Search

The subsector of pulp and paper presented in 2013 an electric power consumption of 9.3% of electricity consumption in the industrial sector in Brazil. It experienced a significant growth of production in the last 20 years, exceeding 160%, and their consumption of electricity increased by 130%, reaching the level of 19,594GWh. Due to its characteristics, with a known structure and production processes, and its importance in the country's industrial sector, this subsector was selected for study in this paper, to evaluate the behavior of its long-term annual electricity demand, at the horizon of 20 years ahead, in energy efficiency scenarios through bottom-up approach. This approach is data intensive and requires data with good quality; it consists of a hierarchical structure that enables to check how specific actions can affect the results at each level and its effect on higher levels. [ABSTRACT FROM AUTHOR]

This study considered the possibility of reducing the environmental footprint of paper and pulp industry by producing bioenergy from paper sludge by using process wastewater instead of fresh water, and reclaiming water trapped in paper sludge. Experimental studies are conducted with streams from three different pulp and paper mills (virgin pulp mill (VP), corrugated recycling mill (CR), tissue printed recycling mill (TPR)) for sequential bioethanol and biogas production with simultaneous reclamation of water from paper sludge (PS). Total energy yields of 9215, 6387, 5278 MJ/tonne dry PS for VP, CR and TPR, respectively, were obtained for ethanol-biogas production. Virgin pulp paper sludge gave the highest yield for ethanol and biogas in stand-alone processes (275.4 kg and 67.7 kg per ton dry PS respectively) and also highest energy conversion efficiency (55%) in sequential process compared with CR and TPR. Energy and environmental case study conducted on virgin pulp mill has proven the possibility of using paper sludge bioenergy to reduce energy demand by 10%, while reclaiming 82% of the water from the PS, reducing greenhouse gas emissions (GHG) by 3 times and producing solids suitable for land spreading. Integration of a sequential bioprocessing technology into the pulp and paper industry treats wastewater, produces bioenergy and eliminates the need to landfill paper sludge. [Display omitted] • Process wastewater substituted clean water in fermentation and anaerobic digestion. • Sequential bioprocessing reclaimed 65%−85% of entrapped water from paper sludge. • 80% of GHG emissions from landfills is avoided in paper sludge bioprocessing. • Integration of paper sludge bioprocessing in pulp mills can reduce energy demand by 10%. [ABSTRACT FROM AUTHOR]

This paper provides a concise overview of research and techniques aimed at enhancing the performance of wind power systems, with a focus on mitigating power quality concerns. Although wind energy has become a more substantial component of the world's energy mix, there are still concerns because wind resources are naturally variable, especially when it comes to electricity quality. The paper highlights the potential of wind energy in reducing greenhouse gas emissions and meeting rising electricity demand. In order to improve overall effectiveness in wind power generation, the study emphasizes optimization methodologies and emphasizes the significance of control systems for wind turbines in maximizing power output, adjusting to changing wind conditions, and addressing power quality issues. A grid connected system incorporating the wind power system network along with D STATCOM is modelled with proportional-integral (PI) controller and detailed comparative total harmonic distortion (THD) analysis has been done for various faults. The STATCOM is tuned with genetic algorithms (GA), ant colony optimization (ACO), and harmony search (HS) based PI controller as a control device to achieve the desired power quality. Leveraging ACO for mitigating THD in wind systems under fault conditions holds promise for substantial enhancements in power quality. This approach has the potential to safeguard equipment, improve system reliability, and contribute to overall performance. [ABSTRACT FROM AUTHOR]

Renewable sources and Distributed Generation (DG) have been generating a growing economic interest given the increase in electricity consumption. For the end consumer, the lower environmental impact, easy-to-install and quick payback are great alternatives to traditional connections. DG growth drives new studies to predict different results in the electrical grid. The IEEE 1547 technical guidelines bring the possibility that in case of any failure that causes a shutdown, the operation is possible through intentional islanding provided by the electrical utility, continuing service, and maintaining customer satisfaction. For a more in-depth analysis of the impacts of this scenario, this paper contributes with a proposal to modify the strategy for identifying possible intentional islanding. A hybrid relay was modeled using passive techniques along with a suggestion for the operation of the newly formed Microgrid (MG), presenting a control philosophy of the regulators connected to the grid or being islanded, the latter defining the functions of the DGs as master–slave. The results obtained by the experiments proved to be excellent when inserting loads while the intentional islanding event occurs, confirming that the model is within the technical guidelines of IEEE 1547. This paper brings exciting conclusions about this operation, and the power quality of the electrical generator in the MG, according to the criteria established by ANEEL's Distribution Procedures Guidelines (PRODIST). [ABSTRACT FROM AUTHOR]

In more recent years Asia has had the highest level of investment in renewables and the highest level of electricity generation from renewable sources. This paper analyzes the determinants of renewable energy consumption in Asia and the Pacific from 1996 to 2018. It explores the major challenges this region has faced in moving towards a more environmentally friendly generation and use of energy. We find that the renewable electricity output, GDP per capita, merchandise trade, the value added of the manufacturing sector and the income share held by the poorest population boost renewable electricity consumption. On the other hand, energy use per capita, access to electricity, energy imports, broad FDI, population growth and the income share held by the richest population lower consumption of electricity from renewable sources. Trade openness and being an island do not seem to have a statistically significant impact in our sample over the observed period. [ABSTRACT FROM AUTHOR]

To reflect the future electricity demand variations in the industrial sector of western China under the "carbon peaking" and "carbon neutral" strategies, that the traditional methods of electricity consumption forecasting are no longer effective, this paper proposes a new hierarchy of electricity demand influencing factors based on the transition path of low-carbon and CO 2 emission intensity constraints of the industrial sector. Then, the feedback equations of the influencing factors based on CO 2 emission intensity is constructed, and a long-term electricity forecast model based on system dynamics is established. Finally, taking Ningxia Province as an example, the electricity demand and carbon emissions of the industrial sector are predicted under different policy constraint scenarios. The results show: (1) In the baseline scenario, electricity demand will tend to saturate with about 140.1TWh in 2030, and the CO 2 emission level of electricity demand will peak at 72.52 million tons in 2029. (2) Compared with the system dynamics model without considering CO 2 emission intensity, the mean absolute percentage error is reduced about 3.16 % by inputting the influence factors and regression coefficients designed in this paper. (3) Compared with other forecasting methods, the model proposed in this paper has the lowest mean absolute percentage error of 2.75 %. The model improves the accuracy and provides a reference for planning departments to develop energy transition plans, where the prediction results reflect the reality of the industrial sector in western China. Correspondingly, this model has reference value for relevant institutions in forecasting the electricity demand under the low-carbon development path in industrial sectors of different regions. • Influencing factors are screened according to the low-carbon transition paths. • The distributed power installed capacity and load interaction coefficient are considered. • Multiple regression equations are developed between CO 2 emission targets and influencing factors. • A long-term electricity demand forecast model driven by CO 2 constraints is constructed. • The multi-scenario electricity demand in western China is forecasted in response to carbon peaking and carbon neutral targets. [ABSTRACT FROM AUTHOR]

