The Cost-Benefit Relations of the Future Environmental Related Developments Strategies in the Hungarian Energy Sector." YBL Journal of Built Environment 4, no. 1 (July 1, 2016): 33–48. http://dx.doi.org/10.1515/jbe-2016-0004.
Abstract:
Abstract In the case of economic and social wealth, it is strategically essential to provide reliable energy sources which are available in long-term. Setting an energy network which suits the sustainable criteria might take a long time. Therefore, it is important to make decisions on the energy sector in advance. The Hungarian National Energy strategy elaborated on certain scenarios towards 2030, which describe the possible electricity generation opportunities up to 2020 and 2030. For 2020, there is already an accurate recommendation, but in case of the 2030 targets, there are several ways for innovation. Out of all, the realization of the “Nuclear-Carbon-Green” scenario seems most likely to be implemented. It implies the obvious involvement of nuclear energy potential development in the future strategies. Considering this trend, the present study divides the mentioned strategy into “Nuclear-Carbon” and “Nuclear-Green” scenarios to compare their long-term efficiency by economic means.
22
Luo, Bin, Shumin Miao, Chuntian Cheng, Yi Lei, Gang Chen, and Lang Gao. "Long-Term Generation Scheduling for Cascade Hydropower Plants Considering Price Correlation between Multiple Markets." Energies 12, no. 12 (June 12, 2019): 2239. http://dx.doi.org/10.3390/en12122239.
Abstract:
The large-scale cascade hydropower plants in southwestern China now challenge a multi-market environment in the new round of electricity market reform. They not only have to supply the load for the local provincial market, but also need to deliver electricity to the central and eastern load centers in external markets, which makes the generation scheduling much more complicated, with a correlated uncertain market environment. Considering the uncertainty of prices and correlation between multiple markets, this paper has proposed a novel optimization model of long-term generation scheduling for cascade hydropower plants in multiple markets to seek for the maximization of overall benefits. The Copula function is introduced to describe the correlation of stochastic prices between multiple markets. The price scenarios that obey the Copula fitting function are then generated and further reduced by using a scenario reduction strategy that combines hierarchical clustering and inconsistent values. The proposed model is applied to perform the long-term generation scheduling for the Wu River cascade hydropower plants and achieves an increase of 106.93 million yuan of annual income compared with the conventional scheduling model, without considering price scenarios, showing better performance in effectiveness and robustness in multiple markets.
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Uthathip, Narongkorn, Pornrapeepat Bhasaputra, and Woraratana Pattaraprakorn. "Stochastic Modelling to Analyze the Impact of Electric Vehicle Penetration in Thailand." Energies 14, no. 16 (August 17, 2021): 5037. http://dx.doi.org/10.3390/en14165037.
Abstract:
Electric Vehicle (EV) technology is one of the most promising solutions to reduce dependence on fossil fuels and greenhouse gas (GHG) emissions in the transportation sector. However, a large increase of EVs raises concerns about negative impacts on electricity generation, transmission, and distribution systems. This study analyzes the benefits and trade-offs for EV penetration in Thai road transport based on EV penetration scenarios from 2019 to 2036. Two charging strategies are considered to assess the impact of EV charging: free charging and off-peak charging. Uncertainty variables are considered by a stochastic approach based on Monte-Carlo simulation (MCS). The simulation results shown that the adoption of EVs can reduce both energy consumption and GHG emissions. The results also indicate that the increased load due to EV charging demand in all scenarios is still within the buffer level, compared to the installed generation capacity in the Power Development Plan 2018 revision 1 (PDP2018r1), and the off-peak charging strategy is more beneficial than the free-charging strategy. However, the increased load demand caused by all EV charging strategies has a direct impact on the power generating schedule, and also decreases the system reliability level.
24
Nie, Zifei, and Hooman Farzaneh. "Role of Model Predictive Control for Enhancing Eco-Driving of Electric Vehicles in Urban Transport System of Japan." Sustainability 13, no. 16 (August 16, 2021): 9173. http://dx.doi.org/10.3390/su13169173.
Abstract:
Electrification alters the energy demand and environmental impacts of vehicles, which brings about new challenges for sustainability in the transport sector. To further enhance the energy economy of electric vehicles (EVs) and offer an energy-efficient driving strategy for next-generation intelligent mobility in daily synthetic traffic situations with mixed driving scenarios, the model predictive control (MPC) algorithm is exploited to develop a predictive cruise control (PCC) system for eco-driving based on a detailed driving scenario switching logic (DSSL). The proposed PCC system is designed hierarchically into three typical driving scenarios, including car-following, signal anticipation, and free driving scenario, using one linear MPC and two nonlinear MPC controllers, respectively. The performances of the proposed tri-level MPC-based PCC system for EV eco-driving were investigated by a numerical simulation using the real road and traffic data of Japan under three typical driving scenarios and an integrated traffic situation. The results showed that the proposed PCC system can not only realize driving safety and comfortability, but also harvest considerable energy-saving rates during either car-following (16.70%), signal anticipation (12.50%), and free driving scenario (30.30%), or under the synthetic traffic situation (19.97%) in urban areas of Japan.
