Статті в журналах з теми "Thermal energy demand forecast"
Щоб переглянути інші типи публікацій з цієї теми, перейдіть за посиланням: Thermal energy demand forecast.
Оформте джерело за APA, MLA, Chicago, Harvard та іншими стилями
Ознайомтеся з топ-50 статей у журналах для дослідження на тему "Thermal energy demand forecast".
Біля кожної праці в переліку літератури доступна кнопка «Додати до бібліографії». Скористайтеся нею – і ми автоматично оформимо бібліографічне посилання на обрану працю в потрібному вам стилі цитування: APA, MLA, «Гарвард», «Чикаго», «Ванкувер» тощо.
Також ви можете завантажити повний текст наукової публікації у форматі «.pdf» та прочитати онлайн анотацію до роботи, якщо відповідні параметри наявні в метаданих.
Переглядайте статті в журналах для різних дисциплін та оформлюйте правильно вашу бібліографію.
1
Liu, Yujing, Ruoyun Du, and Dongxiao Niu. "Forecast of Coal Demand in Shanxi Province Based on GA—LSSVM under Multiple Scenarios." Energies 15, no. 17 (September 5, 2022): 6475. http://dx.doi.org/10.3390/en15176475.
Повний текст джерелаАнотація:
Under the “carbon peaking and carbon neutrality” goal, Shanxi Province adjusts the power supply structure and promotes the development of a high proportion of new energy, which has a certain impact on the demand for thermal coal. Therefore, constructing a reasonable forecasting model for thermal coal demand can play a role in stabilizing coal supply and demand. This paper analyzes various factors related to coal demand, and uses Pearson coefficient to screen out six variables with strong correlation. Then, based on the scenario analysis method, combined with the “14th Five-Year Plan” of Shanxi Province, different scenarios of economic development and carbon emission reduction development are set. Finally, a multi-scenario GA–LSSVM forecasting model of thermal coal demand in Shanxi Province is constructed, and the future development trend of thermal coal demand in Shanxi Province is predicted. The results show that the demand for thermal coal is the largest in the mode of high-speed economic development and low emission reduction, and the demand for thermal coal is the lowest in the mode of low-speed economic development and strong emission reduction, which provides a scientific basis for the implementation of Shanxi Province’s thermal coal supply policy.
2
Lucas Segarra, Eva, Hu Du, Germán Ramos Ruiz, and Carlos Fernández Bandera. "Methodology for the Quantification of the Impact of Weather Forecasts in Predictive Simulation Models." Energies 12, no. 7 (April 5, 2019): 1309. http://dx.doi.org/10.3390/en12071309.
Повний текст джерелаАнотація:
The use of Building Energy Models (BEM) has become widespread to reduce building energy consumption. Projection of the model in the future to know how different consumption strategies can be evaluated is one of the main applications of BEM. Many energy management optimization strategies can be used and, among others, model predictive control (MPC) has become very popular nowadays. When using models for predicting the future, we have to assume certain errors that come from uncertainty parameters. One of these uncertainties is the weather forecast needed to predict the building behavior in the near future. This paper proposes a methodology for quantifying the impact of the error generated by the weather forecast in the building’s indoor climate conditions and energy demand. The objective is to estimate the error introduced by the weather forecast in the load forecasting to have more precise predicted data. The methodology employed site-specific, near-future forecast weather data obtained through online open access Application Programming Interfaces (APIs). The weather forecast providers supply forecasts up to 10 days ahead of key weather parameters such as outdoor temperature, relative humidity, wind speed and wind direction. This approach uses calibrated EnergyPlus models to foresee the errors in the indoor thermal behavior and energy demand caused by the increasing day-ahead weather forecasts. A case study investigated the impact of using up to 7-day weather forecasts on mean indoor temperature and energy demand predictions in a building located in Pamplona, Spain. The main novel concepts in this paper are: first, the characterization of the weather forecast error for a specific weather data provider and location and its effect in the building’s load prediction. The error is calculated based on recorded hourly data so the results are provided on an hourly basis, avoiding the cancel out effect when a wider period of time is analyzed. The second is the classification and analysis of the data hour-by-hour to provide an estimate error for each hour of the day generating a map of hourly errors. This application becomes necessary when the building takes part in the day-ahead programs such as demand response or flexibility strategies, where the predicted hourly load must be provided to the grid in advance. The methodology developed in this paper can be extrapolated to any weather forecast provider, location or building.
3
Maliarenko, O. Ye, N. Yu Maistrenko, and V. V. Horskyi. "Forecast of fuel and coal consumption in Ukraine until 2040 by a complex method of forecasting energy consumption." Problems of General Energy 2021, no. 3 (September 23, 2021): 28–35. http://dx.doi.org/10.15407/pge2021.03.028.
Повний текст джерелаАнотація:
The article presents a projection of Ukraine economy development up to 2040 according to the baseline scenario, taking into account the changes that have occurred during 2017-2020. Using the projection, a preliminary estimate of the forecasted demand for electricity at the national level (TOP-DOWN method) for 2040 was developed, which taking into account a new national thermal power production structure including structure of coal-fired power plants according to the NPC “Ukrenergo” 2020 Adequacy Report. Based on these data, the forecast for fuel demand in the country including coal for 2040 is developed, which takes into account consolidated economic activities, changes in household sector, the potential of energy savings from structural changes and technological changes. Also, the forecast of fuel and coal use for transformation in industrial technological processes and in power plants are calculated. The study shows that fuel consumption in the country is significantly influenced by two factors: the structure of the economy and the structure of generating capacity for electricity and heat. Reducing the share of fossil fuels in electricity generation leads to almost constant consumption. The structural potential for energy savings is almost 50% of the total. Keywords: forecast, demand, fuel, coal, structure of economy, technological potential of energy saving, method
4
Dargahi, Ali, Khezr Sanjani, Morteza Nazari-Heris, Behnam Mohammadi-Ivatloo, Sajjad Tohidi, and Mousa Marzband. "Scheduling of Air Conditioning and Thermal Energy Storage Systems Considering Demand Response Programs." Sustainability 12, no. 18 (September 7, 2020): 7311. http://dx.doi.org/10.3390/su12187311.
Повний текст джерелаАнотація:
The high penetration rate of renewable energy sources (RESs) in smart energy systems has both threat and opportunity consequences. On the positive side, it is inevitable that RESs are beneficial with respect to conventional energy resources from the environmental aspects. On the negative side, the RESs are a great source of uncertainty, which will make challenges for the system operators to cope with. To tackle the issues of the negative side, there are several methods to deal with intermittent RESs, such as electrical and thermal energy storage systems (TESSs). In fact, pairing RESs to electrical energy storage systems (ESSs) has favorable economic opportunities for the facility owners and power grid operators (PGO), simultaneously. Moreover, the application of demand-side management approaches, such as demand response programs (DRPs) on flexible loads, specifically thermal loads, is an effective solution through the system operation. To this end, in this work, an air conditioning system (A/C system) with a TESS has been studied as a way of volatility compensation of the wind farm forecast-errors (WFFEs). Additionally, the WFFEs are investigated from multiple visions to assist the dispatch of the storage facilities. The operation design is presented for the A/C systems in both day-ahead and real-time operations based on the specifications of WFFEs. Analyzing the output results, the main aims of the work, in terms of applying DRPs and make-up of WFFEs to the scheduling of A/C system and TESS, will be evaluated. The dispatched cooling and base loads show the superiority of the proposed method, which has a smoother curve compared to the original curve. Further, the WFFEs application has proved and demonstrated a way better function than the other uncertainty management techniques by committing and compensating the forecast errors of cooling loads.
5
Guzhov, S. V. "Forecast of demand for the rmal energy for buildings of secondary educational institutions based on the properties of heteromorphism of their energy systems." Power engineering: research, equipment, technology 22, no. 5 (December 24, 2020): 18–27. http://dx.doi.org/10.30724/1998-9903-2020-22-5-18-27.
Повний текст джерелаАнотація:
THE PURPOSE. Improving the accuracy of forecast calculations of demand for energy resources is an urgent task, especially in the light of the Digital Energy of the Russian Federation program. Prediction is also required for he at supply systems. The complexity of the analysis is the lack of confirmation of the similarity properties of energy systems and complexes for buildings with similar functionality. On the example of buildings of secondary educational institutions located i n the territory of Moscow, the assumption of heteromorphism of thermal systems is proved. METHODS. In the work, an assumption was made that there were no significant changes in the data on the heat consumption of the energy facilities of schools, which was confirmed by the absence of changes in the average annual heat consumption and jumps in the monthly heat consumption diagrams. The amount of heat energy consumption measured and transferred to the IS is influenced by a number of additional factors: accura cy drift of heat energy metering devices; aging and overgrowing of the internal surfaces of the building's heating network equipment; physical aging and deterioration of the building envelope and deterioration of their thermal insulation performance. When compiling predicted energy consumption, this means that it is permissible to use not only statistical data about the analyzed object itself, but also about a variety of objects similar to those analyzed in structure and functionality. RESULTS. A set of input factors is proposed that makes it possible to accurately determine the predicted demand for thermal energy for buildings of secondary educational institutions. The possibility and similar accuracy of the results of forecasting the demand for thermal ene rgy is shown both through the use of multivariate regression analysis and artificial neural networks. CONCLUSION. ЭBased on the combined use of various mathematical approaches, it is proposed to use the methodology for forecasting energy demand by energy complexes and systems as a mechanism for determining the correctness of the transmitted meter readings.