China has witnessed 556 million people migrating to urban areas over the past three decades with the process of urbanization, which has brought enormous pressure on electricity consumption and urban sustainable development. This paper establishes the generalized Divisia Index Decomposition Method (GDIM) model, and aims to reveal the electricity consumption mechanism of urban residents in China between 2000 and 2018. The results show that residential income, per capita electricity consumption and residential appliances are the main driving factors for the growth of urban residential electricity consumption, while the energy-saving willingness and expenditure structure have the inhibiting effect. With the expansion of urban residential income, the increase in income and air-conditioning numbers will continue to promote the growth of urban residential electricity consumption. Then, this paper puts forward relevant suggestions to alleviate the pressure of electricity demand and sustainable development from urban residents in China. [Display omitted] • The growth of power consumption in residential sector mainly comes from urban areas. • Establish GDIM model to decompose urban residential electricity consumption in China • Income has the greatest driving effect on urban residential electricity consumption. • Air-conditioning is the main source of the effect of household appliances. • Energy-saving willingness inhibits urban residential electricity consumption. [ABSTRACT FROM AUTHOR]

In this paper, a hybrid fuzzy-based method is suggested for determining India's best system for power generation. This suggested approach was created using a fuzzy-based combination of the Giza Pyramids Construction (GPC) and Recalling-Enhanced Recurrent Neural Network (RERNN). GPC is a meta-heuristic algorithm that deals with solutions for many groups of problems, whereas RERNN has selective memory properties. The evaluation of the current load requirements and production profile information system is the main objective of the suggested method. The Central Electricity Authority database, the Indian National Load Dispatch Centre, regional load dispatching centers, and annual reports of India were some of the sources used to compile the data regarding profiles of electricity loads, capacity factors, power plant generation, and transmission limits. The RERNN approach makes advantage of the ability to analyze the ideal power generation from energy data, however the optimization of RERNN factor necessitates the employment of a GPC technique. The proposed method was tested using MATLAB, and the findings indicate that it is effective in terms of accuracy, feasibility, and computing efficiency. The suggested hybrid system outperformed conventional models, achieving the top result of 93% accuracy with a shorter computation time of 6814 seconds. [ABSTRACT FROM AUTHOR]

This paper has sorted out the general logic of the impact of COVID-19 on energy consumption. In the short term, the epidemic has forced governments to adopt different levels of lockdown measures. The total electricity consumption decreased and the share of renewable energy has increased. Based on the International Energy Agency's (IEA) data on electricity production and consumption, this paper analyzes the long-term effects: The COVID-19 pandemic does affect overall electricity demand and market structure in the short term, but in the long term, the impact of the pandemic is detrimental to the proportion of electricity generated from renewable sources. Increased, especially in hard-hit countries. The impact on countries with different levels of development is different, and countries with higher levels of economic development are better able to resist such exogenous shocks. The pandemic is still ongoing, how to steadily promote sustainable development? In this paper we suggest that to cope with climate change, to accomplish energy transformation and upgrading or even sustainable development, we should not only rely on reducing human activities, but give more support to countries with low development levels in realizing sustainable development. [ABSTRACT FROM AUTHOR]

The ultra-short-term electricity demand forecast is very crucial to the operation of power grid. This paper analyses the hourly electricity demand in a functional data framework and uses the functional nonparametric prediction method to forecast the hourly electricity demand of the next day. The results indicate functional nonparametric prediction method performs better than traditional time series model (seasonal-ARMA). This paper expands the application field of functional data analysis. This paper also introduces some future research directions under this topic. [ABSTRACT FROM AUTHOR]

The total electricity consumption (TEC) reflects the operation condition of the national economy, and the prediction of the total electricity consumption can help track the economic development trend, as well as provide insights for macro policy making. Nowadays, novel neural networks provide a new perspective to predict the total electricity consumption. In this paper, a time series forecasting method based on Transformer model, Trans-T2V model, is proposed and applied to TEC forecasting. Transformer is the first network structure that completely relies on self-attention to calculate input and output. In this paper, the Time2vec method is used to improve the existing Transformer model, as embedding the month sequence more efficiently in the Transformer model. By comparing with the existing Transformer models and other intelligent algorithm models, the robustness and superiority of the proposed method framework are verified, and the highest accuracy reaches 97.36%. The method presented in this paper provides valuable insights in the field of time series prediction. [ABSTRACT FROM AUTHOR]

The research and investment community seems to ignore the long-term sustainability of Bitcoin, which is reflected in four flaws: transaction fees, miners' revenue, concentration and electricity consumption. While most of the authors have aimed to examine one topic at a time, with a particular interest in electricity consumption and carbon footprint, the aim of this paper is to examine all these issues simultaneously to provide a more comprehensive view on long-term sustainability of Bitcoin. This paper looks at these flaws and reveals why Bitcoin is not sustainable in the long run, how decentralization is being lost, how the design is putting artificial and unrealistic pressure on the ecosystem, while all being powered by an unjustifiable amount of dirty electricity sources. Our main findings are as follows. Firstly, transaction fees are already high and set to increase in time, further discriminating small transactions against big ones. Secondly, miners' revenue comes mostly from the block reward. The block reward is the main income source for miners, but is set to be cut on a regular basis, making miners' revenue not sustainable in the long run. Thirdly, miner concentration is already an issue, with a possibility of deepening even more and diminishing the idea of decentralization. Fourthly, the high electricity demand and the associated carbon footprint thus cannot be justified by any means. We deem our results useful for overall policy and regulatory implications. [ABSTRACT FROM AUTHOR]

• Critical challenge in developing countries. • Limitations of demand flexibility. • Innovative multi-strategy approach with community engagement. • Quantified benefits and sensitivity analysis. Electricity shortage remains a critical challenge in developing countries striving to achieve the United Nations' universal electricity access goal by 2030. Limited fuel availability and limited funds for renewable energy investments, while demand flexibility is implemented to regulate electricity access, it faces limitations due to voluntary participation, rotating load-shedding schedules, and lack of electrification in certain communities. This research paper proposes a multi-strategy approach as an alternative solution by scheduling essential appliances and optimizing power flow in distribution grids. The article aims at community engagement and utility collaboration to improve electricity access. This paper proposes a multi-strategy framework for regulating social organization and utility operations, and attaining energy conservation, efficiency, and incentives. Optimization tool is developed to extend benefits of optimal scheduling of end user devices under power supply shortage, uncertainty of demand and grid operational codes, using multiple strategies regarding to consumers' fairness of electricity access in a community, and maximum electricity access per customer and region. Such strategies are also compared with the case of customized appliance priority of preference. The results demonstrate that energy efficiency and economic sustainability indices can achieve 94 % and 29.5 % improvement, respectively, compared to conventional methods. [ABSTRACT FROM AUTHOR]

Frequent power outages constrain households' electricity consumption and lead to additional backup expenditures on top of their electric utility bills in many developing countries. This study analyzes how residential electricity consumers of different income levels would change their electricity consumption and backup decisions in response to electricity reliability improvements. Using a nationally-representative sample of Nepalese households, the findings point out that improved reliability is positively correlated with the probability of electric appliance ownership. The interaction of income and reliability-constraint indicators suggests that the unreliable electricity supply constrains households equally at all income levels. However, the results from an ordered probit model with three off-grid backup decision alternatives indicate that the improved reliability matters more for poorer households, for whom the combined ownership cost of both appliances and backup equipment may be prohibitive. Putting this paper's findings into an energy-policy perspective, a connection to the grid by itself does not necessarily translate to realized benefits from electricity consumption. The reliability of the service plays a critical role, especially for poorer households. • This paper investigates how households respond to reductions in power outages. • The probability of electric appliance ownership increases at all income levels. • Household income constrains both appliance ownership and backup decisions. • Therefore, poorer households benefit relatively more from a reliable grid supply. [ABSTRACT FROM AUTHOR]