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Pal, Poushali, Parvathy Ayalur Krishnamoorthy, Devabalaji Kaliaperumal Rukmani, S. Joseph Antony, Simon Ocheme, Umashankar Subramanian, Rajvikram Madurai Elavarasan, Narottam Das, and Hany M. Hasanien. "Optimal Dispatch Strategy of Virtual Power Plant for Day-Ahead Market Framework." Applied Sciences 11, no. 9 (April 23, 2021): 3814. http://dx.doi.org/10.3390/app11093814.
Abstract:
Renewable energy sources prevail as a clean energy source and their penetration in the power sector is increasing day by day due to the growing concern for climate action. However, the intermittent nature of the renewable energy based-power generation questions the grid security, especially when the utilized source is solar radiation or wind flow. The intermittency of the renewable generation can be met by the integration of distributed energy resources. The virtual power plant (VPP) is a new concept which aggregates the capacities of various distributed energy resources, handles controllable and uncontrollable loads, integrates storage devices and empowers participation as an individual power plant in the electricity market. The VPP as an energy management system (EMS) should optimally dispatch the power to its consumers. This research work is proposed to analyze the optimal scheduling of generation in VPP for the day-ahead market framework using the beetle antenna search (BAS) algorithm under various scenarios. A case study is considered for this analysis in which the constituting energy resources include a photovoltaic solar panel (PV), micro-turbine (MT), wind turbine (WT), fuel cell (FC), battery energy storage system (BESS) and controllable loads. The real-time hourly load curves are considered in this work. Three different scenarios are considered for the optimal dispatch of generation in the VPP to analyze the performance of the proposed technique. The uncertainties of the solar irradiation and the wind speed are modeled using the beta distribution method and Weibull distribution method, respectively. The performance of the proposed method is compared with other evolutionary algorithms such as particle swarm optimization (PSO) and the genetic algorithm (GA). Among these above-mentioned algorithms, the proposed BAS algorithm shows the best scheduling with the minimum operating cost of generation.
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Jiang, Nan, Minjun Peng, Wei Wei, and Tenglong Cong. "Strategy Evaluation for Cavity Flooding during an ESBO Initiated Severe Accident." Science and Technology of Nuclear Installations 2018 (2018): 1–15. http://dx.doi.org/10.1155/2018/8680406.
Abstract:
Intentional depressurization and cavity flooding are two important measures in current severe accident management guidelines (SAMGs). An extreme scenario of an extended station blackout (ESBO), when electric power is unavailable for more than 24 hours, actually occurred in the Fukushima Daiichi accident and attracted lots of attention. In an ESBO, the containment spray cannot be activated for condensation, and, thus, cavity flooding will generate a large amount of steam, which, ironically, overpressurizes the containment to failure before the reactor vessel is melted through. Therefore, consideration of these conflicting issues and the ways in which plants operate is crucial for strengthening the strategies outlined in SAMGs. In this paper, the effects of intentional depressurization and cavity flooding in an ESBO for a representative 900 MW second-generation pressurized water reactor (PWR) are simulated with MAAP4 code. Diverse scenarios with different starting times of depressurization and water injection are also compared to summarize the positive and negative impacts for accident mitigation. The phenomena associated with creep ruptures, hydrogen combustion, corium stratification, and cavity boiling are also analyzed in detail to strengthen our understanding of severe accident mechanisms. The results point out the facility limitations of second-generation PWRs which can improve existing SAMGs.
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Hwang, Soon Hyun, and Balho H. Kim. "A Study on the Generation Expansion Planning Model Reflecting Environmental Policies." Advanced Materials Research 962-965 (June 2014): 2210–19. http://dx.doi.org/10.4028/www.scientific.net/amr.962-965.2210.
Abstract:
In response to climate change convention, Green Growth Policy was implemented in Korea and GENCOs are having a huge burden financially because of this policy. To clear these problems, it is necessary to make adaptive strategy for Green Growth Policy to reduce overall cost. However, the existing tools have limitation that it cannot consider the environmental constraints in solving this problem. Therefore, in this paper, we developed a generation expansion planning model which can consider the environmental constraints and studied GENCO’s optimal portfolio strategy by selecting expected scenarios.
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Al-Jawad and Kalin. "Assessment of Water Resources Management Strategy under Different Evolutionary Optimization Techniques." Water 11, no. 10 (September 28, 2019): 2021. http://dx.doi.org/10.3390/w11102021.
Abstract:
Competitive optimization techniques have been developed to address the complexity of integrated water resources management (IWRM) modelling; however, model adaptation due to changing environments is still a challenge. In this paper we employ multi-variable techniques to increase confidence in model-driven decision-making scenarios. Here, water reservoir management was assessed using two evolutionary algorithm (EA) techniques, the epsilon-dominance-driven self-adaptive evolutionary algorithm (-DSEA) and the Borg multi-objective evolutionary algorithm (MOEA). Many objective scenarios were evaluated to manage flood risk, hydropower generation, water supply, and release sequences over three decades. Computationally, the -DSEA’s results are generally reliable, robust, effective and efficient when compared directly with the Borg MOEA but both provide decision support model outputs of value.
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Baron, Silja, Jannis Hoek, Inka Kaufmann Alves, and Sabine Herz. "Comprehensive scenario management of sustainable spatial planning and urban water services." Water Science and Technology 73, no. 5 (November 12, 2015): 1041–51. http://dx.doi.org/10.2166/wst.2015.578.
