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Signatories of the Paris Agreement are set to miss their climate targets. The net-zero pledges announced to date across many countries and private industries are insufficient to achieve carbon neutrality, which requires implementation of far-reaching and significantly scaled-up climate-positive actions. All technology options that pertain to deep decarbonization and carbon removal must be part of the mitigation portfolio. Transformative action plans must be established in which every individual/organization around the globe is an actor of changes in relation to net-zero goals. Such plans must involve governmental policy support but also encourage voluntary efforts to boost innovations, investments, initiatives, and behavioral changes. Recognition of these efforts is made quantifiable with the concepts of carbon handprint and net positivity. This paper presents a carbon handprint perspective on characterizing the environmental benefits of hybrid energy systems (HESs)—a widely applicable solution to cleaner production leveraging the capabilities of a portfolio of low-carbon energy sources—that provide heat and electricity to industrial processes. First, the carbon handprint and net positivity concepts and their calculation approach are introduced. The state of the art of HES-enabled industrial cogeneration is then surveyed, and the greenhouse gas emission intensities of different energy sources that power HESs are compared. Next, drawing on a case study about a US chemical facility's voluntary initiative to explore replacing its fossil fuel–based cogeneration infrastructure with a clean energy–generated HES, several technically viable scenarios are evaluated—especially those with small modular nuclear reactors—to illustrate how the positive-thinking handprint approach helps encourage and inform the search for widespread influence pathways in pursuit of net-positive sustainability. Finally, current knowledge gaps in the case study are identified, and opportunities to scale up the proposed handprint-based analysis are outlined with consideration of an expanded role of HESs in fulfilling climate objectives. It is envisioned that like-minded decision makers in the industry sector and beyond will adopt this perspective and act synergistically to enhance their environmental stewardship through voluntary actions and make greater contributions to the planet's climate future. • Environmental benefits of hybrid energy systems are assessed using carbon handprint. • Hybrid energy systems produce cleaner heat and power for industrial processes. • Carbon handprint measures impacts of voluntary climate-positive actions. • Exemplary handprint-based net-positivity analysis is performed on a case study. • Opportunities for nuclear and renewable energy are highlighted in the case study. [ABSTRACT FROM AUTHOR]

The conventional method used for palm oil mill effluent (POME) treatment is associated with several environmental issues that affect the sustainable production of palm oil. Integrated POME treatment with a closed anaerobic digester is identified as a practical cleaner production approach to mitigate global issues on energy security and climate change and enhance the sustainability of the industry. This paper discusses the potential of capturing biogas to the palm oil industry and Malaysia's renewable energy (RE) and greenhouse gases (GHG) emissions targets. Approximately 75% GHG reduction could be achieved for every t of crude palm oil produced from palm oil mill with biogas capture. In total, 1750 million m3 biogas with GHG savings of 19.5 million t CO 2 eq can be achieved annually, if all palm oil mills in Malaysia capture the biogas. A 540 MW installed capacity or 37 million MMBtu in the form of bio-compressed natural gas (Bio-CNG) or biomethane, can supplement the nation's energy mix for the sustainable energy generation. These contribute towards enhancing the sustainability criteria and economic performance of the palm oil mills by adopting cleaner production technology of POME treatment for circular economy and better market access of palm oil products. Future work should emphasise on nationwide biogas implementation through synergising and strengthening private commitments and the government supports. [Display omitted] • Palm oil mill effluent (POME) generation, characteristics and treatment methods. • Biogas production and utilisation for renewable energy and GHG mitigation. • Economic and environmental benefits of biogas for sustainable palm oil. • Business model and research gaps for nationwide biogas implementation. [ABSTRACT FROM AUTHOR]