This research aims to study the sustainability of Taiwan power supply chain based on system dynamics forecasting. The paper tries to investigate electricity shortage effects not only on the industrial side, but also from the standpoint of society. In our model, different forecasting methods such as linear regression, time series analysis, and gray forecasting are also considered to predict the parameters. Further tests such as the structure, dimension, historical fit, and sensitivity of the model are also conducted in this paper. Through analysis forecasting result, we believe that the demand for electricity in Taiwan will continue to increase to a certain level for a period of time in the future. This phenomenon is closely related to Taiwan's economic development, especially industrial development. We also point out that electricity prices in Taiwan do not match with high industrial demand, and that prices are still slightly low. Finally, the future growth trend of Taiwan's electricity demand has not changed, and ensuring adequate supply to meet electricity demand to prevent potential power shortages will pose some difficulty. [ABSTRACT FROM AUTHOR]

Anomalies in electricity consumption can have a significant impact on the efficiency and dependability of power systems. Therefore, precise and prompt anomaly detection is essential for ensuring efficient system operation. This research paper presents an in-depth analysis of various machine-learning approaches for detecting abnormalities in Indian electricity consumption data. Specifically, the performance of three widely used machine learning algorithms, namely eXtreme Gradient Boosting, (XGBoost) Long Short-Term Memory (LSTM), and Gated Recurrent Unit (GRU), is evaluated. The efficacy of each technique is assessed using performance indicators, including mean absolute error (MAE), mean squared error (MSE), and coefficient of determination (R2). The findings of this study provide valuable insights into the most effective machine-learning methods for identifying anomalies in Indian energy usage data. Moreover, this research contributes to the advancement of power grid anomaly detection technologies, leading to more effective systems. The anticipated outcome of this effort is an increase in the dependability and efficiency of the electrical supply system, benefiting both power system operators and end users. [ABSTRACT FROM AUTHOR]

In recent years, along with the rapid development of economy and society, the quality of life of people has been greatly improved, the total amount of electricity consumption has increased rapidly, and the scale of power system has been expanding. With the rapid increase of China's electricity supply, the corresponding infrastructure construction cannot keep up with the development of electricity consumption, resulting in a huge electricity consumption gap. Especially in recent years, some regions of China are facing industrial and industrial transformation, urban re-planning and renovation, leading to the complication of the structure and operation of the power grid, as well as the problem of heavy load and overload of power-using equipment. This paper describes the concepts and principles of the data algorithm and the peak-limit method, and makes a comparison of the economic losses of eight regions before and after the optimization of the data mining (DM) algorithm through experimental analysis. The results verify the effectiveness of the algorithm in solving the peak-limit problem and prove that the algorithm is of great research significance. [ABSTRACT FROM AUTHOR]

In this paper, we investigate the effects of information and communication technology (ICT) on economic growth and energy consumption for a panel of 27 countries ranked top of the ICT Development Index, from 1990 to 2019. By employing the panel nonlinear autoregressive distributed lag (NARDL) model approach, we confirm the presence of a long-run asymmetric effect of ICT on economic growth and energy consumption. Specifically, negative changes (reduction) in the adoption and usage of ICT can have severe consequences on economic growth in these 27 countries in the long-run, erasing all previous gains from positive ICT changes. Additionally, positive or negative shocks to ICT in these countries increase energy consumption, indicating the rebound effect exists in these countries. Finally, the results also reveal that, while the economic growth in these countries is dependent on energy consumption in the short-run, it is energy independent in the long-run. Several important policy implications are drawn. [ABSTRACT FROM AUTHOR]

Increased use of renewable energy sources in energy sector as well as improvements and electrification in transportation sector significantly contribute to reduction of green-house gasses emissions and mitigation of problems with fossil fuel dependency. Optimal integration of electric vehicles (EVs) into the grids and their charging/discharging schedules have to be realized in accordance with electricity demand, dayahead electricity market prices and intermittency of photovoltaic and wind generators electricity production. A microgrid that includes non-deferrable loads, renewable energy sources, EV fleet and its charging station is analyzed in this paper. Its Vehicle-to-Grid (V2G) service is optimized with the aim of minimizing the operational costs and obtaining peak load shaving and valley filling of the load curve, which is especially effective in the case of EVs fleet with occupational time intervals known in advance. Optimized schedule of EVs charging and discharging is obtained as a result of the procedure that uses multi-criteria optimization function. These criteria include minimization of microgrid electricity costs as the local aggregator's benefit, maximization of the flattening of total microgrid demand curve as main grid operator's benefit, and minimization of battery degradation (due to a number of charging/discharging cycles) as EVs owner's benefit which is the novelty of this paper. Experimental analysis is performed on several scenarios and program Lingo is used to solve the optimization problem. [ABSTRACT FROM AUTHOR]

Electricity is fundamental for supporting economic development in emerging economies. Power generation from clean energy sources may help to meet societal needs such as increasing electricity demand while complying with contemporary global challenges (e.g. climate change). This paper aims at qualitatively describing the development of clean energy technologies in Mexico by using a multi-perspective analytical framework. By integrating a meta-theoretical framework for national energy transitions and core concepts from policy mixes for sustainability transitions literature, a series of stylised facts are developed to qualitatively describe the evolution of clean energy technologies in Mexico from 1879 to 2021. Interactive governance theory is used as an analytical framework to synthesise these storylines by highlighting the role and interactions among technologies, actors, and policy instruments in governance processes. The case study revealed that the ever-growing electricity demand in Mexico have called for exploring different available energy sources and technologies over time. Diminishing oil and gas domestic reserves, increasing concerns around environmental issues, and worldwide agreements on greenhouse gas emission reductions have been the main drivers for clean energy and energy efficiency development in Mexico. However, different barriers to a clean energy transition in Mexico have been identified and discussed in this study: (1) access to a vast oil and gas resources in the past reinforced the position of fossil-fuel incumbent regimes, displacing clean energy technological niches; (2) lack of investment in clean energy research, development, and innovation; (3) policy paradigms have shifted based on (dynamic) societal needs and socio-political interests in favour of fossil fuel power generation. To overcome these barriers and allow a transition towards a clean energy electricity system in Mexico, multiple policy instruments have been identified and discussed. • The paper describes the evolution of clean energy technologies in Mexico. • A multi-perspective analytical framework is used to create micro-logics. • The interactive governance approach is used to synthesise these micro-logics. • Barriers and drivers for a clean energy transition in Mexico are discussed. • Energy policy mixes towards a clean energy transition are discussed. [ABSTRACT FROM AUTHOR]

• The center of gravity of China's EPC is located in Luoyang City, Henan Province. • During the study period, China's EPC in general exhibits an east-high and west-low trend. • During the period 2000–2019, there are significant regional differences in the strength of the spatial linkage of EPC in China. • In 2000–2019, the decoupling status of EPC in china is of two types of decoupling: weak decoupling and expansive coupling. Exploring the evolution of spatial and temporal patterns of electricity power consumption in China is of great significance and value for the promotion of Carbon Peaking and Carbon Neutrality Strategy and the scientific allocation of electricity resources. Therefore, this paper takes China as an example and innovatively applies nighttime light data from a combination of two remote sensing satellites. And using spatial analysis methods to analyze the evolution of spatial and temporal patterns of electricity power consumption over a longer study time series. Meanwhile, this paper also uses the Tapio decoupling model to study the decoupling effect of electricity power consumption and economic growth in China. The results of the study show that, during the period 2000–2019, China's electricity power consumption was in an unbalanced state, with the eastern coastal region dominating. It also formed a gradient distribution from the coastal area to the inland area. In terms of spatial connection, it shows an unbalanced distribution pattern of "low in the west and high in the east". In addition, the spatial linkage of electricity power consumption among provinces is more than 93 % of weak linkage, and gradually increases to 97 %, indicating that the spatial linkage of electricity power consumption among regions is gradually weakening. The decoupling state of electricity power consumption in China is characterized by two types of decoupling: Weak decoupling and Expansive coupling. The decoupling status has experienced a significant change from 84 % of provinces being Expansive coupling to 100 % of provinces being Weak decoupling. [ABSTRACT FROM AUTHOR]