Abstract:
Adaptations of existing central water supply and wastewater disposal systems to demographic, climatic and socioeconomic changes require a profound knowledge about changing influencing factors. The paper presents a scenario management approach for the identification of future developments of drivers influencing water infrastructures. This method is designed within a research project with the objective of developing an innovative software-based optimisation and decision support system for long-term transformations of existing infrastructures of water supply, wastewater and energy in rural areas. Drivers of water infrastructures comprise engineering and spatial factors and these are predicted by different methods and techniques. The calculated developments of the drivers are illustrated for a model municipality. The developed scenario-manager enables the generation of comprehensive scenarios by combining different drivers. The scenarios are integrated into the optimisation model as input parameters. Furthermore, the result of the optimisation process – an optimal transformation strategy for water infrastructures – can have impacts on the existing fee system. General adaptation possibilities of the present fee system are presented.
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Fernández-Guillamón, Ana, Antonio Vigueras-Rodríguez, Emilio Gómez-Lázaro, and Ángel Molina-García. "Fast Power Reserve Emulation Strategy for VSWT Supporting Frequency Control in Multi-Area Power Systems." Energies 11, no. 10 (October 16, 2018): 2775. http://dx.doi.org/10.3390/en11102775.
Abstract:
The integration of renewables into power systems involves significant targets and new scenarios with an important role for these alternative resources, mainly wind and PV power plants. Among the different objectives, frequency control strategies and new reserve analysis are currently considered as a major concern in power system stability and reliability studies. This paper aims to provide an analysis of multi-area power systems submitted to power imbalances, considering a high wind power penetration in line with certain European energy road-maps. Frequency control strategies applied to wind power plants from different areas are studied and compared for simulation purposes, including conventional generation units. Different parameters, such as nadir values, stabilization time intervals and tie-line active power exchanges are also analyzed. Detailed generation unit models are included in the paper. The results provide relevant information on the influence of multi-area scenarios on the global frequency response, including participation of wind power plants in system frequency control.
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Zaheb, Hameedullah, Habibullah Amiry, Mikaeel Ahmadi, Habibullah Fedayi, Sajida Amiry, and Atsushi Yona. "Maximizing Annual Energy Yield in a Grid-Connected PV Solar Power Plant: Analysis of Seasonal Tilt Angle and Solar Tracking Strategies." Sustainability 15, no. 14 (July 14, 2023): 11053. http://dx.doi.org/10.3390/su151411053.
Abstract:
Harnessing the abundant solar resources holds great potential for sustainable energy generation. This research paper delves into a comprehensive analysis of seasonal tilt and solar tracking strategy scenarios for a 15 MW grid-connected PV solar power plant situated in Kandahar province, Afghanistan. The study investigates the impact of fixed tilt, seasonal tilt, SAHST (single-axis horizontal solar tracking), and SAVST (single-axis vertical solar tracking) on energy yield, considering technical, economic, and environmental aspects. In the first scenario, a fixed tilt angle of 31 degrees was employed. The second scenario explored the use of seasonal tilt angles, with a summer tilt angle of 15 degrees and a winter tilt angle of 30 degrees. The third scenario analyzed SAHST. Finally, the fourth scenario focused on implementing SAVST. SAVST proved to be an exceptional solution, showcasing a remarkable increase in annual energy yield, and generating an additional 6680 MWh/year, 6336 MWh/year, and 5084 MWh/year compared to fixed, seasonal, and SAHST scenarios, respectively. As a result, surplus energy yielded an income of USD 554,440.00 per year compared to fixed tilt. However, the investment cost for the solar tracking system amounted to USD 1,451,932, accompanied by an annual operation and maintenance cost of 0.007 USD/W/year. The analysis revealed a promising payback period of 3 years, confirming the economic feasibility of this investment. The findings underscore the effectiveness of different strategies for optimizing solar power generation in the Kandahar region. Notably, the installation of SAVST emerged as an influential solution, significantly increasing power production. These research outcomes bear practical implications for solar tracking strategies for addressing the load challenges faced by Kandahar province and offer valuable insights for the operators and operation of solar power plants in similar regions.
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Zhou, Liangchuan, and Surendra Gupta. "A Pricing and Acquisition Strategy for New and Remanufactured High-Technology Products." Logistics 3, no. 1 (February 21, 2019): 8. http://dx.doi.org/10.3390/logistics3010008.
Abstract:
New generations of high-technology products are frequently launched before the previous model is sold out. Customers have an incentive to end the use of their old product and purchase a new one with the latest technological innovations. The unsold old models become less attractive, while the supply of remanufactured products from end-of-use products is uncertain in time, quantity, and quality. Other than adjusting the price, upgrading the returning unsold new products may be a source of remedy. This study provides profit maximization models associated with customer choice demand functions based on manufacturer, retailer, and joint supply chain scenarios. Two acquisition strategies are compared: acquire end-of-use products only and collect both end-of-use products and unsold old-style new products. The results reveal that returning the optimal quantity of overstocked new products brings about a greater benefit in all scenarios. Compared to the remanufacturer, the retailer is the optimal undertaker for collecting used products. In addition to this, slow technological development of the new-generation model causes a decrease in profit for the manufacturer. The optimal quantity of new products to be bought back decreases, because both the manufacturer and the retailer prefer to promote unsold outmoded products rather than upgrade the used products.
