Energy demand increase due to large deployment of electric vehicles combined with volatile decentralized renewable energy production is bringing up new challenges in the transmission network. Power quality issues might be avoided taking advantage from the flexibility offered by the charging process to match the local renewable energy production. However, the potential benefits from a controlled electric vehicle charging process could be optimally exploited only if electric vehicles energy demand is reliably evaluated.
This study proposes a detailed methodology to evaluate the load of a working place charging station, in order to further optimally design a second life battery storage system for ancillary services provision. In details, the electric vehicles energy demand has been estimated using a multiple linear regression model that links the vehicles battery energy consumption with microscopic driving parameters (such as speed and acceleration). In particular, the model inputs are typical driving cycles performed by the employees to reach the working place. These representative speed profiles have been reconstructed with a Markov chain-based method using real-world collected data.
The proposed approach allows to predict the battery energy consumption with a Mean Absolute Error less than 18% and with a correlation coefficient R2 of 99%.
With advances of Internet-of-Things and home automation technologies, design and development of Home Energy Management Systems (HEMSs) have become an active research area in recent years. This paper proposes a new HEMS that performs operation scheduling for household appliances by coordinately considering the household’s energy cost and the occupant’s acoustic comfort. A noise gain model is established for the electric household appliances and an acoustic comfort model is proposed for the occupant. Based on this, an optimal appliance scheduling model is formulated that balances the objectives of minimizing the home energy cost and maximizing the occupant’s acoustic comfort. A biological intelligence inspired optimizer is applied to solve the proposed model, and case studies are designed to validate the proposed method.
The current World situation is particularly complex and heavy, from many points of view, among which energy and socio-economic ones. The COVID-19 pandemic, besides causing a health crisis, has brought out, even more, the need to reduce emissions for a healthier environment. The role played by the existing building stock is fundamental, as it is responsible for 36% of CO2 emissions in Europe. The proposed work aims to define some energy refurbishment interventions, for a residential building in south Italy, regarding the building envelope, the heating and cooling systems, and the addition of systems from renewable energy sources in order to improve its energy labeling. The methodological approach has followed the guidelines for the energy certification of buildings in Italy. It was possible to evaluate the improvement in building energy labels according to the proposed efficiency measures, highlighting those which allow for a tax relief recently introduced by the Government. Finally, the addition of photovoltaic and solar collector systems was evaluated to allow even the fulfillment of nZEB standard.
By using non-intrusive load monitoring, energy consumption of individual appliances can be labeled through disaggregating the aggregated consumption of an electrical network by data analytical algorithms. Due to the advantage of low cost and easy installation, and the requirements of smart grid applications, NILM has been widely focused in recent years. However, the accuracy of the NILM can be greatly affected by the difference in power resolution of appliances. In this paper, a two hierarchical Gaussian mixture model-based method is proposed to solve this problem. At the 1st hierarchical level, the aggregated energy consumption signals are disaggregated into high-power appliances and low-power appliances. Consequently, at the 2nd hierarchical level, detailed appliances energy usage behaviors can be estimated with adapted power resolutions, respectively. The pubic dataset– BLUED is used to verify the proposed method. The results show that the proposed method effectively improve the accuracy of NILM, particularly for low-power appliances, compared with conventional Gaussian mixture model method.
Despite the large availability of low-temperature industrial waste heat, and the maturity of the technologies for its exploitation, the rate of implementation of these interventions in the industrial sector is low, mainly due to the presence of numerous barriers, such as the complexity of identifying the solution on which to focus attention based on the available heat and internal needs. Through the definition of a comprehensive methodology, this work aims to propose an easy-to-use tool that can provide real support to companies in the preliminary assessment of waste heat recovery opportunities.