Solar Home Systems (SHSs) have become a popular means of electrification for millions of people living in rural regions of developing countries with no access to the national grid. As the electrical demand grows, however, there are economic and technical challenges that exist in scaling-up SHSs. At present, up to 70% of the energy generated by SHSs is surplus and effectively goes to waste, due to high solar resource peaks aligning with low demand periods and restricted storage capacity. Interconnecting existing SHSs to form 'DC village' microgrids, comprised of distributed photovoltaic generation and battery storage, enables communities to unlock this surplus energy for more productive uses. This may be to service small businesses with higher energy demand than can be provided by a typical SHS (e.g. to shops or barbers), or to allow domestic consumers to connect higher powered appliances. Meanwhile, people without their own SHS can also connect to the system introducing basic lighting and phone charging for lower capital costs than those with SHSs. This paper presents the results of a field trial conducted in Murambi Village, in the Northern Province of Rwanda, which involved the interconnection of seven households with their own SHS and one with no previous access to electricity. An innovative smart controller developed by the authors to facilitate SHS interconnection was deployed across this village. Using a mix of survey and focus group methods, the paper examines how people experienced the interconnected SHS system; the opportunities and benefits (social, economic) this presented; how the system enabled the use of new (higher power) appliances and new practices by trialing a novel shared refrigeration unit; and the potential of SHS interconnection in the broader context of the 'bottom-up electrification' paradigm. • Introduction of Interconnected Solar Home Systems Networks in Sub-Saharan Africa. • Stimulating productive use of electricity in the Developing World. • Engaging highly distributed prosumers to form local energy networks. • Increasing penetration of high-power appliances in rural Sub-Saharan Africa. [ABSTRACT FROM AUTHOR]

Nowadays, different types of dispatchable and non-dispatchable distributed energy resource (DER) units, combined heat and power (CHP) plants as well as heat only units are integrated into a micro-grid (MG) platform. These facilities can help MG operators to satisfy the required electricity and heat demands of controllable and non-controllable loads even with no power exchange between the external main grid and the MG. This paper introduces a network constrained optimal operation model based on an integrated active-reactive and heat powers scheduling in MG for both grid-connected and isolated modes. The DERs like fuel cell (FC) units, diesel generators (DGs) and micro turbines (MTs) are motivated to take part in providing reactive power to the MG based on a reactive power payment function. Meanwhile, the dependency of produced heat and active power by CHP units through feasible operating region as well as the dependency of produced active and reactive powers by DER units through capability curves are taken into account in the optimal economic dispatch of the MGs. The main goal of this paper is to find the optimal economic dispatch of active and reactive powers as well as heat energy for DER and CHP based micro-grids in which network constraints such as AC power flow limitations are taken into consideration. The proposed model is examined on a 33-bus test MG for grid-connected and isolated modes and the simulation results are presented to investigate and analyze the different features of the proposed method. [ABSTRACT FROM AUTHOR]

Data on potential electricity demand for the future is a fundamental necessity for forecasting and setting up the national grid extension. The lack of data in Malawi has resulted in the existence of enormously sparse literature on electricity demand modeling. A handful of studies on total electricity demand modeling exist in the African context and none for Malawi. The study is concerned with modeling possible future paths for Malawi's electricity future. The study aims at linking the current gap in the literature of electricity demand forecasting, at least partially, by projecting the total electricity demand for Malawi for a long-term duration and modeling alternate electricity supply prospects, together with scenarios and providing policy insights on a variety of user-defined assumptions of which can be applied in other African countries with similar challenges. This study employed a projection methodology using demographic, economic, and government policy trends with a pragmatic structure that uses bottom-up analysis. Electricity use scenario analysis focuses on government policy targets by 2030 at 15 %, 30 %, and 50 % electrification rate plus Growth Domestic Product (GDP) growth at 5 %, 6.5 %, and 7.2 % for three scenarios namely: low, base, and high, respectively. The base and high scenario outcomes indicate an overall electricity consumption growth of 165 % and 218 % by 2030 compared to the low scenario in the government of Malawi's (GoM) outlook, about 4776.78 GWh and 6319.05 GWh. The paper provides insights into the status of Malawi's electricity sector, policy influence, and recommendations regarding the "next steps" in electricity security analysis to create an understanding among various planners and stakeholders in the power sector. • Employed a methodology for modeling electricity demand in Malawi. • Presents overall synopsis of electric power consumption including sector-wise electricity demand forecasts. • Established a national-scale electricity plan for the period 2020–2030 for Malawi. • Provides insights on policies and recommendations regarding "next steps" in electricity security analysis [ABSTRACT FROM AUTHOR]

The paper presents the types of batteries used to store electricity. The operating principle, advantages, disadvantages and areas of use are mentioned based on a study of the specialized literature. A comparison of the energy efficiency of different types of batteries is also presented, with end users able to make a choice knowing the cause. [ABSTRACT FROM AUTHOR]

The demand for electrical energy continues to rise at an alarming rate, while supply is not proportional to power demand. Thus, in order to accomplish appropriate preparation and operation of an electrical power system network, it is crucial to properly assess the present and future consumption of electric power systems. Meeting the electric load needs entails actively anticipating the amount of energy required to meet the subscribers' power demand. To forecast energy demand, many algebraic and artificial intelligence schemes have been used. This paper proposes an improved medium-term forecasting scheme that employs an artificial neural network (ANN). The model was developed and trained with the help of an artificial neuro-fuzzy inference system (ANFIS). The data used, which ranged from 2014 to 2019, was obtained from the University of Nigeria Nsukka's (UNN) research and energy Centre, and the works department of the same university. The model was designed and simulated using MATLAB 2018a, and the results were evaluated using MAPE. The proposed system used a robust machine learning based scheme. Unlike conventional systems, the system demonstrated a MAPE of 4.34% and a correlation of 0.91144315 at predicted and actual loads, as shown in Table 2. The proposed system shows that the predicted loads has 25th and 75th percentiles of 0.58MW and 0.98WM, while that of the actual loads are 0.649MW and 0.936MW. [ABSTRACT FROM AUTHOR]

The paper discusses whether the electricity trade in South East Europe in 1990-2019 is driven by fundamentals or, like other commodities, one could argue for a speculative movement of electricity flows motivated by factors such as prices. We employ a panel structural gravity model on the example of Bulgaria, Romania, Serbia, Republic of North Macedonia, Greece, Turkey, and Hungary to study the dependence between net exports, generation, and consumption of electricity, as well as the regulations in the field of exporting electricity. We find that domestic consumption of electricity is a much stronger factor than the generation when considering foreign trade in electricity. The construction of power capacity in the countries considered is much more oriented towards the domestic market, which results in an underdeveloped interconnection between them. This is a serious obstacle to the liberalization on the electricity market in the region and its integration into pan-European market structures. It also creates conditions for maintaining higher and volatile electricity prices and, consequently, negatively affects economic development. [ABSTRACT FROM AUTHOR]