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Leon, Luis M., Arturo S. Bretas, and Sergio Rivera. "Quadratically Constrained Quadratic Programming Formulation of Contingency Constrained Optimal Power Flow with Photovoltaic Generation." Energies 13, no. 13 (June 28, 2020): 3310. http://dx.doi.org/10.3390/en13133310.
Abstract:
Contingency Constrained Optimal Power Flow (CCOPF) differs from traditional Optimal Power Flow (OPF) because its generation dispatch is planned to work with state variables between constraint limits, considering a specific contingency. When it is not desired to have changes in the power dispatch after the contingency occurs, the CCOPF is studied with a preventive perspective, whereas when the contingency occurs and the power dispatch needs to change to operate the system between limits in the post-contingency state, the problem is studied with a corrective perspective. As current power system software tools mainly focus on the traditional OPF problem, having the means to solve CCOPF will benefit power systems planning and operation. This paper presents a Quadratically Constrained Quadratic Programming (QCQP) formulation built within the matpower environment as a solution strategy to the preventive CCOPF. Moreover, an extended OPF model that forces the network to meet all constraints under contingency is proposed as a strategy to find the power dispatch solution for the corrective CCOPF. Validation is made on the IEEE 14-bus test system including photovoltaic generation in one simulation case. It was found that in the QCQP formulation, the power dispatch calculated barely differs in both pre- and post-contingency scenarios while in the OPF extended power network, node voltage values in both pre- and post-contingency scenarios are equal in spite of having different power dispatch for each scenario. This suggests that both the QCQP and the extended OPF formulations proposed, could be implemented in power system software tools in order to solve CCOPF problems from a preventive or corrective perspective.
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Ji, Ning, Yongnan Rao, Xue Wang, Decai Zou, Xiaofei Chen, and Yao Guo. "Spoofing Traction Strategy Based on the Generation of Traction Code." Remote Sensing 15, no. 2 (January 14, 2023): 500. http://dx.doi.org/10.3390/rs15020500.
Abstract:
Traction spoofing is an important component of Global Navigation Satellite System (GNSS) intermediate attacks, and the traction scheme directly determines the concealment of spoofing. However, spoofing via conventional traction strategies can be easily detected using Time of Arrival (TOA) and power detection. Based on a BPSK-modulated signal, a novel traction strategy using traction code is proposed to suppress part of the authentication signal and form an ideal correlation peak. This strategy was modeled and simulated to verify its theoretical feasibility. Effective spoofing data were generated based on the signal generation software to verify the spoofing effect with the reception of the software receiver. It can be inferred that no significant distortion occurred throughout the traction process, and the value range of the traction speed was expanded. The received results in different scenarios demonstrated that the observations’ Root-Mean-Square Error (RMSE) percentage change in the proposed strategy is significantly better than those of conventional strategies. A Ratio Test was also performed, verifying that the strategy can bypass Signal Quality Monitoring (SQM) detection. Meanwhile, the proposed strategy remained effective when the C/N0 increased to 60 dBHz. In summary, the proposed strategy exhibits destructiveness, concealment, and adaptability on the battlefield.
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Thi Thuy Linh, Nguyen, and Frederick N. F. Chou. "Mitigate water shortage by improving hydropower generation strategy of the cascade reservoir system in the Be River Basin, Vietnam." Water Supply 20, no. 8 (September 15, 2020): 3216–32. http://dx.doi.org/10.2166/ws.2020.220.
Abstract:
Abstract To meet increasing water consumption with limited water resources, management approaches that transfer water between purposes must be improved for sustainable development. This entails an urgent requirement for appropriate water resources management within water–energy interaction if severe water shortage occurs occasionally. This study evaluates hydropower generation policies of a cascade reservoir system in the Be River Basin in terms of security of water supply and energy production. The Generalized Water Allocation Simulation Model (GWASIM) was applied to simulate the water use of a complex system of hydropower generation and water supply. Two water allocation scenarios and six alternatives defined by varying monthly generating hours were modeled and compared. The results demonstrate that a compromise between hydropower generation and water supply can be negotiated to reduce the severity of water shortages. Different monthly hours of hydropower generation among alternatives show an effect on improving power production and reliable water supply. This study provides overall insight into the performance of a multi-purpose cascade reservoir system. It will provide a foundation for improving future study of reservoir operations in meeting the increasing demands of water and energy in Vietnam.
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Ali, Sajjad, Imran Khan, Sadaqat Jan, and Ghulam Hafeez. "An Optimization Based Power Usage Scheduling Strategy Using Photovoltaic-Battery System for Demand-Side Management in Smart Grid." Energies 14, no. 8 (April 15, 2021): 2201. http://dx.doi.org/10.3390/en14082201.