6
Yousefi, Hossein, Mohammad Hasan Ghodusinejad, and Armin Ghodrati. "Multi-Criteria Future Energy System Planning and Analysis for Hot Arid Areas of Iran." Energies 15, no. 24 (December 12, 2022): 9405. http://dx.doi.org/10.3390/en15249405.
Повний текст джерелаАнотація:
An increase in energy demand in the coming years is inevitable, and therefore it is necessary to provide optimal solutions for this future need. This paper examines the future energy demands of the southern regions of Iran (with a hot and dry climate and high energy needs). In this regard, the overall structure of the research has been divided into three parts. In the first part, using historical energy consumption data, the energy demand in 2030 is predicted. This is carried out utilizing a time series analysis method, namely Holt–Winters. Then, relying on the plans of the Iran Ministry of Energy, various energy plans have been designed and energy modeling has been carried out for both base and forecast years. Finally, regarding a multi-criteria decision-making approach, energy plans are ranked and the best scenarios are selected and analyzed. The results of modeling and multi-criteria analysis showed that comprehensive and simultaneous development in the construction of thermal and renewable power plants is the best option to meet future energy needs.
7
Niederau, Jan, Johanna Fink, and Moritz Lauster. "Connecting Dynamic Heat Demands of Buildings with Borehole Heat Exchanger Simulations for Realistic Monitoring and Forecast." Advances in Geosciences 56 (October 6, 2021): 45–56. http://dx.doi.org/10.5194/adgeo-56-45-2021.
Повний текст джерелаАнотація:
Abstract. Space heating is a major contributor to the average energy consumption of private households, where the energy standard of a building is a controlling parameter for its heating energy demand. Vertical Ground Source Heat Pumps (vGSHP) present one possibility for a low-emission heating solution. In this paper, we present results of building performance simulations (BPS) coupled with vGSHP simulations for modelling the response of vGSHP-fields to varying heating power demands, i.e. different building types. Based on multi-year outdoor temperature data, our simulation results show that the cooling effect of the vGSHPs in the subsurface is about 2 K lower for retrofitted buildings. Further, a layout with one borehole heat exchanger per building can be efficiently operated over a time frame of 15 years, even if the vGSHP-field layout is parallel to regional groundwater flow in the reservoir body. Due to northward groundwater flow, thermal plumes of reduced temperatures develop at each vGSHP, showing that vGSHPs in the southern part of the model affect their northern neighbors. Considering groundwater flow in designing the layout of the vGSHP-field is conclusively important. Combining realistic estimates of the energy demand of buildings by BPS with subsurface reservoir simulations thus presents a tool for monitoring and managing the temperature field of the subsurface, affected by Borehole Heat Exchanger (BHE) installations.
8
Liu, Dunnan, Mengjiao Zou, Yue Zhang, Lingxiang Wang, Tingting Zhang, and Mingguang Liu. "Market clearing price forecast for power peak shaving auxiliary service." E3S Web of Conferences 237 (2021): 02007. http://dx.doi.org/10.1051/e3sconf/202123702007.
Повний текст джерелаАнотація:
The use of new energy to generate electricity in the power system and the large-scale increase of new energy grid connection has led to increasingly insufficient power system regulation, in order to solve this problem, the peak shaving auxiliary service market came into being.This article comprehensively analyzes the factors those affect the market clearing price of power peak shaving auxiliary services: The macro factors include energy economic policies (renewable energy and electric energy substitution), technological innovation, market operation rules, etc., and the micro factors include the quotation and demand of thermal power plants and wind power generation.The power peak shaving auxiliary service market is an important part of the power market. Its appearance makes the grid operation safer and more reliable, and the reasonable fluctuation of clearing prices and total market costs reflects the market’s sensitivity to peak shaving resource demand.This paper uses the BP neural network model to select 31 consecutive days of peak shaving auxiliary service clearing price data in North China for prediction.
9
Szul, Tomasz, and Stanisław Kokoszka. "Application of Rough Set Theory (RST) to Forecast Energy Consumption in Buildings Undergoing Thermal Modernization." Energies 13, no. 6 (March 11, 2020): 1309. http://dx.doi.org/10.3390/en13061309.
Повний текст джерелаАнотація:
In many regions, the heat used for space heating is a basic item in the energy balance of a building and significantly affects its operating costs. The accuracy of the assessment of heat consumption in an existing building and the determination of the main components of heat loss depends to a large extent on whether the energy efficiency improvement targets set in the thermal upgrading project are achieved. A frequent problem in the case of energy calculations is the lack of complete architectural and construction documentation of the analyzed objects. Therefore, there is a need to search for methods that will be suitable for a quick technical analysis of measures taken to improve energy efficiency in existing buildings. These methods should have satisfactory results in predicting energy consumption where the input is limited, inaccurate, or uncertain. Therefore, the aim of this work was to test the usefulness of a model based on Rough Set Theory (RST) for estimating the thermal energy consumption of buildings undergoing an energy renovation. The research was carried out on a group of 109 thermally improved residential buildings, for which energy performance was based on actual energy consumption before and after thermal modernization. Specific sets of important variables characterizing the examined buildings were distinguished. The groups of variables were used to estimate energy consumption in such a way as to obtain a compromise between the effort of obtaining them and the quality of the forecast. This has allowed the construction of a prediction model that allows the use of a fast, relatively simple procedure to estimate the final energy demand rate for heating buildings.
10
Lucas Segarra, Eva, Germán Ramos Ruiz, and Carlos Fernández Bandera. "Probabilistic Load Forecasting Optimization for Building Energy Models via Day Characterization." Sensors 21, no. 9 (May 10, 2021): 3299. http://dx.doi.org/10.3390/s21093299.
Повний текст джерелаАнотація:
Accurate load forecasting in buildings plays an important role for grid operators, demand response aggregators, building energy managers, owners, customers, etc. Probabilistic load forecasting (PLF) becomes essential to understand and manage the building’s energy-saving potential. This research explains a methodology to optimize the results of a PLF using a daily characterization of the load forecast. The load forecast provided by a calibrated white-box model and a real weather forecast was classified and hierarchically selected to perform a kernel density estimation (KDE) using only similar days from the database characterized quantitatively and qualitatively. A real case study is presented to show the methodology using an office building located in Pamplona, Spain. The building monitoring, both inside—thermal sensors—and outside—weather station—is key when implementing this PLF optimization technique. The results showed that thanks to this daily characterization, it is possible to optimize the accuracy of the probabilistic load forecasting, reaching values close to 100% in some cases. In addition, the methodology explained is scalable and can be used in the initial stages of its implementation, improving the values obtained daily as the database increases with the information of each new day.
11
You, Zhengjie, Michel Zade, Babu Kumaran Nalini, and Peter Tzscheutschler. "Flexibility Estimation of Residential Heat Pumps under Heat Demand Uncertainty." Energies 14, no. 18 (September 10, 2021): 5709. http://dx.doi.org/10.3390/en14185709.
Повний текст джерелаАнотація:
With the increasing penetration of intermittent renewable energy generation, there is a growing demand to use the inherent flexibility within buildings to absorb renewable related disruptions. Heat pumps play a particularly important role, as they account for a high share of electricity consumption in residential units. The most common way of quantifying the flexibility is by considering the response of the building or the household appliances to external penalty signals. However, this approach neither accounts for the use cases of flexibility trading nor considers its impact on the prosumer comfort, when the heat pump should cover the stochastic domestic hot water (DHW) consumption. Therefore, in this paper, a new approach to quantifying the flexibility potential of residential heat pumps is proposed. This methodology enables the prosumers themselves to generate and submit the operating plan of the heat pump to the system operator and trade the alternative operating plans of the heat pump on the flexibility market. In addition, the impact of the flexibility provision on the prosumer comfort is investigated by calculating the warm water temperature drops in the thermal energy storage given heat demand forecast errors. The results show that the approach with constant capacity reservation in the thermal energy storage provides the best solution, with an average of 2.5 min unsatisfactory time per day and a maximum temperature drop of 2.3 °C.
12
Lu, Xiaojuan, and Leilei Cheng. "Day-Ahead Scheduling for Renewable Energy Generation Systems considering Concentrating Solar Power Plants." Mathematical Problems in Engineering 2021 (August 23, 2021): 1–14. http://dx.doi.org/10.1155/2021/9488222.
Повний текст джерелаАнотація:
With the advent of the new types of electrical systems that attach more importance to the renewability of the energy resource, issues arising out of the randomness and volatility of the renewable energy resource, such as the safety, reliability, and economic operation of the underlying power generation system, are expected to be challenging. Generally speaking, the power generation company can do a reasonable dispatch of each unit according to weather forecast and load demand information. Focusing on concentrating solar power (CSP) plants (wind power, photovoltaic, battery energy storage, and thermal power plants), this paper proposes a day-ahead scheduling model for renewable energy generation systems. The model also considers demand response and related generator set constraints. The problem is described as a mixed-integer nonlinear programming (MINLP) problem, which can be solved by the CPLEX solver to obtain an optimal solution. At the same time, the paper compares and analyzes the impact of concentrating solar power plants on other renewable energy generation and thermal power operation systems. The results show that the renewable energy generation system can lower power generation costs, reduce load fluctuation, and enhance the energy storage rate.