Mini-grids are discussed as a cost-effective and sustainable solution to provide electricity to populations lacking access, of which the majority reside in sub-Saharan Africa. Projects struggle to be fiscally solvent, however, in part because electricity demand from rural households is low relative to installed mini-grid generation and distribution capacity, making it difficult to recoup investment costs. This paper explores how productive uses of electricity may increase electricity demand and evaluates impacts of increased electrical load to the technical and economic metrics of mini-grid projects. A full cashflow analysis is completed to evaluate business model efficacy for mini-grid developers and investors for a case study of a township in Sierra Leone. Productive uses of electricity are shown to have minimal improvement to the financial viability of residential mini-grid projects, and subsidies are required to reach financial metrics suitable to attract private investment. Of the productive uses investigated, year-round loads had the most positive impact to payback periods. Seasonal loads offer only small improvements to payback period and internal rate of return because they require extra generation assets to meet peak demand in the high season that are left idle or heavily under-loaded during the low season. Overall, the study finds the most effective way to improve financial outcomes through productive uses is to increase the mini-grid load factor with productive uses of electricity that operate year-round. • PUE interventions that increase mini-grid load factor improve project financials. • Higher load factors better utilized hybrid systems with generator, solar, storage. • PUE type and load profile significantly affects mini-grid design and financials. • Constant-load PUE types improved financials over seasonal loads. • Seasonal PUEs increase capital costs that go largely unused during the off-season. [ABSTRACT FROM AUTHOR]

This paper estimates electricity consumption and subsidies for each of the Mexican households surveyed in the ENCEVI and the ENIGH in 2018. Using information from the Mexican Utility Federal Electricity Commission (CFE, Comisión Federal de Electricidad), we expand these two datasets into a comprehensive source of electricity consumption for 100,000 households representative of all of Mexico. Our estimates show that large consumers receive large subsidies, with the top 20 % of the highest consuming households receiving 50 % of the subsidies. We also find that electricity consumption is determined by socio-economic status. In the extremely hot region, families in the highest income decile consume 57 %–66 % more than those in the lowest. For this reason, Mexicans in the highest income brackets benefit the most from subsidies; the average subsidy per household for those in the two highest income brackets is almost twice the average subsidy for those in the lowest bracket. • A comprehensive dataset with estimates of electricity consumption and subsidies in the Mexican residential sector was created • The residential electricity consumption in Mexico varies significantly by climatic region, socioeconomic status, and season. • Without subsidies, the electricity burden of households in the lowest income deciles would increase approximately 2.2 times. • Almost forty cents of every dollar spent on electricity subsidies is allocated to households with the highest income quintile. [ABSTRACT FROM AUTHOR]

A large part of the energy for heating and cooling is consumed due to human needs for thermal comfort in the living environment. It is important to use a suitable method to design an energy-efficient building to satisfy the thermal comfort of residents throughout the year. This paper focuses on the implementation of the sensitivity analysis in the area of energy analysis of a prefabricated timber building on box models. The sensitivity analysis plays an important role in early planning, partially also presenting the energy analysis. A combination of the building energy simulation and the sensitivity analysis technique identifies key variables by order, which can either have a strong or insignificant impact on the further design of the energy efficiency of a building. Moreover, this paper presents a sensitivity analysis method performed on the case study of six different timber building models where design parameters, such as geometry, insulation features, glazing ratio and distribution, shading control and interior set-point temperatures, are varied in environments with different climatic conditions: moderate (Ljubljana), warm (Athens), and cold (Helsinki). The sensitivity analysis for a range of variable input parameters is conducted by using the global sensitivity analysis technique, i.e. the Morris method, which is based on the elementary effect. The main objective of the article is to provide guidance for researchers, professionals and building planners about the parameters they should focus on in the early planning of an energy-efficient timber building to reduce the energy required for heating and cooling, thereby ensuring constant thermal comfort in the living environment. The results of the sensitivity analysis, which serve as guidelines, show that the overall U-factor of a window, the solar heat gain coefficient, and a set-point temperature for heating are design variables that most affect the heating energy demand for the considered one- and two-storey box models in the three locations. The dominant parameters have on average 30% more influence on heating energy demand in cold climates than parameters in moderate climates and 88% more than in warm climates. While shading control and solar heat gain coefficient are the most influential parameters for cooling energy demand, with the dominant parameters in warm climates having on average 39% more influence than parameters in moderate climates and 49% more influence than parameters in cold climates. In the total energy demand for the operation of all box models under all climatic conditions, special attention should be paid to shading control. In addition, in moderate and cold climates, the overall U-value of the window and the internal set- point temperature in winter must be considered, and in hot climates, the solar heat gain coefficient is important. In the case of the two-storey box model, designers need to be careful about the glazing-to-wall ratio which depends on the orientation of the glazing (south-north or east-west). In this case, the dominant parameters have on average 47% more influence in warm climates than in cold climates and 56% more than in temperate climates. • Prefabricated timber-framed box models located in cold, moderate and warm locations were introduced. • Architectural parameters that minimize the total energy demand are searched. • The Morris method was used to identify the most influential parameters. • A guideline for early design of energy efficient timber buildings under different climatic conditions was developed. • Window characteristics, AGAW, shading and set-point temperature in winter have the strongest influence on energy demand. [ABSTRACT FROM AUTHOR]

Accurate modelling of household electricity on a large scale is a cornerstone for demand management and decarbonisation efforts. This paper introduces a data-driven model for South African households. According to the Paris Agreement, a 50 % reduction in global CO 2 emissions is required to ensure the global average temperature does not surpass 2 °C above pre-industrial levels. In this context, 26 % of the overall European energy consumption is used by the residential sector and the global energy consumption is estimated to increase by 1.3 % on average per year until 2050. In the last decade, the development and implementation of smart grid technologies have grown to efficiently and cost-effectively meet the electricity demands of the grid and mitigating greenhouse gas emissions. We present a data-driven, enveloped sum of Gaussians-based model and residential household synthesiser that statistically models the household's electricity usage demand based on smart meter data and generates synthetic data that accurately represents the actual demand profile, load peaks, and daily variances for an individual or aggregate group of households. The measured data was gathered over a one year period for 1200 households in South Africa. Our model accounts for temporal variations such as seasonality and the day of week, household uniqueness, and is fully autonomous. Our results show that the root mean square error between the aggregated measured and synthetic electricity profiles is 0.181 A (5.68 %) and the total energy of each profile is 75.9 and 76.3 A·h, respectively. [ABSTRACT FROM AUTHOR]

Making materials consumes about 21% of the global energy demand; concerning metals production, it accounts for about 8% of total global energy consumption. Circular economy strategies such as longer life, more intense use, repair, product upgrades, modularity, remanufacturing, component reuse, and open/closed-loop recycling are strategies to put in place urgently to reduce the environmental impact of raw material production. Although recycling of metals is the most used strategy and is being improved in terms of efficiency, it is mandatory moving towards more virtuous circular economy strategies, such as product/component reuse. In this paper a novel reuse strategy for sheet metal based End-of-life components is proposed; to be more specific the suitability of Single Point Incremental Forming (SPIF) to reshape End-of-life aluminum components is presented. An experimental approach is used to change the shape of a square box, previously obtained by deep-drawing processes, by means of single point incremental forming processes. The paper aims at outlining the environmental impact of the proposed reshaping approach presenting a Cumulative Energy Demand and CO 2 -eq. emissions comparative analysis. Three different routes are analyzed and compared to one another: SPIF based reshaping, conventional recycling and Solid State Recycling (SSR) routes. The SPIF based approach proved to be the most environmentally friendly strategy, the energy and emissions saving are ascribable to the fewer process steps and the less material involved with respect to the analyzed recycling strategies. [ABSTRACT FROM AUTHOR]