Abstract:
Due to rapid population growth, technology, and economic development, electricity demand is rising, causing a gap between energy production and demand. With the emergence of the smart grid, residents can schedule their energy usage in response to the Demand Response (DR) program offered by a utility company to cope with the gap between demand and supply. This work first proposes a novel optimization-based energy management framework that adapts consumer power usage patterns using real-time pricing signals and generation from utility and photovoltaic-battery systems to minimize electricity cost, to reduce carbon emission, and to mitigate peak power consumption subjected to alleviating rebound peak generation. Secondly, a Hybrid Genetic Ant Colony Optimization (HGACO) algorithm is proposed to solve the complete scheduling model for three scenarios: without photovoltaic-battery systems, with photovoltaic systems, and with photovoltaic-battery systems. Thirdly, rebound peak generation is restricted by using Multiple Knapsack Problem (MKP) in the proposed algorithm. The presented model reduces the cost of using electricity, alleviates the peak load and peak-valley, mitigates carbon emission, and avoids rebound peaks without posing high discomfort to the consumers. To evaluate the applicability of the proposed framework comparatively with existing frameworks, simulations are conducted. The results show that the proposed HGACO algorithm reduced electricity cost, carbon emission, and peak load by 49.51%, 48.01%, and 25.72% in scenario I; by 55.85%, 54.22%, and 21.69% in scenario II, and by 59.06%, 57.42%, and 17.40% in scenario III, respectively, compared to without scheduling. Thus, the proposed HGACO algorithm-based energy management framework outperforms existing frameworks based on Ant Colony Optimization (ACO) algorithm, Particle Swarm Optimization (PSO) algorithm, Genetic Algorithm (GA), Hybrid Genetic Particle swarm Optimization (HGPO) algorithm.
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Melita, Carmelo Donato, Dario Calogero Guastella, Luciano Cantelli, Giuseppe Di Marco, Irene Minio, and Giovanni Muscato. "Low-Altitude Terrain-Following Flight Planning for Multirotors." Drones 4, no. 2 (June 25, 2020): 26. http://dx.doi.org/10.3390/drones4020026.
Abstract:
Surveying with unmanned aerial vehicles flying close to the terrain is crucial for the collection of details that are not visible when flying at higher altitudes. This type of missions can be applied in several scenarios such as search and rescue, precision agriculture, and environmental monitoring, to name a few. We present a strategy for the generation of low-altitude trajectories for terrain following. The trajectory is generated taking into account the morphology of the area of interest, represented as a georeferenced Digital Surface Model (DSM), while ensuring a safe separation from any obstacle. The surface model of the scenario is created by using a UAV-based photogrammetry software, which processes the images acquired during a preliminary mission at high altitude. The solution was developed, tested, and verified both in simulation and in real scenarios with a multirotor equipped with low-cost sensing. The experimental results proved the validity of the generation of trajectories at altitudes lower than most of the works available in the literature. The images acquired during the low-altitude mission were processed to obtain a high-resolution reconstruction of the area as a representative application result.
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Shi, Tao, Jialin Sheng, Zhiqiang Chen, and Hangyu Zhou. "Simulation Experiment Design and Control Strategy Analysis in Teaching of Hydrogen-Electric Coupling System." Processes 12, no. 1 (January 5, 2024): 138. http://dx.doi.org/10.3390/pr12010138.
Abstract:
Hydrogen energy, as a clean and green energy medium, is characterized by large capacity, extended lifespan, convenient storage, and seamless transmission. On the one hand, in the power system, hydrogen can be prepared by the electrolysis of water using the surplus power from intermittent new energy generation, such as photovoltaic and wind power, to increase the space for new energy consumption. On the other hand, it can be used to generate electricity from the chemical reaction between hydrogen and oxygen through the fuel cell and be used as a backup power source when there is a shortage of power supply. In this paper, based on the teaching practice, the conversion mechanism and coupling relationship between various forms of energy, such as photovoltaic energy, hydrogen energy, and electric energy, were deeply analyzed. Further, a hydrogen-electricity coupling digital simulation experimental system, including photovoltaic power generation, fuel cell, and electrolysis hydrogen system, was formed. Simultaneously, considering the synergy between hydrogen production and electricity generation businesses, as well as the demand for the efficient utilization and flexible regulation of multiple energy sources, eight sets of simulation experimental scenarios were designed. A cooperative control strategy for the hydrogen-electric coupling system was proposed and validated through simulation on the MATLAB/SIMULINK-R2023a platform. This study shows that the simulation system has rich experimental scenarios and control strategies, and can comprehensively and accurately demonstrate the multi-energy complementary and cooperative control characteristics of the hydrogen-electric coupling system.
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Zuñiga-Cortes, Fabian, Eduardo Caicedo-Bravo, and Juan D. Garcia-Racines. "Reference Framework Based on a Two-Stage Strategy for Sizing and Operational Management in Electrical Microgrid Planning." Sustainability 15, no. 19 (October 3, 2023): 14449. http://dx.doi.org/10.3390/su151914449.
Abstract:
The challenges of today’s energy landscape, marked by the search for sustainable development, the expansion of coverage, and the diversification of the energy matrix, allow for electricity systems focusing on renewable energy resources. Microgrids are considered an efficient paradigm for managing distributed renewable energy generation and providing reliable access to electricity in remote areas where the grid has not been extended. However, their planning is a complex task that requires a thorough understanding of various multi-dimensional aspects and decision-making scenarios to define feasible and sustainable alternatives. In this context, this study presents a new planning framework based on a two-stage strategy. The strategy seeks to optimize the capacity of generation resources, considering the microgrid’s operational knowledge in various scenarios and aspects related to its sustainability. The framework was evaluated through a case of planning a microgrid for a remote community in Vaupés, Colombia, considering the local energy potential and demand requirements. Twenty optimized alternatives were identified based on the best compromise levels achieved for a set of performance criteria in the technical, economic, environmental, and social dimensions.