• The distributions of temperature and air velocity for a curved double-skin façade (CDSF) were investigated. • The nonuniformity of solar irradiation on a curved surface is higher than that of a planar surface. • The thermal pressures of a CDSF show different values at different positions. • Solar irradiances show a power function relationship with the air velocity. • There should be transverse flows and heat transfers in the cavity of a CDSF. Curved architectural forms are becoming popular in modern building designs, resulting in more demanding requirements of curved double-skin façades (CDSF). However, CDSF's natural ventilation performance is barely known, as most current studies investigate planar DSF (PDSF). This paper experimentally evaluated the thermal performance of a reduced scale CDSF model. Experimental results show that CDSF can form natural ventilation by buoyancy, however, with significant discrepancies in the distribution of solar irradiances on the curved surface. Observed nonuniformity shows 30.05 % on average vertically and 9.12 % on average horizontally. The thermal pressures vary at different horizontal positions with a fluctuation range between 0.056 Pa and 0.063 Pa, indicating the influence of gradients and the existence of transverse flows and heat transfers in the cavity. Moreover, the effects of solar irradiation and outdoor air temperature on the CDSF are discussed. The results show that solar irradiation is the main influencing factor on the thermal performance of a CDSF, which are correlated with air velocities in the form of power functions. The influence of solar irradiation on CDSF is similar to that of PDSFs, but CDSF exhibits a bigger exponent. In comparison, outdoor air temperature shows very limited effects, and the influence of outdoor air temperature on CDSF is similar to that of PDSF. For the analyzed CDSF, the thermal pressures only fluctuate from 0.053 Pa to 0.055 Pa when the outdoor air temperature raises from 24 °C to 36 °C. [ABSTRACT FROM AUTHOR]

Maintaining frequency stability in a power system with a large scale of renewable energy resources (RES) requires extra frequency regulation resources due to the intermittent generation of RES. As one of the most promising solutions, electric vehicles (EVs) have faster response characteristics and can provide considerable regulation capacity when aggregated. When participating in load frequency control (LFC), the EV aggregator needs to dispatch the LFC signal into every single EV. However, since the aggregated EVs have different travel profiles and state-of-charge (SOC), it is challenging for the aggregator to decide the dispatching method so that both regulation requirements and users' transport usage can be best satisfied. In this paper, an optimal dispatching control is designed for the EV aggregator. With the proposed control scheme, the EV aggregator can provide the regulation capacity to the system while ensuring that each individual EV will have enough SOC before the next trip. Compared with the optimal dispatching methods in existing literature, the proposed control operates at a faster time-step and allows EV aggregators to utilize EVs more efficiently, therefore more capacity payment from the market can be obtained by the EV aggregators. Simulation is performed in Matlab and Simulink to examine the performance and the effectiveness of the proposed dispatching controller. [Display omitted] • Optimal dispatching scheduling to maximize the revenue from the LFC market. • A faster dispatching time-step to utilize the EVs more efficiently for LFC. • A specific GA is designed to solve the optimization problem relatively rapidly. [ABSTRACT FROM AUTHOR]

To reduce global greenhouse gas emissions, the world must find intelligent solutions to maximise the utilisation of carbon-free renewable energy sources. In this paper, multi-agent reinforcement learning is used to control a microgrid in a mixed cooperative and competitive setting. The agents observe fluctuating energy demand, dynamic wholesale energy prices, and intermittent renewable energy sources to control a hybrid energy storage system to maximise the utilisation of the renewables to reduce the energy costs of the grid. In addition, an aggregator agent trades with external microgrids competing against one another and the aggregator to reduce their own energy bills. For this, the algorithm deep deterministic policy gradients (DDPG) and multi-agent DDPG (MADDPG) are used to compare the use of a single global controller versus multiple distributed agents, along with the single and multi-agent variants of distributional DDPG (D3PG) and twin delayed DDPG (TD3). The research found it is significantly more profitable for the primary microgrid to sell energy on its own terms rather than selling back to the utility grid, and is also beneficial for the external microgrids as they also reduce their own energy bills. The methods that produced the greatest profits were the multi-agent approaches where each agent has its own reward function based on the principle of marginal contribution from game theory. The multi-agent approaches were better able to evaluate their performance controlling individual components of the environment which allowed them to develop their own unique policies for the different types of energy storage system. • Novel use of MADDPG for mixed cooperative and competitive microgrid environment. • Compare use of a central controller or multiple distributed agents in microgrids. • Hybrid energy storage system for long, medium, and short-term storage. • Case studies for renewable utilisation and trading with neighbouring microgrids. • Showcase the benefit of using rewards based on marginal contribution in a MAS. [ABSTRACT FROM AUTHOR]