The smart meter is an essential part of an intelligent grid system. Defects in the LCD screen the smart meters affect their use. Therefore, detection of LCD screen defects of smart meters is of great significance for management and use of smart electricity meters. At present, detection methods are mainly realized by manual detection and automatic detection based on machine vision. However, performance of these two methods is not satisfactory. The fault detection task of a smart meter LCD screen can be divided into two parts: smart meter LCD localization and LCD fault detection. Therefore, this paper proposes a twostage system based on deep learning, which combines YOLOv5 with ResNet34. YOLOv5 is used for smart meter LCD localization and the classification network based on ResNet34 for LCD fault detection. We have constructed an LCD screen localization dataset and an LCD screen defect detection dataset to train and test our model. As a result, our model achieves a defect detection accuracy of 98.9% on the dataset proposed in this paper and can accurately detect the common defects of an LCD screen. [ABSTRACT FROM AUTHOR]

In recent times, much more attention has been focused on the usage of wind power to meet the present day electricity demand. Due to its vast availability and its cleanliest form, wind power got importance throughout the world. But, the intermittent nature of wind availability creates chaos during the scheduling of units. Therefore, the wind power generation in large scale brings great challenges to the power system operators. Hence, accurate and precise wind power forecasting plays a vital role to overcome the challenges during planning and operation of the power system. This paper uses a single-hidden layer feed forward neural network called Extreme Learning Machine algorithm (ELM) and its hybrid version Wind Driven Optimization based ELM (WDO-ELM). The historical data of meteorological parameters are considered as input parameters to forecast future wind speed. The performance of the proposed algorithms are evaluated in terms of Mean Absolute Error (MAE) and Root Mean Square Error (RMSE). From the obtained results, it is observed that the proposed algorithm outperforms and can be utilized for the real time applications. [ABSTRACT FROM AUTHOR]

Implementing a proper clustering algorithm and a high accuracy classifier for applying on electricity smart meter data is the first stage in analyzing and managing electricity consumption. In this paper, Random Forest (RF) classifier optimized by Artificial Bee Colony (ABC) which is called Artificial Bee Colony-based Random Forest (ABC-RF) is proposed. Also, in order to determine the representative load curves, the Convex Clustering (CC) is used. The solution paths generated by convex clustering show relationships among clusters that were hidden by static methods such as k-means clustering. To validate the proposed method, a case study that includes a real dataset of residential smart meters is implemented. The results evidence that the proposed ABC-RF method provides a higher accuracy if compared to other classification methods. • To investigate the application of convex clustering on smart meter data. • To present ABC-RF as an accurate classifier for smart meter data • To introduce a demand response approach based on the proposed data mining technique. [ABSTRACT FROM AUTHOR]

This paper aims to generate electricity, by decreasing fossil fuels consumption and conserving electricity for further use. In this project, the method is to produce pollution-free electricity using the technique i.e. piezoelectric effect technique. By applying mechanical stress electric charge is generated known as the piezoelectric effect. A sufficient amount of energy is produced as it can reduce the damage of pollution caused by power plants. So to make the use of moving vehicles on road, power generated helps the environment last longer. Different shape, proof mass geometries are analyzed and plotted a graph between frequency versus voltage and electrical energy. Among them, it is identified that stacked cylindrical block has a good Voltage of 2.04mV (milliVolts = 10-3V) and Energy of 21.2fJ (femtoJoule = 10-15J). [ABSTRACT FROM AUTHOR]

The progressive fall in the cost of residential photovoltaic (PV) systems opens the possibility of evaluating systems that do not export surpluses to the grid, avoiding bureaucratic procedures and effects on the grid. The main purpose of this papers is to present a techno-economic model based in the context of Dominican Republic, to evaluate the profitability of residential PV systems, considering a non-incentivized self-consumption scheme, a step tariff of electricity and monthly electricity consumption. The methodology consists of maximizing the net present value (NPV) through the number of PV panels; subsequently, the simple investment recovery time (SPBT), the internal rate of return (IRR), the self-sufficiency rate (SSR), the self-consumption rate (SCR) and others quantities are obtained. In addition, a methodology is proposed to determine the self-consumed energy based on daily power demand profiles, differentiating between regular and irregular loads. For a domestic house with an average monthly energy of 579 kWh, the optimal value of the NPV was 535 USD with a SPBT of 4.83 years and a IRR of 20.8%. The results indicate that non-incentivized self-consumption with PV systems can be profitable in the residential sector of Dominican Republic. • Two definitions of daily profiles of residential power demand were proposed to size the PV system. • With the proposed model, the profitability of a PV system for a house was studied. • The internal rate of return on investment was 20.8%, which is more favorable than most of the rates offered in Dominican Republic. • The optimized NPV of the investment was $535 with a payback time of 4.83 years. [ABSTRACT FROM AUTHOR]

Demand-side management of energy seeks to foster energy efficiency investments and curtailment behaviour in households. The role of environmental concern and knowledge for both types of energy saving behaviour has hardly been investigated in middle income countries with growing middle classes and rising electricity demand. Drawing on unique household survey data from Ghana, Peru and the Philippines, this paper analyses the links from individual motivation to behaviour, and from behaviour to the impact on households' total electricity expenditures. We find that consumers with more environmental concern are more likely to adopt curtailment behaviours, but that concern does not influence energy efficiency investments. In turn, higher levels of environmental knowledge make households' energy efficiency investments more likely, but do not influence curtailment. Neither energy efficiency investments nor curtailment behaviours significantly impact households' electricity expenditures. Small differences between Ghana, Peru and the Philippines exist. • Energy consumption behaviour of middle class households in emerging economies requires more attention. • Levels of environmental knowledge matter for energy efficiency investments. • Levels of environmental concern matter for curtailment behaviour. • Sociodemographic factors are the main predictors of electricity expenditure. [ABSTRACT FROM AUTHOR]

In this paper, we study the performance of the electricity company of Mauritius, the Central Electricity Board (CEB). We undertake a comparative analysis with other island companies in similar environmental conditions. We mainly rely on data available on annual reports for the CEB and four other companies operating in islands: EAC (Cyprus), CEM (Macao), EEM (Madeira), and MEA (Isle of Man). The period under study covers the years 2000-2010 and uses the Malmquist Index and Data Envelopment Analysis. For the CEB, results show that productivity change is driven mainly by technical change. CEB operational costs grew during the study period due to the increasing price of fossil fuel used in the generation of electricity. Companies less dependent on thermal production were less exposed to increasing costs. [ABSTRACT FROM AUTHOR]

The Irish government is implementing policies to transition Ireland to a low carbon and environmentally sustainable economy by 2050. Ireland has sectoral targets of 600,000 installed heat pumps by 2030, currently roughly 28,000 are installed. Such a high target of heat pumps will not only have a significant effect on electricity demand but also on the management and operation of the grid. In this paper we explore the demand from homes heated by air source heat pumps using an innovative dataset from a field trial in Ireland. To assess the impact of large-scale adoption of heat pumps, this paper estimates the after diversity maximum demand per heat pump heated home. In particular we explore statistical distributions to best model coincident demand, and estimate after diversity maximum demand per home. We use the software package RStudio to model several different distributions. Based on goodness-of-fit statistics and criteria, a Gamma distribution is the best fit. We apply our methodology to data from a similar heat pump trial in the UK to complement our results. [ABSTRACT FROM AUTHOR]