40
Hou, Langbo, Heng Chen, Jinjun Wang, Shichao Qiao, Gang Xu, Honggang Chen, and Tao Liu. "Optimal Dispatch Strategy for a Distribution Network Containing High-Density Photovoltaic Power Generation and Energy Storage under Multiple Scenarios." Inventions 8, no. 5 (October 19, 2023): 130. http://dx.doi.org/10.3390/inventions8050130.
Abstract:
To better consume high-density photovoltaics, in this article, the application of energy storage devices in the distribution network not only realizes the peak shaving and valley filling of the electricity load but also relieves the pressure on the grid voltage generated by the distributed photovoltaic access. At the same time, photovoltaic power generation and energy storage cooperate and have an impact on the tidal distribution of the distribution network. Since photovoltaic output has uncertainty, the maximum photovoltaic output in each scenario is determined by the clustering algorithm, while the storage scheduling strategy is reasonably selected so the distribution network operates efficiently and stably. The tidal optimization of the distribution network is carried out with the objectives of minimizing network losses and voltage deviations, two objectives that are assigned comprehensive weights, and the optimization model is constructed by using a particle swarm algorithm to derive the optimal dispatching strategy of the distribution network with the cooperation of photovoltaic and energy storage. Finally, a model with 30 buses is simulated and the system is optimally dispatched under multiple scenarios to demonstrate the necessity of conducting coordinated optimal dispatch of photovoltaics and energy storage.
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Niu, Dongxiao, Weibo Zhao, and Zongyun Song. "Research on decision making of energy utilization project in China based on benefit evaluation." International Journal of Energy Sector Management 13, no. 1 (April 1, 2019): 183–95. http://dx.doi.org/10.1108/ijesm-12-2017-0001.
Abstract:
Purpose There are thousands of areas excluded from using electrical energy in China. It is mainly because that these places, which are away from towns, have the characteristics of scattered living and low-power consumption and are difficult to construct the power grid. The utilization of energy in remote areas could improve the level of education and quality of life for people living in there, which has great social significance. However, how to choose the optimal power generation model quantitatively according to local energy advantages is a difficult problem. Design/methodology/approach To carry out a better assessment of the energy benefits of Chinese rural areas to assist the decision-making of energy utilization project, this paper takes Sunan Yugu Autonomous County in Gansu Province as an example. Four feasible energy utilization scenarios are proposed by analyzing its geographical conditions and re-source advantage, respectively, are photovoltaic power generation, biomass power generation, wind power generation and power grid extension. Based on the above scenarios, the evaluation index system of comprehensive utilization of energy in remote areas is constructed, and the comprehensive benefit of each model is evaluated by adopting entropy-based fuzzy comprehensive evaluation model. Findings Evaluation results show that the comprehensive benefits of photovoltaic power generation is the best, followed by power grid extension. Thus, preference should be given to the two models in the energy utilization in Sunan County. This evaluation model can provide a scientific reference for the selection decision-making of energy utilization project, which is helpful to provide the feasibility and efficiency of the construction of energy utilization project. Originality/value The authors construct the comprehensive benefit evaluation index system and evaluate the comprehensive benefits of different scenarios are by using entropy - fuzzy comprehensive evaluation model. Then the qualitative problem can be analyzed quantitatively. The purpose of this study is to support the decision-making of energy investment. Simultaneously, the paper also has some practical significance in improving the credibility of the government and the quality of local people’s life.
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Yang, Lilan, Shichao Wu, Kai Liao, Xuheng Ding, Zhiqiang You, Zhoujian Cao, Marek Biesiada, and Zong-Hong Zhu. "Event rate predictions of strongly lensed gravitational waves with detector networks and more realistic templates." Monthly Notices of the Royal Astronomical Society 509, no. 3 (November 15, 2021): 3772–78. http://dx.doi.org/10.1093/mnras/stab3298.
Abstract:
ABSTRACT Strong lensing of gravitational waves (GWs) is attracting growing attention of the community. The event rates of lensed GWs by galaxies were predicted in numerous papers, which used some approximations to evaluate the GW strains detectable by a single detector. The joint detection of GW signals by a network of instruments will increase the detecting ability of fainter and farther GW signals, which could increase the detection rate of the lensed GWs, especially for the 3rd generation detectors, e.g. Einstein Telescope (ET) and Cosmic Explorer (CE). Moreover, realistic GW templates will improve the accuracy of the prediction. In this work, we consider the detection of galaxy-scale lensed GW events under the 2nd, 2.5th, and 3rd generation detectors with the network scenarios and adopt the realistic templates to simulate GW signals. Our forecast is based on the Monte Carlo technique which enables us to take Earth’s rotation into consideration. We find that the overall detection rate is improved, especially for the 3rd generation detector scenarios. More precisely, it increases by ∼37 per cent adopting realistic templates, and under network detection strategy, further increases by ∼58 per cent comparing with adoption of the realistic templates, and we estimate that the 3rd generation GW detectors will detect hundreds lensed events per year. The effect from the Earth’s rotation is weakened in the detector network strategy.
43
Huang, Chao, Zhenyu Zhao, Qingwen Li, Xiong Luo, and Long Wang. "Wind Power Bidding Based on an Ensemble Differential Evolution Algorithm with a Problem-Specific Constraint-Handling Technique." Energies 17, no. 2 (January 12, 2024): 380. http://dx.doi.org/10.3390/en17020380.