13
Borowski, Marek, and Klaudia Zwolińska. "Prediction of Cooling Energy Consumption Using a Neural Network on the Example of the Hotel Building." Proceedings 58, no. 1 (September 11, 2020): 21. http://dx.doi.org/10.3390/wef-06917.
Повний текст джерелаАнотація:
The purpose of this work is to determine internal and external factors affecting the cooling energy demand of a building. During the research, the impact of weather conditions and the level of hotel occupancy on cooling energy, which is necessary to obtain indoor comfort conditions, was analyzed. The subject of research is energy consumption in the Turówka hotel located in Wieliczka (southern Poland). In the article, the designer of neural networks was used in the Statistica statistical package. To design the network, a widely used multilayer perceptron model with an algorithm with backward error propagation was used. Based on the collected input and output data, various multilayer perceptron (MLP) networks were tested to determine the relationship most accurately reflecting actual energy consumption. Based on the results obtained, factors that significantly affect the consumption of thermal energy in the building were determined, and a predictive energy demand model for the analyzed object was presented. The result of the work is a forecast of cooling energy demand, which is particularly important in a hotel facility. The prepared predictive model will enable proper energy management in the facility, which will lead to reduced consumption and thus costs related to facility operation.
14
Cox, Rick, Shalika Walker, Joep van der Velden, Phuong Nguyen, and Wim Zeiler. "Flattening the Electricity Demand Profile of Office Buildings for Future-Proof Smart Grids." Energies 13, no. 9 (May 8, 2020): 2357. http://dx.doi.org/10.3390/en13092357.
Повний текст джерелаАнотація:
The built environment has the potential to contribute to maintaining a reliable grid at the demand side by offering flexibility services to a future Smart Grid. In this study, an office building is used to demonstrate forecast-driven building energy flexibility by operating a Battery Electric Storage System (BESS). The objective of this study is, therefore, to stabilize/flatten a building energy demand profile with the operation of a BESS. First, electricity demand forecasting models are developed and assessed for each individual load group of the building based on their characteristics. For each load group, the prediction models show Coefficient of Variation of the Root Mean Square Error (CVRMSE) values below 30%, which indicates that the prediction models are suitable for use in engineering applications. An operational strategy is developed aiming at meeting the flattened electricity load shape objective. Both the simulation and experimental results show that the flattened load shape objective can be met more than 95% of the time for the evaluation period without compromising the thermal comfort of users. Accurate energy demand forecasting is shown to be pivotal for meeting load shape objectives.
15
Devine, K. "Gas in Electricity Generation." Energy Exploration & Exploitation 13, no. 2-3 (May 1995): 149–57. http://dx.doi.org/10.1177/0144598795013002-305.
Повний текст джерелаАнотація:
Gas is New Zealand's major thermal fuel for electricity generation. This paper describes what influences the volumes of gas burnt by ECNZ, and forecasts future gas demands for electricity generation. It also reviews the uncertainties associated with these forecasts and likely competition in building new electricity generating stations and outlines the strategy now being formulated to accommodate them. Because ECNZ's generation system is hydro-based, relatively small rapid changes in hydrological conditions can significantly affect the amount of gas used. This situation will change over time with major increases in thermal generation likely to be needed over the next 20 years. However, there are considerable uncertainties on gas supply and electricity demand levels in the long run, which will complicate investment and fuel decisions.
16
Guzhov, S. V. "About combining determinated and stochastic approaches for prediction of the heating balance of the building for water sports." Power engineering: research, equipment, technology 22, no. 1 (April 30, 2020): 103–12. http://dx.doi.org/10.30724/1998-9903-2020-22-1-103-112.
Повний текст джерелаАнотація:
Forecasting the demand for thermal energy by energy complexes of buildings and structures is an urgent task. To achieve the necessary accuracy of the calculation, it is customary to use various deterministic methods based on the available changing and slightly changing data about the object of study. At the same time, statistical data can also be used in analysis by stochastic methods. The purpose of this article is to analyze the question of the admissibility of combining deterministic and stochastic approaches in order to increase the accuracy of the calculation. Formulas for calculating the components of the expenditure part of the heat balance are shown on the example of a building for water sports. Based on the above formulas, a calculation with a monthly discretization in the period from January 2009 is carried out. until January 2019. An example is given of calculating the accuracy of the forecast of demand for thermal energy through multivariate regression analysis and the use of artificial neural networks. Based on the same data, an artificial neural network was trained on seven different factors: six independent and seventh — the idealized value of the building’s heat loss through the building envelope. An example of the analysis of a building for practicing water sports shows the inadmissibility of the described approach if the same initial data are used in the deterministic and stochastic method. Results: the accuracy of the forecast made using regression analysis increases with an increase in the number of factors. However, the use of an additional group of factors in the stochastic method, for example, which are numerically processed climate data that are already used as initial data, will lead to an unreasonable overestimation of the significance of the twice used factor. The presence in the predictive models using artificial neural networks of collinearity and multicollinearity of variables does not negatively affect the forecast. Conclusion: the combination of the deterministic and stochastic approaches in preparing the predicted heat balance by using only the same input data that is used in the stochastic approach in the deterministic approach is unacceptable.
17
Chidiac, Samir E., Lan Yao, and Paris Liu. "Climate Change Effects on Heating and Cooling Demands of Buildings in Canada." CivilEng 3, no. 2 (April 2, 2022): 277–95. http://dx.doi.org/10.3390/civileng3020017.
Повний текст джерелаАнотація:
Climate change is causing more frequent extreme weather events. The consequences of increasing global temperature on the operating cost of existing buildings, and the associated health, safety, and economic risks were investigated. Eight cities in Ontario, Canada, across climate zones 5 to 8, were selected for this study. Statistical models were employed to forecast daily temperatures for 50 years. The impact of climate change on buildings’ heating and cooling demands for energy was measured as changes in heating degree days (HDD) and cooling degree days (CDD) compared to current design requirements. The results predict an increase in the demand for cooling and a decrease in that for heating within the next 50 years. A drop in the total HDD and CDD is shown which reflects a more comfortable outdoor thermal condition. Risk to human health attributable to the increase in global temperature is negligible.
18
Derii, V. O. "Trends in the development of the district heating systems of Ukraine." Problems of General Energy 2021, no. 1 (March 24, 2021): 52–59. http://dx.doi.org/10.15407/pge2021.01.052.
Повний текст джерелаАнотація:
We considered trends in the development of district heating systems (DHS) in Europe and Ukraine. It was established that DHS are widely used and make a significant contribution to the heat supply of European countries. In the European Union as a whole, the share of DHS is 13%, and there are plans to increase it to 50% in 2050 with a wide use of cogeneration and renewable sources of energy, including environmental energy with using heat pumps. Ukraine is one of the countries with a high level of DHS, but, at present, there are negative trends to reducing their contribution to the total heat supply for heating and hot water supply – from 65.2% in 2014 to 52% in 2017. In several cities, DHS ceased to function at all. The main equipment of the DHS of Ukraine is physically worn out and technologically obsolete and needs to be renewed by means of wide reconstruction, modernization, and technological re-equipment. We determined factors and the level of their influence on the demand in thermal energy of DHS. It was established that the factors reducing demand have a much greater potential. We created forecasts of demand for thermal energy, fuel balance, and the structure of DHS generation by 2050. It is shown that the demand for thermal energy from DHS will decrease and reach about 35 million Gcal in 2050. To ensure the low-carbon development of Ukraine in the structure of thermal energy generation in DHS, the use of coal-fired CHPs and boilers, as well as boilers on petroleum products will be significantly reduced. The share of natural gas in the fuel balance of DHS of Ukraine will also decrease, but it will be the main fuel for the period of technological transformation of generating capacities under conditions of the low-carbon development of Ukraine. The use of technologies for the production of thermal energy from biomass, waste, environment, and electricity will gradually increase, and in 2050, using these sources will produce about 23.8 million Gcal, which is more than 60% of the total thermal energy of DHS. Keywords: district heating systems, thermal energy, factors of influence, demand, fuel balance, generation structure
19
Al-Addous, Mohammad, and Aiman Albatayneh. "Knowledge gap with the existing building energy assessment systems." Energy Exploration & Exploitation 38, no. 3 (November 10, 2019): 783–94. http://dx.doi.org/10.1177/0144598719888100.
Повний текст джерелаАнотація:
Energy supply, the increasing demands for energy, climate change, and the imperative to reduce greenhouse gas emissions must be considered in designing buildings. In order to design energy-efficient buildings, there should be accurate information about the thermal performance of the building. The thermal simulation readings should be precise. Its precision will also have a definite indication of the operational energy costs enabling the likelihood of conserving more energy used in building operations and reducing the greenhouse effect that is a result of emissions of greenhouse gases. Energy-efficient buildings are vital as they reduce the consumption of energy in and allow sustainable development. Erecting such buildings will require correct and realistic prediction of the buildings performance when subjected to a wide variety of harsh weather conditions in order to have a view of the impact of all the physical elements that influence the thermal performance. The behavior of the occupants also influences the thermal performance of a building. To achieve this, energy assessment instruments are used to accurately forecast the buildings thermal performance. This paper critically reviews energy rating methods for housing and the limitations of assessment systems.
20
Mugnini, Alice, Gianluca Coccia, Fabio Polonara, and Alessia Arteconi. "Performance Assessment of Data-Driven and Physical-Based Models to Predict Building Energy Demand in Model Predictive Controls." Energies 13, no. 12 (June 16, 2020): 3125. http://dx.doi.org/10.3390/en13123125.