This paper combines Value-Based Data Envelopment Analysis with Economic Input–Output tables to assess the eco-efficiency of the production and supply chains of the Electricity Sector in the European Union 28. We incorporate the preferences of a hypothetical decision-maker, assuming a value function that is framed in prospect theory, but with the zero-state employed as the reference state. Thus, the value functions used are normally concave for economic concerns and convex for environmental impacts. We therefore assume that some temporary and limited form of economic growth is expected from high-income countries, which enables low- and middle-income countries to meet their needs of economic growth. Furthermore, we consider that countries with already good environmental performance evaluate emission increases more critically. Overall, we concluded that Germany and France stand out, being at the top of efficient countries for all electricity chains. In general, the highest performance of eco-efficiency is attained by richer Western European countries and depends heavily on the Gross Value Added generated by the electricity sector. In contrast, most eco-inefficient electricity systems belong to low- and mid-income Eastern European countries and need to focus on improving their environmental performance. These findings are the result of the differing energy paths along the West-East European axis, and clearly envisage a shift on EU policy to specifically tackle the distinct attitudes towards the benefits of renewable energy. [ABSTRACT FROM AUTHOR]

The demand for electricity increases rapidly along with the advancement of the industrial age. To ensure efficient distribution of the electricity, maintain low losses and high level of quality, and the security of electricity supply, the smart grid concept was proposed. The concept enables a small, individual scale to generate electricity and sell it to the grid. However, the concept adds complexity to the existing system, such as how a transaction between these generators and consumers are conducted, verified and recorded. This paper proposes the blockchain as a tool to manage transactions in the smart grid. Transactions are performed with smart contracts, and the network acts as a transaction verifier. The blockchain provides immutability of the transactions, which ensure every transaction between generators and consumers will always be executed. It also provides immutability to transaction history, which can be used for audit or solving a transaction dispute. [ABSTRACT FROM AUTHOR]

Climate change is causing profound changes in energy and power systems around the world. Although many papers have confirmed the impact of environmental weather on electricity demand, the complex nexus of regional environmental temperature and electricity demand instability remains incomplete, especially neglecting the understanding from the perspectives of multi-quantiles. This paper uses a unique dataset of 15-min the high-frequency grid load situation of Datong, a northern city in China, to construct a comprehensive indicator to measure the regional electricity demand instability. Then, employing the Quantile-on-Quantile approach, this paper evaluates the association between intraday temperature difference and both the composite and multidimensional electric instability indicators. According to the empirical analysis, a larger environmental temperature difference always rises the instability of the electricity demand, which is stronger when both two variables are at higher quantile states. There are heterogeneities among the effects on multi-dimensional indicators of electricity demand instability. Environmental temperature difference provides a negative influence on the load rate, while it will magnify other instability indicators. Furthermore, we also assess the impact of daily extreme temperatures and divide the realized volatility of load into two directions for discussion. Our research presents important knowledge about responding the climate change-induced electric system risk. • This paper aims to reveal the relationship between climate change and electric system risk. • Using a unique dataset, a regional electricity demand instability indicator is constructed. • Quantile on Quantile approach is used to analyze quantile correlations. • A larger environmental temperature difference is harmful to stabilizing the electricity demand. • The impact of environmental temperature differences will be strengthened at higher quantile states. [ABSTRACT FROM AUTHOR]

Effective control of electricity consumption is crucial to achieving China's dual carbon goals, as it can significantly reduce coal consumption and associated carbon emissions. Using the Logarithmic Mean Divisa Index (LMDI) method, this paper constructs a two-stage decomposition model, which can divide total electricity consumption into industry and domestic electricity consumption and further examine the differential driving mechanism of the two types of electricity consumption separately. Taking the Yangtze River Delta as the research object, this paper analyzes the drivers of its temporal differences in total electricity consumption from 2000 to 2019 and fills the research gap in the identification of drivers of spatial differences in electricity consumption. The results showed: (1) Industry electricity consumption is the main driver of the increase in total electricity consumption, with economic growth and domestic electricity intensity being the primary and secondary factors behind this trend. The population scale also contributes to the increase. However, the effects of industrial structure, industry electricity intensity, and urbanization on total electricity consumption are not constant over time. This paper further decomposes the factors influencing changes in total electricity consumption in the provinces of Shanghai, Jiangsu, Zhejiang, and Anhui. (2) The spatial difference of electricity consumption between the other three provinces and Shanghai mainly comes from industry electricity consumption. (3) There are two kinds of conversion relations for the spatial-temporal differences of total electricity consumption. According to the conclusion, the targeted countermeasures and suggestions for total electricity consumption control are obtained. • We use the LMDI model to study the driving factors of total electricity consumption. • Production is the main reason for the increase of total power consumption. • Economic growth is the first factor to promote the total electricity consumption. [ABSTRACT FROM AUTHOR]

The increasing use of renewable energy sources and electric vehicles (EVs) has necessitated changes in the design of microgrids. In order to improve the efficiency and stability of renewable energy sources and energy security in microgrids, this paper proposes an optimal campus microgrid design that includes EV charging load prediction and a constant power support strategy from the main grid. The problem of load variation due to changes in the number of EVs connected to the microgrid will occur, and this paper presents a detailed prediction method and effective solutions. The load profile of EVs connected to the microgrid is simulated using the Monte Carlo (MC) method, taking into account EV owners' usage habits, including charging options and dwell time, as well as battery parameters, including state of charge (SOC) and size. The simulation results show that the peak-to-valley difference in grid power after adding EVs is close to 14 times due to the uniformity of travel between staff and students. Based on how long EVs stay in the parking place, a charging and discharging policy for participation in grid dispatch is developed, which has reduced the gap between the peak and the valley. This paper gives precedence here for the main grid to provide constant power support, which would limit the electricity consumption of the campus, reduce the dependence on the main grid and moreover increase the utilization of renewable energy by the microgrid. The ideal solution set for this microgrid system model's best configuration is found using the NSGA-II optimization algorithm. To select the most suitable option, the TOPSIS method is employed. The simulation results show that the microgrid system can operate with EV integration in an economical and stable manner. Additionally, the peak-to-valley value and CO 2 emissions are reasonably reduced, and the income of EV users is increased. At the same time, the microgrid operator's charging fee for EVs can lower operating costs and also suggests that future microgrid electricity sales will be more accessible and transparent. [ABSTRACT FROM AUTHOR]

A smart grid is a developing electrical network of transmission lines, switches, transformers, protection equipment, sensors, and information technologies working together and are the result of a dynamic co-evolution process that leverages the integration of technological advances. These are facing several challenges with renewable energy resources, has high consumption, and the ever-increasing demand for electricity at commercial, residential, and industrial levels. In recent decades, the main grid is facing several challenges related to the integration of renewable energy resources, deployment of grid-level energy storage devices, deployment of new usages such as the electric vehicle, massive usage of power electronic devices at different electric grid stages, and the inter-connection with microgrids and prosumers. This paper aims to identify the challenges, control these perspectives, for attaining the energy infrastructures, to achieve the directions of future research and development, with respect to power generation, transmission, distribution, and consumption. In this study, future smart grids creation and dimensionality reduction with signal handling on the smart grid using, targeted projection pursuit for future smart grids signal analysis is proposed, in which the Signal handling on the smart grid uses Blind equalization and is imposed to transmit the signal which is inferred from the received signal. The smart grid is based on the Non-negative matrix factorization; the Sallen-key topology is implemented to reduce the noise in the signal. Moreover, in this paper, we perform the targeted projection pursuit, the Cognitive Dimensionality Reduction is used for the analysis of n-way arrays, nonlinear dimensionality reduction, and we use the Linear predictive coding, and Hilbert spectrum which represents the spectral envelope of a digital signal of speech in the compressed form. The experiments on the proposed method, show the effectiveness of our method is 92 % well organised with extensive results at Targeted projection pursuit for future smart grids. The signal analysis shows the execution between past research and the ongoing research has achieved an efficiency rate of 92.2 %, in which the current approach outperforms 94.87 % compared existing approach. The analysis of future smart grids creation, Signal Dimensionality Reduction is 75 % higher than the existing analysis. The proposed system flow is performed with more versatility, and energy efficiency is 75.65 % and 34.54 % efficiency than the existing system. • These are facing several challenges with renewable energy resources. • Related to the integration of renewable energy resources. • Aims to identify the challenges, control these perspectives. • The smart grid using, targeted projection pursuit for future smart grids signal analysis. [ABSTRACT FROM AUTHOR]