This paper proposes a novel taxonomy of coordination strategies for distributed energy resources at the edge of the electricity grid, based on a systematic analysis of key literature trends. The coordination of distributed energy resources such as decentralised generation and flexibility sources is critical for decarbonising electricity and achieving climate goals. The literature on the topic is growing exponentially; however, there is ambiguity in the terminology used to date. We seek to resolve this lack of clarity by synthesising the categories of coordination strategies in a novel exhaustive, mutually exclusive taxonomy based on agency, information and game type. The relevance of these concepts in the literature is illustrated through a systematic literature review of 84,741 publications using a structured topic search query. Then 93 selected coordination strategies are analysed in more detail and mapped onto this framework. Clarity on structural assumptions is key for selecting appropriate coordination strategies for differing contexts within energy systems. We argue that a plurality of complementary strategies is needed to coordinate energy systems' different components and achieve deep decarbonisation. [Display omitted] • Systematic and detailed literature reviews help identify key coordination literature trends. • A novel taxonomy synthesises distributed energy resources coordination strategies. • Agency, information, and game type are key literature concepts for classification. • The taxonomy helps resolve the current ambiguity of terminology in the field. [ABSTRACT FROM AUTHOR]

[Display omitted] • Thermal potential estimation of water source and their energy systems are reviewed. • Practical potential mainly depends on the geographical and temporal potential. • Pumping-well system requires high installation costs but can use high temperature. • Critical parameters for improving performance of entire system were investigated. In recent years, the utilization of a water source heat pump system (WSHP) has been proposed in South Korea as a solution to reduce energy consumption of buildings. To practically utilize the thermal energy of a water source, it is necessary to estimate the thermal potential of water sources and apply suitable energy conversion systems. However, there are extremely limited studies that conduct in-depth analyses to estimate the thermal potential of water sources and the energy conversion systems of water source heat pumps. In this context, this paper focuses on the review of the main issues in thermal potential estimation and energy conversion systems of water source heat pump systems. The estimation of thermal potential was reviewed in terms of theoretical, geographical, environmental, technical, and temporal potentials. The components constituting the system were investigated by dividing into heat pumps, water intake systems, and air handling units. In addition, the critical parameters for improving the performance of the entire system, such as building type, water source, and water intake distance, were investigated. It is expected that this study will be a valuable supplement to enable the thermal energy utilization of water sources to meet the growing energy demands. [ABSTRACT FROM AUTHOR]

• Scandinavian countries are fostering a sustainable economy based on renewable energy. • The investments on renewable sources has positively affected the investments in this energy and GHG emissions. • A bidirectional causal relationship exists between renewable energy consumption and CO 2 emissions. • A causal relationship exists between renewable energy consumption and economic growth. Climate change presents the greatest challenge facing all countries of the world in the new millennium. Among others, objective 13 of the Sustainable Development Goals (SDGs) aims at adopting urgent measures to contrast climate change and its consequences. Part of the decline in the global growth of emissions has been the increase in using renewable energies. In this context, the relationship among GDP, CO 2 emissions, and renewable energy use has been investigated in this study, starting from a systematic review that has noticed the presence of three clusters focused on: CO2 emissions, GDP, and energy consumption. Despite the current level of interest in examining the relationship among these variables, there have been few empirical studies. To fill this knowledge gap, this paper has been focused on the Scandinavian countries, where the use of renewable energies has steadily increased, developing novel panel analysis estimates. Using a dataset of these five economies over a 1990–2018 time period, several panel data tests have been carried out, in order to robustly assess the causality issue among renewable energies, CO 2 emissions, and GDP. The results of the empirical analysis imply that renewable energy consumption is a useful policy instrument to reduce CO 2 emissions without adversely affecting GDP growth. The main implications have been that the decrease of CO 2 emissions, by increasing renewable energy use, can guarantee high levels of energy efficiency and economic growth. These empirical findings help design innovative energy policy roadmaps and accelerate the ecological transition through the promotion of renewable energy and the reduction of GHG emissions. [ABSTRACT FROM AUTHOR]

Over 400 colleges and universities have signed carbon reduction pledges through Second Nature. Nonetheless, a review of their greenhouse gas inventories shows that many universities continue to show limited or no progress in terms of renewable energy deployment and generation. One study from 2019 shows that it is now cheaper to build new renewable energy projects than it is to continue operating existing coal-fired power plants (for 74% of existing fleet). Meanwhile, another study shows that universities rarely achieve a positive economic net benefit when deploying solar on their campuses, despite the cost declines in solar and rising utility prices. This same study finds that these same projects accrue a positive social and environmental net benefit for the surrounding community that outweighs current economic subsidies, implying that increased subsidies for college solar deployment would, more often than not, pass cost-benefit analysis. This present study builds on the previous two studies and explores the question of whether state-level financial incentives have a meaningful impact on the deployment of renewable energy on college campuses. The thesis guiding this research is that universities in states with certain state-level financial incentives will be more successful in deploying renewable energy and in meeting their clean energy goals. The financial incentives being studied are limited to grants, tariffs, net metering (at retail rates), and solar renewable energy credits. This research paper shows that the presence of these financial incentives is positively correlated with renewable energy deployment across colleges, providing a starting map for policy-makers interested in supporting universities' renewable energy deployment in their states. [ABSTRACT FROM AUTHOR]