Повний текст джерелаАнотація:
The implementation of model predictive controls (MPCs) in buildings represents an important opportunity to reduce energy consumption and to apply demand side management strategies. In order to be effective, the MPC should be provided with an accurate model that is able to forecast the actual building energy demand. To this aim, in this paper, a data-driven model realized with an artificial neural network is compared to a physical-based resistance–capacitance (RC) network in an operative MPC. The MPC was designed to minimize the total cost for the thermal demand requirements by unlocking the energy flexibility in the building envelope, on the basis of price signals. Although both models allow energy cost savings (about 16% compared to a standard set-point control), a deterioration in the prediction performance is observed when the models actually operate in the controller (the root mean square error, RMSE, for the air zone prediction is about 1 °C). However, a difference in the on-time control actions is noted when the two models are compared. With a maximum deviation of 0.5 °C from the indoor set-point temperature, the physical-based model shows better performance in following the system dynamics, while the value rises to 1.8 °C in presence of the data-driven model for the analyzed case study. This result is mainly related to difficulties in properly training data-driven models for applications involving energy flexibility exploitation.
21
Zhang, Zhenrui. "Comprehensive Research on Application and Optimization of Heat Storage Technology Under Different Industrial Demand: Based on Medium and Low Temperature." Progress in Energy & Fuels 9, no. 2 (September 28, 2020): 27. http://dx.doi.org/10.18282/pef.v9i2.1093.
Повний текст джерелаАнотація:
<p>Heat storage technology is one of the key technologies in the field of solar thermal power generation and cogeneration. It uses heat storage materials as the media to store solar thermal energy, industrial waste heat, low-grade waste heat and other kinds of thermal energy, and release it when needed, so as to solve the mismatch between energy supply and demand. This paper introduces the classification and characteristics of heat storage technology, analyzes the research progress of heat storage technology in the fields of solar thermal power generation, heat storage materials and industrial drying, and forecasts the development trend of heat storage technology in the future. The results indicate that heat storage technology is beneficial to improve industrial production efficiency, broaden industrial energy conservation and emission reduction ideas, and has a great application prospect under the continuous improvement of practitioners with innovative spirit.</p>
22
Wang, Bo, Limao Wang, Shuai Zhong, Ning Xiang, and Qiushi Qu. "Low-Carbon Transformation of Electric System against Power Shortage in China: Policy Optimization." Energies 15, no. 4 (February 21, 2022): 1574. http://dx.doi.org/10.3390/en15041574.
Повний текст джерелаАнотація:
The low-carbon transition of the power system is essential for China to achieve peak carbon and carbon neutrality. However, China could suffer power shortages due to radical policies in some extreme cases. The gap between power demand and supply from March 2021 to November 2021 ranged between 5.2 billion kW·h and 24.6 billion kW·h. The main reason for the power shortage was over-reliance on renewable energy and insufficient coal power supply for the power system. The low-carbon transformation path of the electric system needs to be explored with more flexibility for power security. This study applied a modified LEAP model and carried out a forecast analysis of thermal power generation and installed capacity in 2025 and 2030 under normal and extreme weather scenarios. The results suggested that: the installed capacity of thermal power will need to account for about 44.6–46.1% of power generation in 2025 and 37.4–39.3% in 2030, with the assumption of power shortages caused by the instability and uncertainty of renewable power. In the future, China needs to pursue the development of diversified energy sources and enhance the power supply security capability while strengthening the development and utilization of renewable energy.
23
Rusovs, D., L. Jakovleva, V. Zentins, and K. Baltputnis. "Heat Load Numerical Prediction for District Heating System Operational Control." Latvian Journal of Physics and Technical Sciences 58, no. 3 (June 1, 2021): 121–36. http://dx.doi.org/10.2478/lpts-2021-0021.
Повний текст джерелаАнотація:
Abstract To develop an advanced control of thermal energy supply for domestic heating, a number of new challenges need to be solved, such as the emerging need to plan operation in accordance with an energy market-based environment. However, to move towards this goal, it is necessary to develop forecasting tools for short- and long-term planning, taking into account data about the operation of existing heating systems. The paper considers the real operational parameters of five different heating networks in Latvia over a period of five years. The application of regression analysis for heating load dependency on ambient temperature results in the formulation of normalized slope for the regression curves of the studied systems. The value of this parameter, the normalized slope, allows describing the performance of particular heating systems. Moreover, a heat load forecasting approach is presented by an application of multiple regression methods. This short-term (day-ahead) forecasting tool is tested on data from a relatively small district heating system with an average load of 20 MW at ambient temperature of 0 °C. The deviations of the actual heat load demand from the one forecasted with various training data set sizes and polynomial orders are evaluated for two testing periods in January of 2018. Forecast accuracy is assessed by two parameters – mean absolute percentage error and normalized mean bias error.
24
Aryanto, Nova, Ahmad Jaya, and Chairul Hudaya. "PEMODELAN ENERGI BARU TERBARUKAN (EBT) MELALUI PENDEKATAN DINAMIS UNTUK KETAHANAN ENERGI KABUPATEN SUMBAWA 2017-2027." Jurnal TAMBORA 4, no. 2A (July 30, 2020): 122–32. http://dx.doi.org/10.36761/jt.v4i2a.783.
Повний текст джерелаАнотація:
In an effort to increase the value of the Electrification Ratio value reaches 99.9% andUtilization of New and Renewable Energy (EBT) of up to 25% by 2025 is requiredThe General Plan for National Energy (RUEN) which is revealed to be the General DraftRegional Energy (RUED). Sumbawa as an area in West Nusa Tenggarahas the potential for EBT in the form of Solar Energy Potential, Hydro Energy, and Thermal EnergyEarth and Sea Energy require strategic policies to manage andmeet the energy security of the region. This study aims to predictEnergy needs, and mapping the potential of EBT, in order to obtain a mixenergy (energy mix) is balanced. This research was conducted using toolssoftware Long-range Energy Alternatives Planning System (LEAP) withdynamic systems approach. Data obtained from PT. PLN UP3 Sumbawa, RUPTL DataPLN NTB Region, Bapedda Kab. Sumbawa and Data from BPS Kab. Sumbawa. ResultThis research shows that the potential of EBT can be integrated in RUED formeet the energy needs of the region. Therefore, this research canproduce accurate energy demand forecast for Sumbawa Regencyin particular the use of regional green energy sources (Green Energy) to achieve thisenergy security for the great and dignified Sumbawa Regencyencouraging the formation of RUED Sumbawa Regency in line with the Indicator StrategySDGs program launched by the Government, both the Central Government andLocal Government especially the Clean Energy (Green Energy) program.
25
Kuts, G. O., O. Ye Malyarenko, N. Yu Maistrenko, and V. V. Stanytsina. "Determination of the forecasted demand for thermal energy by a complex method taking into account the potential of energy saving." Problems of General Energy 2018, no. 3 (September 25, 2018): 10–15. http://dx.doi.org/10.15407/pge2018.03.010.
Повний текст джерела
26
Miranda, Mafalda M., Jasmin Raymond, and Chrystel Dezayes. "Uncertainty and Risk Evaluation of Deep Geothermal Energy Source for Heat Production and Electricity Generation in Remote Northern Regions." Energies 13, no. 16 (August 14, 2020): 4221. http://dx.doi.org/10.3390/en13164221.
Повний текст джерелаАнотація:
The Canadian off-grid communities heavily rely on fossil fuels. This unsustainable energetic framework needs to change, and deep geothermal energy can play an important role. However, limited data availability is one of the challenges to face when evaluating such resources in remote areas. Thus, a first-order assessment of the geothermal energy source is, therefore, needed to trigger interest for further development in northern communities. This is the scope of the present work. Shallow subsurface data and outcrop samples treated as subsurface analogs were used to infer the deep geothermal potential beneath the community of Kuujjuaq (Nunavik, Canada). 2D heat conduction models with time-varying upper boundary condition reproducing climate events were used to simulate the subsurface temperature distribution. The available thermal energy was inferred with the volume method. Monte Carlo-based sensitivity analyses were carried out to determine the main geological and technical uncertainties on the deep geothermal potential and risk analysis to forecast future energy production. The results obtained, although speculative, suggest that the old Canadian Shield beneath Kuujjuaq host potential to fulfill the community’s annual average heating demand of 37 GWh. Hence, deep geothermal energy can be a promising solution to support the energy transition of remote northern communities.
27
Xu, Min, Yan Cui, Tao Wang, Yaozhong Zhang, Yan Guo, and Xiaoying Zhang. "Optimal Dispatch of Wind Power, Photovoltaic Power, Concentrating Solar Power, and Thermal Power in Case of Uncertain Output." Energies 15, no. 21 (November 3, 2022): 8215. http://dx.doi.org/10.3390/en15218215.
Повний текст джерелаАнотація:
The integration of large-scale wind and photovoltaic power into modern power grids leads to an imbalance between the supply and demand for resources of the system, where this threatens the safety and stable operation of the grid. The traditional mode of grid dispatch and the capability of regulation of conventional thermal power units cannot satisfy the demands of grid connection for large-scale renewable energy, where the system requires the compensation and coordinated dispatch of flexible power sources. In light of this problem, this paper establishes a model to quantify the uncertainty in the forecasted outputs of wind and photovoltaic power. This is used to develop forecasts of the output of wind and photovoltaic power for several groups of scenarios, and predictions with the best complementarity are selected as a typical set of scenarios by means of their generation, reduction, and combination. By taking full advantage of the complementarity in the rates of regulation of conventional thermal power and concentrating solar power (CSP), a coordinated model of dispatch for wind power, photovoltaic power, CSP, and thermal power is established for a number of typical combinations of scenarios. The influence of uncertainty in the outputs of wind and photovoltaic power on the dispatch of the power grid is examined, and different modes of dispatch are formulated through simulations to analyze the superiority of the dispatch strategy proposed in this paper in terms of abandoned wind quantity, abandoned solar quantity, and the cost of dispatch.