Plasma conversion technology is an emerging technique under development to activate, convert or valorize gas molecules such as CO 2 , N 2 , CH 4 , NH 3 and others. A large-scale application beyond the lab-scale demonstrator unit requires assessment of the efficiency of this new technology. The straightforward approach for assessment of the efficiency is benchmarking with the other well-established technologies of similar technology readiness level (TRL). In this paper we present a benchmarking of the atmospheric pressure microwave-induced CO 2 plasma splitting with electrochemical CO 2 conversion, via both low-temperature and high-temperature electrolysis. An additional step of oxygen removal in case of the plasma reactor is implemented due to the difference in the output stream of the plasma (gas mixture containing CO 2 , CO, and O 2) and the electrochemical reactor (typical gas mixture on cathode containing CO 2 and CO). For the benchmarking, a comprehensive set of comparison parameters that are applicable for both the plasma and the electrochemical route is identified and grouped in three comparison categories: performance, interfaces, and economics. The comparison of these parameters demonstrates that in terms of the electric power consumption (EPC; power required for production of one Nm3 CO) plasma conversion technology (∼20 kWh/Nm3 CO) is in the ballpark with the other two electrochemical technologies (∼4–20 kWh/Nm3 CO). The key features of the plasma conversion technology are relatively large conversion (up to 56%) and moderate energy efficiencies (up to 27%). Also, CO 2 gas of reduced purity of only 98% can be used without decrease of the performance, and CO output values are currently at 3.5 slm (standard litre per minute). Fast on/off response time of order of minutes, and no need for the hot standby indicate that the plasma conversion is particularly suitable for use of intermittent renewable energy sources. The aspects that require further development include optimization of the process towards lower EPC total values, improved oxygen gas separation, and reliable ignition of the plasma. • CO 2 conversion for different technologies: low- and high-temperature electrolysis and plasma conversion technology. • Comparison of the technologies based on performance, assessment of the interface requirements, and economic costs. • Atmospheric pressure CO 2 microwave plasma with and without oxygen separation. [ABSTRACT FROM AUTHOR]

In a demand response scenario, coordinating multiple data centers with an electricity load aggregator provides opportunities to minimize electricity cost and absorb the volatility in the grid that is caused by renewable generation. To enable optimal coordination, this paper introduces a joint data center and aggregator optimization framework that minimizes the cost of data centers while they participate in demand response programs regulated by a load aggregator. The proposed framework, DCAopt , solves three integrated optimization problems: optimizing the quality-of-service of jobs in each data center, coordinating workload sharing among multiple data centers, and assigning (electricity) prices that incentivize demand response. Instead of relying on simplified relations between a data center's overall utilization rate and the average job delay, DCAopt applies queueing theory and job scheduling simulation techniques to model data centers with heterogeneous workloads, where different workload properties can be measured using data from actual servers. DCAopt solves the aforementioned joint optimization problems via gradient descent. Through evaluation using fine-grained simulations, we demonstrate that our framework finds better solutions to the data-center-aggregator optimization problems. With DCAopt, the energy costs of data centers can be reduced by 5% on average, with a corresponding reduction of a social cost assessed by the aggregator amounting to more than 30% in most cases. In addition, power usage reduction at the data centers is 6% higher compared to data-center-centric power use optimization. • DCAopt, a joint data center and aggregator optimization framework, optimizes quality-of-service of jobs, coordinates workload sharing, and assigns electricity prices that incentivize demand response. • DCAopt employs a realistic data center model, accepting heterogenous workloads composed of jobs with different properties (arrival rate, execution time, time constraint). • A queueing-theoretic approach in DCAopt quantifies and optimizes the quality-of-service of jobs. • DCAopt is evaluated using an event-based data center job scheduling simulator, which captures job events (arrival / start / finish) on the timescale of seconds. [ABSTRACT FROM AUTHOR]

This paper presents a method to estimate unserved residential space cooling needs in developing countries where the ownership of air conditioners (AC) is constrained by income. The method consists of first uncovering the relationship between weather and other factors explaining the existence of AC equipment in high income households, to later predict the need for AC equipment among similar households but of lower socio-economic statuses. In this way, the obtained estimates of the cooling gap are free of biases introduced by arbitrary assumptions of the temperatures and humidity levels that trigger adoption and use. Based on data from >100,000 surveys representative of the totality of households in Mexico, we estimate that between 4.9 and 7.2 million households or 14.85 % to 21.65 % of all, need but lack AC equipment. Assuming this cooling gap would be bridged with an efficient mini-split inverter heat pump with a capacity between 1 and 2 Tons, the required electric power generation capacity to supply the needed electricity would be between 3.69 and 10.77 GW (i.e., between 4 and 13 % of Mexico's capacity). Assuming usage patterns estimated from 2018 data, the new AC equipment installed would raise the country's electricity consumption by 5.96 TWh (95 % Confidence Interval (CI) is 3.30–12.17 TWh) or 8.10 % (95 % CI: 4.48 % - 16.54 %) of the residential demand observed in 2022. • We present a method to estimate unserved residential space cooling needs. • Method estimates the need for AC equipment without assuming a threshold temperature. • A cooling gap measure is the number of households without AC in places where is needed. • Other cooling gap measure is the electricity and power generation capacity needed for AC. • 4.9 -7.2 million Mexican households or 14.85% to 21.65% of all, need but lack AC. [ABSTRACT FROM AUTHOR]

• Explores the potential of vertical farms as heat source for heat networks. • Explores the potential for flexible electricity use in a vertical farm. • The energy system with integrated vertical farm reduced energy use by 15 %. • Flexible electricity use by vertical farms significantly reduces costs. The urban energy transition requires innovative heating and cooling systems, as well as enhanced flexibility in electricity usage. This paper explores the theoretical potential for vertical farms to contribute to the energy transition by supplying residual heat to local district heat networks and flexible electricity usage. A stepped approach was used to design energy systems that achieve thermal energy balance through heat and cold exchange between a vertical farm and buildings within a specific Dutch neighbourhood. Furthermore, alternative lighting strategies for vertical farms were explored to reduce grid congestion and to respond to electricity price fluctuations, limiting the mismatch between electricity generation and demand. Compared to the baseline scenario, the energy system with an integrated vertical farm reduces overall energy use by 15 %, even when accounting for the farm's electricity use. By adopting intermittent lighting that is better aligned with electricity price fluctuations, the vertical farm obtained annual cost savings of 14 %. The integration of vertical farms into energy systems can, therefore, contribute to the urban energy transition by producing residual heat to balance thermal energy system and save money for growers by optimising LED operations to align with electricity price fluctuations, whilst producing fresh vegetables for the city. [ABSTRACT FROM AUTHOR]