Abstract:
The intermittent nature of wind power generation induces great challenges for power bidding in the electricity market. The deployment of battery energy storage can improve flexibility for power bidding. This paper investigates an optimal power bidding strategy for a wind–storage hybrid power plant in the day-ahead electricity market. To handle the challenges of the uncertainties of wind power generation and electricity prices, the optimal bidding problem is formulated as a risk-aware scenario-based stochastic programming, in which a number of scenarios are generated using a copula-based approach to represent the uncertainties. These scenarios consider the temporal correlation of wind power generation and electricity prices between consecutive time intervals. In the stochastic programming, a more practical but nonlinear battery operation cost function is considered, which leads to a nonlinear constrained optimization problem. To solve the nonlinear constrained optimization problem, an ensemble differential evolution (EDE) algorithm is proposed, which makes use of the merits of an ensemble of mutant operators to generate mutant vectors. Moreover, a problem-specific constraint-handling technique is developed. To validate the effectiveness of the proposed EDE algorithm, it is compared with state-of-the-art DE-based algorithms for constrained optimization problems, including a constrained composite DE (C2oDE) algorithm and a novel DE (NDE) algorithm. The experimental results demonstrate that the EDE algorithm is much more reliable and much faster in finding a better bidding strategy against benchmarking algorithms. More precisely, the average values of the success rate are 0.893, 0.667, and 0.96 for C2oDE, NDE, and EDE, respectively. Compared to C2oDE and NDE, the average value of the mean number of function evaluations to succeed with EDE is reduced by 76% and 59%, respectively.
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Kotecha, Nisarg. "An Analytical Study on Super Trend Trading System on Bank Nifty Perpetual Futures Contract." RESEARCH REVIEW International Journal of Multidisciplinary 8, no. 12 (December 14, 2023): 108–13. http://dx.doi.org/10.31305/rrijm.2023.v08.n12.016.
Abstract:
To trade efficiently and profitably, we must understand the on-going trend in the market. While a many of popular technical analysis indicators attempt to provide a better understanding of the overall trend in the market, they are limited in both trend generation and momentum. Based on the Japanese Heiken Ashi (HA) candles we develop one trading system, which can reflect in a single number both the likelihood of trend generation and momentum in the market. We back tested the strategy using HA and Super Trend for BANKNIFTY’S perpetual futures contract for one major time frame in the market which is the daily time frame. We find if the strategy gives consistent profits for traders in the markets. The study contributes to economic literature as well as to practical trading scenarios by the development of a trading strategy using Heiken-Ashi charts as a charting technique combined with Super Trend as an indicator, which gives the trend generation and momentum in the market.
45
Mao, Xiangyang, Qianyuan Xiao, Zhanjun Han, and Caihong Zhang. "Analysis and Construction of Typical Application Scenarios of Distribution Network Energy Storage Technology." Journal of Physics: Conference Series 2442, no. 1 (February 1, 2023): 012014. http://dx.doi.org/10.1088/1742-6596/2442/1/012014.
Abstract:
Abstract The power market in China is continuing to open, the energy Internet format is gradually being improved, and the energy storage system is going to become a major key technology that will support China’s energy development strategy. All of these developments are taking place against the backdrop of China’s massive investment in the development of new energy. It is one of the effective approaches to handle the problem of grid connection of new energy electrical power generation. This paper investigate and summarizes the typical application scenarios of the system from the three major fields of user side, power grid side, and power generation side, and takes user-side energy storage as an example to build an calculation model, and at the same time verifies it with cases to reflect the practical value. So as to draw the conclusion that the energy storage configuration can effectively regulate the output.
46
Osis, U., N. Talcis, and J. Ziemele. "Challenges and Barriers by Transition Towards 4th Generation District Heating System: A Strategy to Establish a Pricing Mechanism." Latvian Journal of Physics and Technical Sciences 56, no. 4 (August 1, 2019): 17–37. http://dx.doi.org/10.2478/lpts-2019-0022.
Abstract:
Abstract Transition of the district heating (DH) system to the 4th generation system involves several challenges, which refer not only to the introduction of state-of-art technologies, but also to the development of a sustainable pricing methodology. Introduction of the 4th generation systems will soon force the DH industry to solve issues regarding the possibility of organisation of the market in the same way as it happened in the power industry (Directive 2009/72/EC) by complete separation of producers from the transmission system service providers. The present article discusses various development scenarios of a DH utility within the framework of an organised market and their pricing methodologies, as well as evaluates their sustainability considering the transition to the 4th generation system.
47
Verma, Shekhar, Vaibhav Pandey, and Preeti Khurana. "Load Balancing using Fixed Geometric Arrangements of Fixed and Mobile Small Cells in Mili-Meter Network." Journal of Physics: Conference Series 2327, no. 1 (August 1, 2022): 012053. http://dx.doi.org/10.1088/1742-6596/2327/1/012053.