The significant penetration of renewable power generations (RGs) and the large-scale use of plug-in electric vehicles (PEVs) have brought tangible impacts in tackling the climate change challenge the mankind has been facing due to substantive green-house gas and pollutant emissions from fossil-fuel based thermal power generation plants. However, the uncertainty of RGs has also exerted significant challenges to the grid operation and control. Therefore, dynamic power system scheduling to accommodate the intermittent RGs and mass roll-out of PEVs has become extremely important. In this paper, a novel power system rescheduling strategy is proposed to tackle this problem. Considering the uncertainty of the wind energy, a set of indices according to different wind power application scenarios is proposed to initiate a rescheduling scheme for power generations. In addition, a social learning particle swarm optimization algorithm based on real-value and binary parallel is proposed to schedule the output of generator units and the charging and discharging of the PEV. The effectiveness of the proposed active rescheduling framework and solving algorithm has been verified by extensive experiments considering different number of generating units and scenarios, achieving up to over 5.3% cost reduction. The experimental results have also shown that through expropriate management of the charging and discharging of PEVs would be significantly alleviate the negative impact on the grid stability caused by the intermittent wind power generations. • A new algorithm framework of power system active rescheduling method is adopted. • The influence of renewable energy and electric vehicle access on unit commitment is analyzed. • The proposed method is applied into the UC problems in different wind energy application scenarios. • Reasonable rescheduling of power system is helpful to improve the economy and reliability of power system. [ABSTRACT FROM AUTHOR]

[Display omitted] • A comprehensive overview of NZEBs is conducted by using bibliometric techniques. • Hot research topics of NZEBs are identified through keyword co-occurrence analysis. • The research trend, key journals, publications, authors, and countries are analyzed by co-citation analysis. • The thematic evolutionary analysis of NZEBs was investigated using SciMAT tool. This study aims to provide a comprehensive overview of three-decade research developments in the field of Net-Zero Energy Buildings (NZEBs) by using bibliometric analysis techniques. The specific objectives of this paper are to: (1) identify the most influential researchers, sources, and countries in this field; (2) identify the main thematic research focus areas and hot topics in the field; and (3) analyze the thematic evolution of NZEBs. VOSviewer software was used to realize the first two objectives while to meet the third objective, the SciMAT tool was employed to examine the thematic evolution of the field within three sub-periods (1995–2005; 2006–2015; 2016–2022). The results indicate that the main thematic areas of the field were initially limited to a few themes including 'environmental impact', 'green buildings', 'building materials', and 'fuel cells', and it expanded in the next periods by encompassing more themes. The results also showed that the field has now reached a certain level of consolidation by acknowledging the significance of three major themes: (1) buildings' energy conservation, (2) energy production and energy storage, and (3) methodological approach. Nevertheless, the evolutionary analysis points out that the field is still expanding with several themes newly emerged such as 'electric vehicles', 'zero-emission neighborhood' and 'smart buildings'. The findings offer a status-quo overview and critiques of research developments in NZEBs as well as emerging themes in the field that should be investigated by future studies. [ABSTRACT FROM AUTHOR]

Saudi Arabia and other Gulf Cooperation Council (GCC) members have a parallel agenda of electricity market reforms together with ambitious goals of renewable energy deployment. The motivation for this agenda is multifaceted and extends beyond increasing economic efficiency. Renewable integration raises market and regulatory design issues for countries transitioning to markets and high levels of renewables though. This difficulty or perhaps even incompatibility may undercut both policies' objectives. This paper analyses the implications of deploying new energy storage in this context. Although storage services would facilitate the integration of renewables and market restructuring policies, deploying this novel technology would raise additional regulatory issues in the electricity sector. Ignoring these regulatory challenges could not only lead to unnecessary costs of transition but also make it more difficult to obtain the full benefits of other economic policy objectives such as the removal of subsidies to relieve public finances and the release oil for sale in international markets. [ABSTRACT FROM AUTHOR]