28
Szul, Tomasz, Krzysztof Nęcka, and Thomas G. Mathia. "Neural Methods Comparison for Prediction of Heating Energy Based on Few Hundreds Enhanced Buildings in Four Season’s Climate." Energies 13, no. 20 (October 19, 2020): 5453. http://dx.doi.org/10.3390/en13205453.
Повний текст джерелаАнотація:
Sustainable development and the increasing demand for equitable energy use as well as the reduction of waste of energy are the author’s social and scientific motivations. This new paradigm is the selection of a pertinent methodology to evaluate the efficiency of habitat thermomodernization, which is one of the scientific tasks of the presented study. In order to meet the social and scientific requirements, 380 buildings from the end of the last century (made of large plate technology), which were thermally improved at the beginning of the XXI century, were designed for a comparative analysis of the predictive modelling of heating energy consumption. A specific set of important variables characterizing the examined buildings has been identified. Groups of variables were used to estimate the energy consumption in such a way as to achieve a compromise between the difficulty of obtaining them and the quality of forecast. To predict energy consumption, the six most appropriate neural methods were used: artificial neural networks (ANN), general regression trees (CART), exhaustive regression trees (CHAID), support regression trees (SRT), support vectors (SV), and method multivariant adaptive regression splines (MARS). The quality assessment of the developed models used the mean absolute percentage error (MAPE) also known as mean absolute percentage deviation (MAPD), as well as mean bias error (MBE), coefficient of variance of the root mean square error (CV RMSE) and coefficient of determination (R2), which are accepted as statistical calibration standards by (American Society of Heating, Refrigerating and Air-Conditioning Engineers) ASHRAE. On this basis, the most effective method has been chosen, which gives the best results and therefore allows to forecast with great precision the energy consumption (after thermal improvement) for this type of residential building.
29
Anutoiu, Sorina, Ion Dosa, and Dan Codrut Petrilean. "Steam turbine efficiency assessment, first step towards sustainable electricity production." MATEC Web of Conferences 342 (2021): 04007. http://dx.doi.org/10.1051/matecconf/202134204007.
Повний текст джерелаАнотація:
The main objective of actual energy policies around the world is the transition to renewable energy. EIA forecasts nearly 50% increase in world energy usage by 2050, which is hard to achieve using only renewable energy. For year 2019 the electricity production in EU relies mainly on conventional thermal (42.8 %) and nuclear energy sources (26.7%). The accelerated transition to electrical cars puts more pressure on energy producers. As a result, in order to match the ever-growing demand of electrical energy, the conventional thermal energy generation will play a key role, among them coal-based production. In order to meet the environmental goals and for sustainable production of electrical power, energy assessment of power production of coal-based power plants must be performed. The purpose of this paper is to perform an energy assessment of the electrical power production, focusing on a key component of this, the steam turbine. The performance characteristics of the turbine in condensing operation were determined. A proper efficiency of the turbine will have a significant impact on sustainable production of electricity.
30
Makarov, V. M. "Assessment of the mining potential of the public sector of the coal industry of Ukraine." Problems of General Energy 2021, no. 4 (December 22, 2021): 21–29. http://dx.doi.org/10.15407/pge2021.04.021.
Повний текст джерелаАнотація:
The projection of the hot water thermal load of the district heating system’s consumers is developed. It is shown that the total heat load of centralized hot water supply systems in Ukraine today is about 3.0 GW. It determines the full potential of maneuvering power of electric heat generators to provide ancillary services to a power system. Moreover, due to the decline of the Ukrainian population and the decrease of demand for thermal energy, it is expected to decline in the future and will reach 1.9 GW in 2050 (down 36.6% compared to 2020). During the non-heating period, under market conditions, it is expected that heat-generating technologies will compete with each other for the ability to supply heat water to the district systems. The solar collectors will be excluded from the market competition as they do not require a fuel, and therefore their use during the non-heating period is the most profitable. Another technology that will be in use is biomass boilers, their minimum reduced weighted average lossless price of thermal energy (Marginal Levelized Price of Energy - MLPOE) is 102 UAH / Gcal. Gas cogeneration technologies also have a great chance to use their thermal capacity (MLPOE - 258 UAH / Gcal), heat pumps (MLPOE - 155 UAH / Gcal), electric boilers (MLPOE - 633 UAH / Gcal) and gas boilers (MLPOE - 964 UAH / Gcal) will also be used. The analysis of different options for providing ancillary services to the power system showed that considering the competition among technologies, the most feasible option is to involve CHP equipped with electric heat generators. This option allows performing both daily regulation of power and load of power system and also regulation during the system’s night minimum load. At the same time, the balancing power for the current situation is about 1.3 GW for daily control and 1.4 GW for regulation during the night minimum load Keywords: coal industry, production technologies, modernization, forecast, development
31
Farhat, Mohamed, Salah Kamel, Ahmed M. Atallah, Mohamed H. Hassan, and Ahmed M. Agwa. "ESMA-OPF: Enhanced Slime Mould Algorithm for Solving Optimal Power Flow Problem." Sustainability 14, no. 4 (February 17, 2022): 2305. http://dx.doi.org/10.3390/su14042305.
Повний текст джерелаАнотація:
In this work, an enhanced slime mould algorithm (ESMA) based on neighborhood dimension learning (NDL) search strategy is proposed for solving the optimal power flow (OPF) problem. Before using the proposed ESMA for solving the OPF problem, its validity is verified by an experiment using 23 benchmark functions and compared with the original SMA, and three other recent optimization algorithms. Consequently, the ESMA is used to solve a modified power flow model including both conventional energy, represented by thermal power generators (TPGs), and renewable energy represented by wind power generators (WPGs) and solar photovoltaic generators (SPGs). Despite the important role of WPGs and SPGs in reducing CO2 emissions, they represent a big challenge for the OPF problem due to their intermittent output powers. To forecast the intermittent output powers from SPGs and WPGs, Lognormal and Weibull probability density functions (PDFs) are used, respectively. The objective function of the OPF has two extra costs, penalty cost and reserve cost. The penalty cost is added to formulate the underestimation of the produced power from the WPGs and SPGs, while the reserve cost is added to formulate the case of overestimation. Moreover, to decrease CO2 emissions from TPGs, a direct carbon tax is added to the objective function in some cases. The uncertainty of load demand represents also another challenge for the OPF that must be taken into consideration while solving it. In this study, the uncertainty of load demand is represented by the normal PDF. Simulation results of ESMA for solving the OPF are compared with the results of the conventional SMA and two further optimization methods. The simulation results obtained in this research show that ESMA is more effective in finding the optimal solution of the OPF problem with regard to minimizing the total power cost and the convergence of solution.
32
Sztekler, Karol, Wojciech Kalawa, Lukasz Mika, Jaroslaw Krzywanski, Karolina Grabowska, Marcin Sosnowski, Wojciech Nowak, Tomasz Siwek, and Artur Bieniek. "Modeling of a Combined Cycle Gas Turbine Integrated with an Adsorption Chiller." Energies 13, no. 3 (January 21, 2020): 515. http://dx.doi.org/10.3390/en13030515.
Повний текст джерелаАнотація:
Forecasts to 2030 indicate that demand for electricity will increase from 2% to 3% per year, and due to the observed high rate of development of the world economy, energy demand will continue to increase. More efficient use of primary energy has influence on reduction emissions and consumption of fuel. Besides, reducing the amount of fuel burned, it reveals a beneficial effect on the environment. Since extraction-back pressure turbines have some limitations, including the restriction of electricity production due to limited heat consumption in summer. The paper discusses the possibilities of integrating the adsorption aggregate with a combined cycle gas turbine and its impact on the operation of all devices. Simulations are performed on Sim tech IPSEPro software. The obtained results confirm that the adsorption aggregate, using a low grade of thermal energy, does not affect the operation of the gas and steam cycle and allows the production of electricity at a constant level. The calculated chemical fuel energy utilisation factor was 85.7% in cogeneration and 75.6% in trigeneration. These factors indicated a reduced utilisation of chemical fuel energy; however, this reduction is caused by a lower COP for adsorption chillers. Besides, the adsorption aggregate additionally generates chilled water for air conditioning or other technological processes, which stands for an added value of the innovative concept proposed in the paper.
33
Nie, Wanshu, Benjamin F. Zaitchik, Guangheng Ni, and Ting Sun. "Impacts of Anthropogenic Heat on Summertime Rainfall in Beijing." Journal of Hydrometeorology 18, no. 3 (February 23, 2017): 693–712. http://dx.doi.org/10.1175/jhm-d-16-0173.1.