Abstract:
Abstract The upcoming generation of cellular networks is going to make extensive use of mmWave for communication. Hence, there will be a need for small cells to counter the loss incurred due to the more energy dissipation of mmWave. Small cells contain transmitters and receivers and hence there will be the need to balance the load efficiently so that no one is overwhelmed with functions to perform while others are relatively idle. In this paper, the performance of the user association algorithm is analysed when it is subjected to different scenarios like micro-urban, macro urban, suburban and rural. these scenarios are subjected to different frequency bands of mmWave and are compared for the values of the load balancing index. The load balancing algorithm is subjected to different small cell deployment techniques and the comparison is made for best deployment strategy among Quadrature based Approach (QBA) and Random deployment. Simulation results show that the sub-urban scenario has the maximum load-balancing index. On comparing QBA and random deployment approach QBA has a higher load balancing index in the suburban and rural scenarios and random deployment has a higher load balancing index in Indoor environments.
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Zhang, Tao, Xiaokang Zhou, Yao Gao, and Ruijin Zhu. "Optimal Dispatch of the Source-Grid-Load-Storage under a High Penetration of Photovoltaic Access to the Distribution Network." Processes 11, no. 10 (September 25, 2023): 2824. http://dx.doi.org/10.3390/pr11102824.
Abstract:
In the context of carbon peaking and carbon neutralization, distributed photovoltaics is a relatively mature new energy power generation technology that is being widely promoted. However, the randomness and volatility of distributed generation bring severe challenges to the distribution network’s operation. Based on this, taking the typical scenario of a high proportion of distributed photovoltaic grid connections against the background of a whole-county photovoltaic system as the research object, this paper constructs a source-grid-load-storage coordination optimal scheduling model in distribution networks, considering the spatial distribution of power flow, tie-line power fluctuation, grid loss, and voltage amplitude from the perspective of optimal day-to-day scheduling. Next, the Lehmer weighted and improved multi-mutation cooperation strategy differential evolution (LW-IMCSDE) algorithm is introduced to enhance the differential evolution algorithm based on the weighted Lehmer average, improved multi-mutation cooperation, and population update strategies. The feasibility and effectiveness of the algorithm are investigated by using a test function to verify its effectiveness. Finally, the feasibility and effectiveness of the proposed strategy are verified in two typical power scenarios: summer and winter.
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Dornellas, Carlos R. R., Armando M. Leite da Silva, João G. C. Costa, Zulmar S. Machado Jr, André L. M. Marcato, and João C. O. Mello. "Toward a New Nodal Pricing Policy of the Brazilian Transmission System Considering Multiple Hydrological Scenarios." International Transactions on Electrical Energy Systems 2022 (September 13, 2022): 1–12. http://dx.doi.org/10.1155/2022/7684857.
Abstract:
This paper presents a new method for allocating transmission system costs in hydro prevailing energy markets. The proposed approach is based on nodal pricing, and it considers multiple hydrological scenarios obtained from optimized energy models. These models are much closer to the operational reality of hydro dominant systems and more adequate to the transmission operation planning. The nodal pricing strategy comprises two steps to recover the total costs of the transmission system. The first one calculates the charges corresponding to the utilized network capacity, estimated at a specified operating condition, and it brings the locational signal to the tariff. In the second step, the charges related to the transmission capacity available but not used in the system are assessed by a postage stamp. This latter step accomplishes the task of recovering the total transmission charges and defining the final tariff for specific operating dispatching. On the other hand, the optimized energy models, based on stochastic dual dynamic programming, are used to minimize the operating costs while penalizing possible energy spillages. These models create monthly sequences of hydrothermal coordinated generating scenarios and dispatching conditions, which are used by the nodal pricing strategy to produce the generation/load charges and tariffs and the corresponding statistics. The proposed method, combining the nodal pricing strategy and the optimized energy model, is applied to a Brazilian network configuration and the results are fully discussed. Moreover, probabilistic analyses are also performed to describe the annual costs of transmission of thermoelectric and hydroelectric plants and industrial electrointensive consumers installed in different areas of the network.
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He, Qingsu, Muqing Wu, Pei Sun, Jinglin Guo, Lina Chen, Lihua Jiang, and Zhiwei Zhang. "Research on Charging Mechanism of Electric Vehicle Biased to Photovoltaic Nearby Absorption Strategy." Electronics 11, no. 20 (October 20, 2022): 3407. http://dx.doi.org/10.3390/electronics11203407.
Abstract:
With the promotion of the pilot development of distributed whole county roof photovoltaics in China, problems such as power consumption, energy regional balance, and grid stability have become prominent. In this paper, an application mode of electric vehicle (EV) charging network and distributed photovoltaic power generation local consumption is studied. The management idea of two-layer and four model has been established, including the regional distributed photovoltaic output model, electricity consumption model, EV consumption model, and regional grid load dispatching model, which can realize the scheduling of the energy flow formed by photovoltaic, induce the charging of EVs, and make the photovoltaic consumption in office building areas and residential building areas complementary. Firstly, according to the randomness of photovoltaic power generation and EV charging, the dynamic response capability, power support capability, effective convergence time, system stability, system failure rate, and other characteristics of regional loads are comprehensively analyzed, and the grid energy management model of EV charging network and distributed photovoltaic is proposed. Secondly, according to certain statistical characteristics, the distributed photovoltaic will be concentrated, and EV charging will be prioritized to achieve nearby consumption. Finally, different scenarios are described, and the scenarios of charging in the park, community life, and power supply service are selected for analysis. This mode is intended to guide the consumption of new energy through economic leverage, which can realize the unified regulation of distributed energy convergence, consumption and storage.