METHOD AND MODELS FOR ESTIMATING THE INFLUENCE OF DISTRIBUTED GENERATION DEVELOPMENT ON ELECTRICITY DEMAND AND PRICE IN A REGION

Актуальность статьи обусловлена переходом энергетики на путь интеллектуального развития. Одной из характеристик этого перехода является расширение перспектив децентрализованного производства электроэнергии. В новых условиях у потребителя появляется возможность изменять свое энергопотребление в зависимости от стоимости энергоресурсов, иметь собственные установки по производству и/или хранению электроэнергии. В статье кратко представлены особенности использования и технологии распределенной генерации (РГ) энергии в отдельных секторах экономики (промышленности, жилом и коммерческом секторах, на транспорте). Приведены некоторые способы определения перспективных значений мощностей РГ у потребителей. Предложен подход к моделированию и оценке влияния масштабов распределенной генерации на долгосрочное развитие региональных систем энергоснабжения. Отличительной чертой подхода является использование ранее разработанной оптимизационной стохастической модели объединенной энергосистемы макрорегиона в качестве основы для разработки ее региональной модификации и источника прогнозной информации об условиях развития региональной энергосистемы. Несмотря на то, что предложенный подход дает лишь приблизительную оценку, это позволяет очертить перспективный диапазон изменения стоимости и спроса на электроэнергию в регионе при разных вариантах развития РГ. The relevance of the article is due to the transition of energy to the path of intellectual development. One of the characteristics of this transition is the broadening of the prospects for decentralized electricity generation. Under the new conditions, a consumer has the opportunity to change his energy consumption depending on the cost of energy resources, to have his own installations for the production and / or storage of electricity. The article briefly presents the features of the use and technology of distributed generation (DG) energy in certain sectors of the economy (industry, residential and commercial sectors, transport). Some methods of determining the prospective values of the power of the RG at consumers are given. An approach to modeling and assessing the impact of the scale of distributed generation on the long-term development of regional power supply systems is proposed. A distinctive feature of the approach is the use of the previously developed stochastic optimization model of the unified power system of the macroregion as a basis for the development of its regional modification and a source of forecast information on the conditions for the development of the regional power system. Of course, the proposed approach gives only a rough estimate, but it allows us to outline a promising range of changes in the cost and demand for electricity in the region under different options for the development of the RG.

An Experimental Study of Bricks Made with Different Mineral Additives

Large numbers of bricks are required to meet the needs of increasing population for both residential and commercial sectors. The overall development and industrial development lead to the use of the traditional bricks at a very hefty rate. The traditional bricks are commonly made up of clay as its main component as a result it is on the verge of exhaustion. To standardize its broad, utilize and discovering its auxiliary one is the need of the hour. In this study, bricks were made by using different proportions of alternative materials like fly ash (FA), sand, lime and plaster of Paris. The size of specimen 230×110×70 mm was manufactured and quality constraints like compressive strength and water absorption were tested at different curing ages. The cost-effective mix design of the bricks for optimum compressive strength and rate analysis was also the part of this study. It was detected from the observation of results that the compressive strength decreases with increasing percentage in fly ash and increases with increasing percentage of sand and lime in fly ash-based bricks. Increment in water absorption was observed as fly ash content is increased and decrement of water absorption is seen with increasing percentage of sand and lime in fly ash-based bricks.

A Machine Learning-Based Prediction Model of LCCO2 for Building Envelope Renovation in Taiwan

In 2015, Taiwan’s government announced the “Greenhouse Gas Reduction and Management Act”, the goal of which was a 50% reduction in carbon emissions by 2050, compared with 2005. The residential and commercial sectors produce approximately one third of all carbon emissions in Taiwan, and the number of construction renovation projects is much larger than that of new construction projects. In this paper, we considered the life-cycle CO2 (LCCO2) of a building envelope renovation project in Tainan and focused on local construction methods for typical row houses. The LCCO2 of 744 cases with various climate zones, orientations, and insulation and glazing types was calculated via EnergyPlus, SimaPro, and a local database (LCBA database), and the results were then used to develop a machine learning model. Our findings showed that the machine learning model was capable of predicting annual energy consumption and LCCO2. With regard to annual energy consumption, the RMSE was 227.09 kW·h (per year) and the R2 was 0.992. For LCCO2, the RMSE was 2792.47 kgCO2eq and the R2 was 0.989, which indicates a high-confidence process for decision making in the early stages of building design and renovation.