Повний текст джерелаАнотація:
Abstract Anthropogenic heat is an important component of the urban energy budgets that can affect land surface and atmospheric boundary layer processes. Representation of anthropogenic heat in numerical climate modeling systems is therefore important when simulating urban meteorology and climate and has the potential to improve weather forecasts, climate process studies, and energy demand analysis. Here, spatiotemporally dynamic anthropogenic heat data estimated by the Building Effects Parameterization and Building Energy Model (BEP-BEM) are incorporated into the Weather Research and Forecasting (WRF) Model system to investigate its impact on simulation of summertime rainfall in Beijing, China. Simulations of four local rainfall events with and without anthropogenic heat indicate that anthropogenic heat leads to increased rainfall over the urban area. For all four events, anthropogenic heat emission increases sensible heat flux, enhances mixing and turbulent energy transport, lifts PBL height, increases dry static energy, and destabilizes the atmosphere in urban areas through thermal perturbation and strong upward motion during the prestorm period, resulting in enhanced convergence during the major rainfall period. Intensified rainfall leads to greater atmospheric dry-down during the storm and a higher poststorm LCL.
34
Sekret, Robert. "Environmental aspects of energy supply of buildings in Poland." E3S Web of Conferences 49 (2018): 00097. http://dx.doi.org/10.1051/e3sconf/20184900097.
Повний текст джерелаАнотація:
The article presents the assessment of the environmental impact of 9 variants of building heat supply for heating purposes. The building energy standards and the main primary energy carriers being in use in Poland were taken as input data. The subject of analysis was a single-family house characterized by a utility energy demand of 47 kWh/(m2 year). An environmental impact analysis was made using the specification for LCA in damage categories encompassing human health, ecosystem quality and natural resources depletion. From the obtained results it has been found that coal-based technologies in Poland's building energy supply systems are capable of reducing the noxious environmental impact. An example of such a system is the effective heat distribution network with a coal-based cogeneration energy source. From the point of view of radical low emission reduction, an interesting solution is the effective heat distribution network with a gas-based cogeneration energy source. Nevertheless, forecasts about the development of renewable energy source installations in Poland indicate that a significant role in building heat supply systems will be played by solar systems and geothermal systems using heat pumps. Achieving the environmental acceptance of heat pumps in Poland's conditions requires a decisive intensification of efforts to increase the share of renewable energy sources in electric energy generation processes in the central electric power system and in local and individual systems, as well as the continuation of the processes of thermal insulation of already existing buildings.
35
Chintada, Shoba, Siva Prasad Dora, and Dorathi Kare. "An overview on the microstructure and mechanical properties of sintered aluminum-based composites." Metallurgical and Materials Engineering 28, no. 1 (March 31, 2022): 1–16. http://dx.doi.org/10.30544/687.
Повний текст джерелаАнотація:
Sintered composites have revolutionized as a thermal treatment to consolidate a wide range of engineering materials where the transition of powders takes place thermally in a thermodynamical equilibrium state with a decrease in free surface energy in materials owing to their specific capability. Sintering aids in providing effective bonding between the reinforced powder particles. However, the inadequate understanding of the sintering mechanism may limit the practical application of a few materials such as aluminum metal matrix composites. In addition to the rapid growth of various sintering related technologies, researchers need attention to highlight the structural barriers and forecast the emerging demands while dealing with such composites. A review report is made in this paper regarding the sintering mechanisms and sintering techniques. Common sintering techniques such as traditional, microwave assisted, hot pressing, hot isostatic sintering, and spark plasma sintering are identified and discussed here. As a result, the key challenges in sintering aluminium metal matrix composites that can affect sintering parameters are investigated. From the review, spark plasma is identified to attain densified and pore-free green composites and, microwave sintering is the best technique for achieving uniform microstructure in powder metallurgy samples.
36
Ha, Duong Minh. "A study on financial mechanisms to develop the power system in Vietnam." Petrovietnam Journal 10 (November 1, 2022): 59–69. http://dx.doi.org/10.47800/pvj.2022.10-08.
Повний текст джерелаАнотація:
Vietnam’s commercial electricity demand grew by 9.6% per year during 2011 - 2020. The Ministry of Industry and Trade (MOIT) forecasts that the average annual investment cost for the power system over 2021 - 2030 will be around USD 9.0 billion to USD 12.6 billion per year for generation sources and USD 1.5 billion to USD 1.6 billion for the grid. This article discusses the financial options to mobilise this capital. The private sector interest in financing new thermal power projects is low for coal and uncertain for gas; the current energy price crisis suggests deferring any new LNG power plant openings until after 2026. There, the state-owned sector takes the lead. For renewable energy, private investors have shown eagerness to finance new solar and onshore/nearshore wind projects under the feed-in-tariff regime. The subsequent mechanisms will be market-based: auctions and direct power purchase agreements. Offshore wind projects allow the state-owned oil and gas industry to invest jointly with international private developers and reorient its strategy in response to the energy transition. Developing the green bond market is an opportunity for Vietnamese banks. State-owned enterprises can use them to raise money through non-sovereign debt. Finally, a gradual increase in electricity prices will improve the sector’s ability to finance the necessary power system expansion.
37
Batukhtin, Sergey, Andrey Batukhtin, and Marina Baranovskaya. "Water-air regenerative heat exchanger with increased heat exchange efficiency." E3S Web of Conferences 295 (2021): 04005. http://dx.doi.org/10.1051/e3sconf/202129504005.
Повний текст джерелаАнотація:
According to experts’ forecasts, by 2040 the global demand for energy will increase by 37%, and renewable energy sources in the next 20 years will become the fastest growing segment of the world energy, their share in the next decade will grow by about one and a half times. Solar energy is the fastest growing industry among all non-conventional energy sources and is gaining the highest rates of development in comparison with other renewable energy sources. In this article, the authors provide an overview of the technologies that increase the efficiency and productivity of solar panels, only the investigated methods are described that can speed up the process of introducing solar energy instead of traditional. All the methods described can increase the efficiency of systems that are based on the use of the sun as the main source of energy. The authors presented and described the scheme of a solar-air thermal power plant, which will improve energy efficiency through the use of a regenerative air solar collector with increased heat transfer efficiency. Strengthening will be achieved through the use of hemispherical depressions on the surface that receives solar radiation. A schematic diagram is given and the principle of operation of such a solar collector is described in detail. A comparative calculation of the intensification of the solar collector with the use of depressions and without the use as modernization was carried out, on the basis of which a conclusion was made about the efficiency of using this type of solar collector and the economic effect from the application of this method. A description of the method for calculating the solar collector is given, thanks to which this development can be used and implemented in existing heating and hot water supply systems.
38
Gryshchenko, Ivan M., Liudmyla M. Hanushchak-Yefimenko, Valeriia G. Scherbak та Оleksii Yu Volianyk. "МАТЕМАТИЧНЕ МОДЕЛЮВАННЯ ПІДВИЩЕННЯ ЕНЕРГОЕФЕКТИВНОСТІ УНІВЕРСИТЕТУ В СИСТЕМІ ЕНЕРГОХАБА ЗНАНЬ". Journal of Strategic Economic Research, № 5 (10 січня 2022): 34–43. http://dx.doi.org/10.30857/2786-5398.2021.5.4.
Повний текст джерелаАнотація:
This study attempts to address the issues of enhancing energy efficiency using mathematical modeling methods. The research findings assert that energy saving is a new challenging task of the 21st century, since thermal and electric power consumption is essential to human life and building a favourable living environment. It is observed that boosting the competitiveness, financial stability, energy and environmental security of Ukraine’s economy, as well as improving the living standards and the life quality seem hardly possible without realizing the energy saving potential and increasing energy efficiency through modernization, technological advancements and the transition towards rational and environmentally responsible utilization of energy resources. It is argued that by resolving the above objectives, Ukraine might strengthen its positions among developed economies. The following methods were used to carry out mathematical modeling to enhance the university energy efficiency in the frameworks of the energy knowledge hub: neural network technologies, mean absolute and relative error, mean absolute deviation; statistical comparison of the forecast accuracy based on the mean absolute error, as well as time series forecasting. A model to boost the University energy efficiency has been developed within the knowledge energy hub by implementing neural network patterns based on the experimental data from the Kyiv National University of Technologies and Design for the heating period 2020–2021. In particular, to optimize the operating modes of automatic power supply control for University Building 4, mathematical models with a complex algorithm structure have been employed (offering the increased resource intensity of such tasks). It is argued that making a decision on the feasibility of using an energy hub for University buildings and selecting appropriate equipment should be accomplished with due regard to the structure and the capacity of energy consumers, their types, demands for quality and reliability of electric power supply, their compliance with operating and safety standards, as well as taking into account the results of climate, wind monitoring and monitoring of solar activity. The conclusions resume that to assure the energy quality and the system sustainability, it is considered important to resolve a range of issues related to inconsistency in generation and supply of renewable energy from power plants, ensuring reliability and quality of energy supply through the use of energy storage (batteries) in particular, etc.).
39
Ekwunife, Ifunanya C. "Technology Focus: Natural Gas Processing and Handling (April 2021)." Journal of Petroleum Technology 73, no. 04 (April 1, 2021): 34. http://dx.doi.org/10.2118/0421-0034-jpt.