This work aims to illustrate a sustainable socio-techno-economic microgrid (SSTEM) design framework based on locally accessible energy resources such as solar, wind, hydro, etc. for remote/rural electrification purposes in the context of developing and least developing nations. The proposed SSTEM framework consists of separate three subdesign levels integrated as one all-inclusive design process. The outlined framework can incorporate several combinations of the available energy resources in the vicinity such as hydrokinetic system (HKS), photovoltaic (PV), small wind turbine (SWT), etc. as primary energy sources with pump hydro system (PHS) and battery as energy storage and diesel generator as a backup for designing the community microgrid. Many combinations of primary generating sources and storage systems are utilized in this study to determine the suitable alternative. A preliminary socio-techno-economic evaluation of different microgrid elements (energy technologies and storage systems) will be introduced in this first stage of the proposed design process using decision analysis tools based on a set of performance indicators. The best alternatives from each of the elements, i.e., renewable energy technologies (RETs) and energy storage systems (ESS) assessed on the anticipated performance indicators, will be obtained to be used for the next-level design process. In the subsequent design stage, the detailed feasibility (techno-economic) analysis of the solutions by combining different elements (RETs and ESS), which are obtained after the first stage with diesel generator (DG) in different microgrid architectures will be performed with multi-objective optimization tool. Different sizes and costing of various microgrid elements in varying suitable architectures will be obtained after this stage. In the final stage multi-criteria decision making (MCDM) models will be utilized to determine the best possible microgrid based on suitably defined criteria for electrifying the remote villages/communities.
The growth of load or depreciation resulting from load shifting and peak shifting are two major phenomena observed when an epidemic or pandemic strike. Robust and reliable power and energy management system becomes the need of the hour to meet the load variation. Hence, in this work, a power and energy management system for an isolated microgrid using fuzzy based controller considering a real-time load growth scenario is illustrated. The microgrid consists of renewable energy technologies (RETs) utilizing the locally available resources (solar and wind) and lead-acid battery as storage and diesel generator as backup. The selection and sizing of the microgrid’s various elements are carried based on the load determined and predicted before the pandemic. An intelligent fuzzy-based controller (IFBC) is designed to manage the power flow between the microgrid elements efficiently. IFBC can deal with the system’s uncertainties through an IF-THEN rule-based approach, reducing mathematical modeling requirements. Further, it is robust to the load variations and operates without boundary conditions. The modeled IFBC has three input variables and two outputs. The input variables of the IFBC are total available power from RETs, total existing load demand, and the difference between power from generation and connected load. Fuzzy logic controller (FLC) output is power, fed to the load and battery energy storage system (BESS) for compensating the gap between power demand and supply and meeting the demand to keep the batteries at minimum SOC of 20%. The analysis presented in this work is based on the actual load data collected from a remote village in India before and during the pandemic, demonstrating the proposed controller’s effectiveness.
With a population of 220 million, Pakistan is the sixth populous country in the world. The first case of COVID-19 in Pakistan was reported in late February 2020 but at least for the first wave the country managed to successfully flatten the curve by August 2020. It was observed that energy usage is correlated more to the lockdown rather than the COVID-19 peak. The nationwide lockdown in Pakistan was enforced in March 2020, which resulted in a year-on-year decrease in electricity consumption of 9% for the month. However, only an insignificant (0.09% year-on-year) increase in energy consumption was seen in June 2020, when the number of COVID-19 cases peaked. During the lockdown period, performance of the short-term load forecasting models was impacted negatively. This impact created unit commitment and dispatch uncertainties for grid operations. Following the curve flattening, we observe a shift to the normal daily energy demand in the country.
International energetic agreements define future targets to push the decarbonization process by renewables increasing. Their deep penetration in AC grids will determine limited and alternating operative modes of traditional Synchronous Generators. In such scenarios, instabilities will not intrinsically be balanced causing inertia critical conditions. In this paper a dual approach is proposed to mitigate the problem. The strategy constituted by preventive and solving actions employ DC microgrids to locally include and manage suitable Variable Energy Resources amount and to assure prompt virtual inertia provision to the AC grid. 2030 case studies for an Italian city are analyzed.
Energy consumption is one of the main sources of GHG emissions in China with the development of rapid urbanization. To tackle climate change and energy conservation, China has processed a series practices that gain co-benefits towards meeting sustainable development goals along with climate change mitigation since 2007.This paper evaluates the impact factors of population, urbanization level, GDP per capita, industrialization level on the environmental energy saving impact using the Stochastic Impacts by Regression on Population, Affluence and Technology (STIRPAT) model based on China provincial panel data from 2005 to 2017. The results show that industrialization has the largest potential effect on environmental impact, followed by urbanization level, GDP per capita and population. Industrialization and GDP per capita can cause an increase in energy consumption per capita. Whereas, urbanization level and population can lead to a decrease in energy consumption per capita. An in-depth analysis on energy consumption of China’s recent urbanization is carried out and policy recommendations are put forward.