Energy audit: types, scope, methodology and report structure

<span>The objective of this paper is to study various methods adopted during the energy audit. Many countries are focusing on energy, not only enhancing the tenable power generation sources but also on utilizing the power more proficiently for economic utilization. The energy audit is an important parameter for all the developing and developed countries and they focus on energy efficiency, energy quality, and energy intensity. In the industrial, residential, and commercial sectors the top operating expense is found to be are material, machine, manpower, and energy. Identification of the energy-consuming sector is the prior attention to look for the energy-saving potential and quality improvement. Implementation of an energy audit can enhance the efficiency, quality of power, reduce the tariff of bills, and reduces the wastage of energy. The need for energy audits, different types of energy audits, various types of software used, and the energy audit report structure were presented in this paper.</span>

Boosting Energy Efficiency and Solar Energy inside the Residential, Commercial, and Public Services Sectors in Mexico

The residential, commercial, and public sectors consume between 20% and 30% of final energy demand worldwide. Due to the intensive use of fossil fuels and conventional electricity, they also have an important participation in the emission of greenhouse gases (GHG). Taking Mexico as a case study, this article develops an alternative scenario that considers that in these sectors, buildings can generate energy for self-consumption or to supply it to the power network—for which four solar energy options are analyzed. In addition, to manage and rationalize the energy demand of these buildings, eight energy efficiency measures are studied. These options were selected on the basis that they are technically and economically feasible to implement in buildings in Mexico. The results reveal that by 2030, in relation to the GHG trend scenario, this mitigation scenario reduces 23.5 million tons of carbon dioxide equivalent (MtCO2e) in the residential (19 MtCO2e), commercial (2.6 MtCO2e), and public services sectors (1.9 MtCO2e), while by 2035 it reaches 45 MtCO2e; which far exceed the avoided emissions goals established in Mexico’s nationally determined contributions (NDC) for 2030 (5 MtCO2e) for the residential and commercial sectors. Therefore, it is possible to increase the ambition for mitigation in these sectors, as well as including the public sector, in a renewed Mexico’s NDC. This mitigation scenario generates a total economic benefit of $7.7 billion, which means that it does not generate an overall incremental cost, but requires an incremental investment of over $9 billion USD, which is a financing challenge to achieve this scenario.

Machine Learning Modeling for Energy Consumption of Residential and Commercial Sectors

Energy has a strategic role in the economic and social development of countries. In the last few decades, energy demand has been increasing exponentially across the world, and predicting energy demand has become one of the main concerns in many countries. The residential and commercial sectors constitute about 34.7% of global energy consumption. Anticipating energy demand in these sectors will help governments to supply energy sources and to develop their sustainable energy plans such as using renewable and non-renewable energy potentials for the development of a secure and environmentally friendly energy system. Modeling energy consumption in the residential and commercial sectors enables identification of the influential economic, social, and technological factors, resulting in a secure level of energy supply. In this paper, we forecast residential and commercial energy demands in Iran using three different machine learning methods, including multiple linear regression, logarithmic multiple linear regression methods, and nonlinear autoregressive with exogenous input artificial neural networks. These models are developed based on several factors, including the share of renewable energy sources in final energy consumption, gross domestic production, population, natural gas price, and the electricity price. According to the results of the three machine learning methods applied in our study, by 2040, Iranian residential and commercial energy consumption will be 76.97, 96.42 and 128.09 Mtoe, respectively. Results show that Iran must develop and implement new policies to increase the share of renewable energy supply in final energy consumption.