Повний текст джерелаАнотація:
In 2020, the spot prices of natural gas hit a record low in the US, reaching the lowest annual average price in more than a decade. Based on US Energy Information Administration (EIA) data, the average annual spot price reported in 2020 was $2.05 per million British thermal units (MMBtu). In the first few months of the year, reports from the EIA showed that natural gas prices started declining amid mild winter temperatures that resulted in a decline in the demand for natural gas for space heating. In March 2020, following the onset of the COVID-19 pandemic, the already declining natural gas prices plummeted further. This decline continued through the first half of the year. The EIA reported the average monthly Henry Hub spot price in the first 6 months at $1.81/MMBtu. June saw the lowest monthly natural gas price in decades (Henry Hub price aver-aged $1.66/MMBtu). Natural gas prices recovered in the second half of the year as natural gas production decreased and global exports of liquefied natural gas increased. Natural gas consumption in the residential, commercial, and industrial sectors declined in 2020, according to the EIA. Milder winter temperatures were a major contributor in the first quarter of the year, but overall declining consumption was attributed to reduced economic activities as a result of the COVID-19 pandemic. On the other hand, the consumption of natural gas for electric power generation registered an overall increase of 2% more than the 2019 average. According to the EIA, citing S&P Global Platts, this increase was attributed to power producers switching to cheaper natural gas from coal to meet the increased demand for electric power for cooling as summer temperatures increased. The EIA in its Annual Energy Outlook 2021 projects that the industrial and electric power sectors and net exports will drive the growth in US energy consumption between 2020 and 2050. Natural gas consumption in other sectors is expected to increase steadily or remain flat. The EIA forecasts that natural gas production will increase as consumption increases and prices will stay low relative to past prices. The EIA expects continued growth in natural gas exports as natural gas production surpasses natural gas consumption. Globally, the International Energy Agency forecasts a recovery in global demand for natural gas in 2021 led by growth in the Asia Pacific region as emerging markets recover. The US will continue to play a significant role as one of the largest producers and contributors to natural gas supply growth. Recommended additional reading at OnePetro: www.onepetro.org. SPE 200300 - Overcoming Challenges in the Development of Underground Gas Storage by Ammar Alali, Saudi Aramco, et al. OTC 30602 - Offshore LNG and Gas Monetization by Femi Adeoye Alabi, Total SPE 200147 - Development of the Underground Gas Storage and Construction of the Salt Cavern Storage in China by Peng Chen, CNPC, et al.
40
Zhang, Yang, Zhenghui Fu, Yulei Xie, Qing Hu, Zheng Li, and Huaicheng Guo. "A Comprehensive Forecasting–Optimization Analysis Framework for Environmental-Oriented Power System Management—A Case Study of Harbin City, China." Sustainability 12, no. 10 (May 22, 2020): 4272. http://dx.doi.org/10.3390/su12104272.
Повний текст джерелаАнотація:
In this study, a comprehensive research framework coupled with electric power demand forecasting, a regional electric system planning model, and post-optimization analysis is proposed for electric power system management. For dealing with multiple forms of uncertainties and dynamics concerning energy utilization, capacity expansions, and environmental protection, the inexact two-stage stochastic robust programming optimization model was developed. The novel programming method, which integrates interval parameter programming (IPP), stochastic robust optimization (SRO), and two-stage stochastic programming (TSP), was applied to electric power system planning and management in Harbin, China. Furthermore, the Gray-Markov approach was employed for effective electricity consumption prediction, and the forecasted results can be described as interval values with corresponding occurrence probability, aiming to produce viable input parameters of the optimization model. Ten scenarios were analyzed with different emissions reduction levels and electricity power structure adjustment modes, and the technique for order of preference by similarity to ideal solution (TOPSIS) was selected to identify the most influential factors of planning decisions by selecting the optimal scheme. The results indicate that a diversified power structure that dominates by thermal power and is mainly supplemented by biomass power should be formed to ensure regional sustainable development and electricity power supply security in Harbin. In addition, power structure adjustment is more effective than the pollutants emission control for electricity power system management. The results are insightful for supporting supply-side energy reform, generating an electricity generation scheme, adjusting energy structures, and formulating energy consumption of local policies.
41
Wegorzewski, Anna, Martin Köpcke, Thomas Kuhn, Maria Sitnikova, and Hermann Wotruba. "Thermal Pre-Treatment of Polymetallic Nodules to Create Metal (Ni, Cu, Co)-Rich Individual Particles for Further Processing." Minerals 8, no. 11 (November 11, 2018): 523. http://dx.doi.org/10.3390/min8110523.
Повний текст джерелаАнотація:
Polymetallic nodules are a potential source of industrially demanded metals such as Ni, Co, Cu, and Mo (up to 3 wt %). Even if there is no deep-sea mining of manganese nodules today, a forecasted gap between metal demand and supply as well as continuously high metal prices may make seabed mining economically viable in the future. Up to now, a well-established industrial-scale extraction method for manganese nodules has been missing. Therefore, the aim of this study is to explore how economically interesting metals can be extracted from the nodules in a cost- and energy-efficient way. Polymetallic nodules have a heterogeneous chemical and structural composition without individual metal-rich particles. The economically interesting metals are distributed between different mineral phases (Mn-Fe-(oxy)hydroxides) as well as different growth structures that are intergrown with each other on a nm‒µm scale. Because of that a typical ore processing with the beneficiation of valuable particles is not feasible. The process presented here starts with a pyro-metallurgical pre-treatment of the polymetallic nodules, with the aim of creating artificial metal-rich (Ni, Cu, Co, Mo) particles with enrichment factors up to 10 compared to the original average metal contents. Afterwards, these particles should be beneficiated by conventional mineral processing steps to create a concentrate while reducing the mass stream in the process. The resulting metal particles can be further treated in conventional hydrometallurgical and/or pyro-metallurgical processes.
42
Elgammal, Adel, and Tagore Ramlal. "Optimal Frequency Control Management of Grid Integration PV/Wind/FC/Storage Battery Based Smart Grid Using Multi Objective Particle Swarm Optimization MOPSO and Model Predictive Control (MPC)." European Journal of Engineering and Technology Research 6, no. 5 (July 12, 2021): 50–56. http://dx.doi.org/10.24018/ejers.2021.6.5.2507.
Повний текст джерелаАнотація:
This article forecasts the performance of smart-grid electrical transmission systems and integrated battery/FC/Wind/PV storage system renewable power sources in the context of unpredictable solar and wind power supplies. The research provided a hybrid renewable energy sources smart grid power system electrical frequency control solution using adaptive control techniques and model predictive control (MPC) based on the Multi-Objective Practical Swarm Optimization Algorithm MOPSO. To solve the problems of parameter tuning in Load Frequency Control, the suggested adaptive control approach is utilized to accomplish on-line adjustment of the Load Frequency Control parameters. During the electrical grid's integration, the system under investigation is a hybrid Wind/PV/FC/Battery smart grid with variable demand load. To achieve optimal outcomes, all of the controller settings for various units in power grids are determined by means of a customized objective function and a particle swarm optimization method rather than a regular objective function with fluctuating restrictions. To suppress the consumption and generation balance, MPCs were designed for each of the Storage Battery, Wind Turbine Generation, and the model Photovoltaic Generation. In addition, demand response (real-time pricing) was used in this scheme to reduce the load frequency by adjusting the controlled loads. The suggested control strategy is evaluated in the Simulink /MATLAB environment in order to analyse the suggested approach's working in the power system, as well as its effectiveness, reliability, robustness, and stability. The simulation findings show that the proposed control method generally converges to an optimal operating point that minimises total user disutility, restores normal frequency and planned tie-line power flows, and maintains transmission line thermal restrictions. The simulation results further indicate that the convergence holds even when the control algorithm uses inaccurate system parameters. Finally, numerical simulations are used to illustrate the proposed algorithm's robustness, optimality, and effectiveness. In compared to previous methodologies, the system frequency recovers effectively and efficiently in the event of a power demand disturbance, as demonstrated. A sensitivity test is also performed to assess the suggested technique's effectiveness.
43
Elgammal, Adel, and Tagore Ramlal. "Optimal Frequency Control Management of Grid Integration PV/Wind/FC/Storage Battery Based Smart Grid Using Multi Objective Particle Swarm Optimization MOPSO and Model Predictive Control (MPC)." European Journal of Engineering and Technology Research 6, no. 5 (July 12, 2021): 50–56. http://dx.doi.org/10.24018/ejeng.2021.6.5.2507.
Повний текст джерелаАнотація:
This article forecasts the performance of smart-grid electrical transmission systems and integrated battery/FC/Wind/PV storage system renewable power sources in the context of unpredictable solar and wind power supplies. The research provided a hybrid renewable energy sources smart grid power system electrical frequency control solution using adaptive control techniques and model predictive control (MPC) based on the Multi-Objective Practical Swarm Optimization Algorithm MOPSO. To solve the problems of parameter tuning in Load Frequency Control, the suggested adaptive control approach is utilized to accomplish on-line adjustment of the Load Frequency Control parameters. During the electrical grid's integration, the system under investigation is a hybrid Wind/PV/FC/Battery smart grid with variable demand load. To achieve optimal outcomes, all of the controller settings for various units in power grids are determined by means of a customized objective function and a particle swarm optimization method rather than a regular objective function with fluctuating restrictions. To suppress the consumption and generation balance, MPCs were designed for each of the Storage Battery, Wind Turbine Generation, and the model Photovoltaic Generation. In addition, demand response (real-time pricing) was used in this scheme to reduce the load frequency by adjusting the controlled loads. The suggested control strategy is evaluated in the Simulink /MATLAB environment in order to analyse the suggested approach's working in the power system, as well as its effectiveness, reliability, robustness, and stability. The simulation findings show that the proposed control method generally converges to an optimal operating point that minimises total user disutility, restores normal frequency and planned tie-line power flows, and maintains transmission line thermal restrictions. The simulation results further indicate that the convergence holds even when the control algorithm uses inaccurate system parameters. Finally, numerical simulations are used to illustrate the proposed algorithm's robustness, optimality, and effectiveness. In compared to previous methodologies, the system frequency recovers effectively and efficiently in the event of a power demand disturbance, as demonstrated. A sensitivity test is also performed to assess the suggested technique's effectiveness.
44
Elgammal, Adel, and Tagore Ramlal. "Optimal Model Predictive Frequency Control Management of Grid Integration PV/Wind/FC/Storage Battery Based Smart Grid Using Multi Objective Particle Swarm Optimization MOPSO." WSEAS TRANSACTIONS ON ELECTRONICS 12 (July 12, 2021): 46–54. http://dx.doi.org/10.37394/232017.2021.12.7.
Повний текст джерелаАнотація:
This article forecasts the performance of smart-grid electrical transmission systems and integrated battery/FC/Wind/PV storage system renewable power sources in the context of unpredictable solar and wind power supplies. The research provided a hybrid renewable energy sources smart grid power system electrical frequency control solution using adaptive control techniques and model predictive control (MPC) based on the Multi-Objective Practical Swarm Optimization Algorithm MOPSO. To solve the problems of parameter tuning in Load Frequency Control, the suggested adaptive control approach is utilized to accomplish on-line adjustment of the Load Frequency Control parameters. During the electrical grid's integration, the system under investigation is a hybrid Wind/PV/FC/Battery smart grid with variable demand load. To achieve optimal outcomes, all of the controller settings for various units in power grids are determined by means of a customized objective function and a particle swarm optimization method rather than a regular objective function with fluctuating restrictions. To suppress the consumption and generation balance, MPCs were designed for each of the Storage Battery, Wind Turbine Generation, and the model Photovoltaic Generation. In addition, demand response (real-time pricing) was used in this scheme to reduce the load frequency by adjusting the controlled loads. The suggested control strategy is evaluated in the Simulink /MATLAB environment in order to analyse the suggested approach's working in the power system, as well as its effectiveness, reliability, robustness, and stability. The simulation findings show that the proposed control method generally converges to an optimal operating point that minimises total user disutility, restores normal frequency and planned tie-line power flows, and maintains transmission line thermal restrictions. The simulation results further indicate that the convergence holds even when the control algorithm uses inaccurate system parameters. Finally, numerical simulations are used to illustrate the proposed algorithm's robustness, optimality, and effectiveness. In compared to previous methodologies, the system frequency recovers effectively and efficiently in the event of a power demand disturbance, as demonstrated. A sensitivity test is also performed to assess the suggested technique's effectiveness.
45
Karuppannan Gopalraj, Sankar, Ivan Deviatkin, Mika Horttanainen, and Timo Kärki. "Life Cycle Assessment of a Thermal Recycling Process as an Alternative to Existing CFRP and GFRP Composite Wastes Management Options." Polymers 13, no. 24 (December 17, 2021): 4430. http://dx.doi.org/10.3390/polym13244430.
Повний текст джерелаАнотація:
There are forecasts for the exponential increase in the generation of carbon fibre-reinforced polymer (CFRP) and glass fibre-reinforced polymer (GFRP) composite wastes containing valuable carbon and glass fibres. The recent adoption of these composites in wind turbines and aeroplanes has increased the amount of end-of-life waste from these applications. By adequately closing the life cycle loop, these enormous volumes of waste can partly satisfy the global demand for their virgin counterparts. Therefore, there is a need to properly dispose these composite wastes, with material recovery being the final target, thanks to the strict EU regulations for promoting recycling and reusing as the highest priorities in waste disposal options. In addition, the hefty taxation has almost brought about an end to landfills. These government regulations towards properly recycling these composite wastes have changed the industries’ attitudes toward sustainable disposal approaches, and life cycle assessment (LCA) plays a vital role in this transition phase. This LCA study uses climate change results and fossil fuel consumptions to study the environmental impacts of a thermal recycling route to recycle and remanufacture CFRP and GFRP wastes into recycled rCFRP and rGFRP composites. Additionally, a comprehensive analysis was performed comparing with the traditional waste management options such as landfill, incineration with energy recovery and feedstock for cement kiln. Overall, the LCA results were favourable for CFRP wastes to be recycled using the thermal recycling route with lower environmental impacts. However, this contradicts GFRP wastes in which using them as feedstock in cement kiln production displayed more reduced environmental impacts than those thermally recycled to substitute virgin composite production.
46
Hu, Jiankun, and Zhijian Chen. "Energy Demand Forecast Analysis Based on Improved Grey Forecast Model." Journal of Physics: Conference Series 2399, no. 1 (December 1, 2022): 012006. http://dx.doi.org/10.1088/1742-6596/2399/1/012006.
Повний текст джерелаАнотація:
Abstract As China is on its way to achieving its carbon peaking and carbon neutrality goals, forecasting energy demand is increasingly important. Aiming at the problem of energy demand forecasting, this paper uses the logistic equation and Markov Model to improve the traditional Grey Forecast Model. And based on the data of China's total energy consumption from 2002 to 2021 as the original data, an improved grey prediction model was established. The validity of the prediction model in this paper is verified by the comparative analysis with the prediction results of the STIRPAT and LSTM prediction models. The results show that compared with the STIRPAT and LSTM prediction models, the predicted results of the improved Grey Forecast Model have smaller relative errors, more accurate prediction results, and better prediction performance.
47
Anik, Asif Reza, and Sanzidur Rahman. "Commercial Energy Demand Forecasting in Bangladesh." Energies 14, no. 19 (October 6, 2021): 6394. http://dx.doi.org/10.3390/en14196394.
Повний текст джерелаАнотація:
Although both aggregate and per capita energy consumption in Bangladesh is increasing rapidly, its per capita consumption is still one of the lowest in the world. Bangladesh gradually shifted from petroleum-based energy to domestically sourced natural-gas-based energy sources, which are predicted to run out within next two decades. The present study first identified the determinants of aggregate commercial energy and its three major components of oil, natural gas, and coal demand for Bangladesh using a simultaneous equations framework on an annual database covering a period of 47 years (1972–2018). Next, the study forecast future demand for aggregate commercial energy and its three major components for the period of 2019–2038 under the business-as-usual and ongoing COVID-19 pandemic scenarios with some assumptions. As part of a sensitivity analysis, based on past trends, we also hypothesized four alternative GDP and population growth scenarios and forecast corresponding changes in total energy demand forecast. The results revealed that while GDP and lagged energy demand are the major drivers of energy demand in the country, we did not see strong effects of own- and cross-price elasticities of energy sources, which we attributed to three reasons: subsidized low energy prices, time and cost required to switch between different energy-mix technologies, and suppressed energy demand. The aggregate energy demand is expected to increase by 400% by the end of the forecasting period in 2038 from its existing level in 2018 under the business-as-usual scenario, whereas the effect of COVID-19 could suppress it down to 300%. Under the business-as-usual scenario, the highest increase will occur for coal (3.94-fold), followed by gas (2.64-fold) and oil (2.37-fold). The COVID-19 pandemic will suppress the future demand of all energy sources at variable rates. The ex ante forecasting errors were small, varying within the range of 3.6–3.7% of forecast values. Sensitivity analysis of changes in GDP and population growth rates showed that forecast total energy demand will increase gradually from 3.58% in 2019 to 8.79% by 2038 from original forecast values. Policy recommendations include capacity building of commercial energy sources while ensuring the safety and sustainability of newly proposed coal and nuclear power installations, removing inefficiency of production and distribution of energy and its services, shifting towards renewable and green energy sources (e.g., solar power), and redesigning subsidy policies with market-based approaches.
48
Hietaharju, Petri, Mika Ruusunen, and Kauko Leiviskä. "Enabling Demand Side Management: Heat Demand Forecasting at City Level." Materials 12, no. 2 (January 9, 2019): 202. http://dx.doi.org/10.3390/ma12020202.
Повний текст джерелаАнотація:
Implementation of new energy efficiency measures for the heating and building sectors is of utmost importance. Demand side management offers means to involve individual buildings in the optimization of the heat demand at city level to improve energy efficiency. In this work, two models were applied to forecast the heat demand from individual buildings up to a city-wide area. District heating data at the city level from more than 4000 different buildings was utilized in the validation of the forecast models. Forecast simulations with the applied models and measured data showed that, during the heating season, the relative error of the city level heat demand forecast for 48 h was 4% on average. In individual buildings, the accuracy of the models varied based on the building type and heat demand pattern. The forecasting accuracy, the limited amount of measurement information and the short time required for model calibration enable the models to be applied to the whole building stock. This should enable demand side management and lead to the predictive optimization of heat demand at city level, leading to increased energy efficiency.
49
Li, Jian, and Xiaoqian Zhang. "Beijing-Tianjin-Hebei Energy Demand Combination Forecast Analysis." IOP Conference Series: Earth and Environmental Science 631 (January 7, 2021): 012104. http://dx.doi.org/10.1088/1755-1315/631/1/012104.
Повний текст джерела
50
Hongbo, Zhang, and Zhong Nian. "Forecast of Energy Demand in the Next Decade." Energy Procedia 5 (2011): 2536–39. http://dx.doi.org/10.1016/j.egypro.2011.03.436.
Повний текст джерела