Trend Analysis on Adoption of Virtual and Augmented Reality in the Architecture, Engineering, and Construction Industry

With advances in Building Information Modeling (BIM), Virtual Reality (VR) and Augmented Reality (AR) technologies have many potential applications in the Architecture, Engineering, and Construction (AEC) industry. However, the AEC industry, relative to other industries, has been slow in adopting AR/VR technologies, partly due to lack of feasibility studies examining the actual cost of implementation versus an increase in profit. The main objectives of this paper are to understand the industry trends in adopting AR/VR technologies and identifying gaps within the industry. The identified gaps can lead to opportunities for developing new tools and finding new use cases. To achieve these goals, two rounds of a survey at two different time periods (a year apart) were conducted. Responses from 158 industry experts and researchers were analyzed to assess the current state, growth, and saving opportunities for AR/VR technologies for the AEC industry. The findings demonstrate that older generations are significantly more confident about the future of AR/VR technologies and they see more benefits in AR/VR utilization. Furthermore, the research results indicate that Residential and commercial sectors have adopted these tools the most, compared to other sectors and institutional and transportation sectors had the highest growth from 2017 to 2018. Industry experts anticipated a solid growth in the use of AR/VR technologies in 5 to 10 years, with the highest expectations towards healthcare. Ultimately, the findings show a significant increase in AR/VR utilization in the AEC industry from 2017 to 2018.

A Cost Effective Solution to Security Constrained Unit Commitment and Dispatch Problem using Hybrid Search Algorithm

The Security constraints unit commitment (SCUC) is a powerful scheduling technique used in power markets for daily planning. SCUC is a framework that combines two common algorithms in the electricity industry: Unit Commitment (UC) and Economic Dispatch (ED), while adding a new dimension – Security. Load demand is changing continuously due to variation of load of industrial, residential and commercial sectors. Thus, it is very important to decide which generating unit should be kept on and how much power should be dispatched so that time varying load demand can be meet on Hourly basis and there should be no scope of occurrence of loss of load hour (LOLH). In the proposed research, the recently developed hybrid meta-heuristics search algorithm i.e. GWO-RES has been applied to solve the security constrained Unit Commitment and dispatch problem of Electric power system. The efficiency of the proposed hybrid algorithm has been tested for standard IEEE-14 Bus, 30-Bus and 56-bus system and it has been experimentally found that GWO-RES performs much better than hybrid GWO-PS and GWO-RS algorithm.

Trend Analysis on Adoption of Virtual and Augmented Reality in the Architecture, Engineering, and Construction Industry

With advances in Building Information Modeling (BIM), Virtual Reality (VR) and Augmented Reality (AR) technologies have many potential applications in the Architecture, Engineering, and Construction (AEC) industry. However, the AEC industry, relative to other industries, has been slow in adopting AR/VR technologies, partly due to lack of feasibility studies examining the actual cost of implementation versus an increase in profit. The main objectives of this paper are to understand the industry trends in adopting AR/VR technologies and identifying gaps within the industry. The identified gaps can lead to opportunities for developing new tools and finding new use cases. To achieve these goals, two rounds of a survey at two different time periods (a year apart) were conducted. Responses from 158 industry experts and researchers were analyzed to assess the current state, growth, and saving opportunities for AR/VR technologies for the AEC industry. The findings demonstrate that older generations are significantly more confident about the future of AR/VR technologies and they see more benefits in AR/VR utilization. Furthermore, the research results indicate that Residential and commercial sectors have adopted these tools the most, compared to other sectors and institutional and transportation sectors had the highest growth from 2017 to 2018. Industry experts anticipated a solid growth in the use of AR/VR technologies in 5 to 10 years, with the highest expectations towards healthcare. Ultimately, the findings show a significant increase in AR/VR utilization in the AEC industry from 2017 to 2018.