• Heating and cooling tests on a fully monitored large-scale pavement solar collector. • Thermal performance investigation of a controllable and configurable prototype. • Sensitivity analysis of design and operational parameters on system performance. • A great potential in reducing asphalt surface temperature to mitigate UHI effects. Solar energy harvesting as a renewable and sustainable energy source has been widely investigated in recent years across engineering fields. The use of Pavement Solar Collectors (PSC) can lead to clean energy production, an increase in road safety, prolong the service life of asphalt pavement, and can mitigate the Urban Heat Island (UHI) effect. This study describes a controllable large-scale research PSC prototype with high configuration flexibility, and full monitoring capability at the University of Antwerp, Belgium. Since small- or laboratory-scale setups do not reflect the behavior of actual projects, the present paper investigates the thermal response of a large-scale PSC in the Western European climate, including heating load, heat extraction capacity, and asphalt surface and profile temperature changes during heating and cooling experiments. The study shows that a low supply temperature compared to high (14 °C vs. 28 °C) can reduce the depletion rate of the stored thermal energy in borehole thermal energy storage up to 7 times. The sensitivity analysis indicates that an increase in flow rate from laminar to transient regime requires twice as much thermal power compared to the same flow rate changes within transient and turbulent regimes. The maximum average daily efficiency of the PSC could reach 34% with a flow rate of 4 l/min. The experimental results showed that increasing the pipe length from 50 m to 200 m reduces the cumulative power extraction capacity by up to 48%. Furthermore, the PSC system shows great potential in reducing the asphalt surface temperature (up to 12 °C) to mitigate the UHI effects. Finally, the PSC system effectively controls the temperatures of the interface zones to reduce the rutting distress in the summertime and lower the potential of cold thermal crack developments and brittle shear failure behavior in wintertime. [ABSTRACT FROM AUTHOR]

Egypt's power system is facing major changes. Rising demand, scarcity of fossil resources and the falling capital costs of solar and wind energy technologies pose the potential to fundamentally remake the national power fleet. Against this backdrop, the paper at hand investigates potential pathways of the domestic power system with a particular focus on regional deployment and associated impacts on the transmission network. To this end, the open-source modelling framework OSeMOSYS was adapted and deployed. The analysis evaluates renewable energy potentials for 320 sites across Egypt in eight different sub-regions. The study concludes that wind and PV installations prove to be cost-competitive and capable of shouldering a large share of projected demand growth, while their regional deployment should take required grid investments into consideration. The results also indicate that a restrained expansion of renewable energy technologies targeted by the government proves to be more costly than a more aggressive deployment pathway. • Cost-optimal future development of the Egyptian power plant fleet. • Inclusion of renewable potentials for 320 sites across Egypt. • Regional implications for the domestic transmission network. [ABSTRACT FROM AUTHOR]

[Display omitted] • Environmental performance is assessed for low-energy/passive house dwellings. • The current and future Northern Ireland electricity mix scenarios are analyzed. • Employing passive-house standards could reduce the building's environmental impact. • The electricity decarbonization leads to a significant reduction of GWP. • Further environmental benefits can be ensured by focusing on the material market. This paper implements a Life Cycle Assessment of several low-energy/passive house buildings located in Northern Ireland. This work aims (i) to assess the environmental performance of the buildings and (ii) to evaluate the effect of electricity decarbonization on the dwellings' global warming potential (GWP). Three different future electricity mix scenarios have been used and compared to a static scenario where the current electricity mix remains constant. The LCA results of the static scenario reveal that applying passive-house standards could reduce the impact of dwellings while it does not necessarily provide a positive environmental outcome. The building operation phase contributed the most to the environmental impact, while negligible impact comes from the end-of-life stage. The electricity decarbonization leads to a significant reduction of GWP in all cases, with the highest value achieved for the passive house using the highest share of electricity, 58%-70% GWP reduction compared to the static scenario. Moreover, electricity decarbonization increases the relative share of the production stage to the overall building emission. Therefore, close attention should be paid to construction material selection in any effort aiming to achieve further environmental benefits. The buildings' environmental and operational energy performances were also compared to the RIBA 2030 Climate Challenge. [ABSTRACT FROM AUTHOR]

• A multi-energy absorption system is proposed for combined cooling and heating. • Multiple sets of indoor terminals are driven by a single set of absorption machine. • Annual operating cost for a typical household is reduced by 25.5–46.8%. • Carbon reduction ratio reaches 41–44% compared with traditional methods. • Annual carbon emission per household for cooling and heating reduces below 3000 kg. Under the background of carbon neutrality, efficient and low-carbon cooling and heating in rural areas away from the centralized network is a meaningful topic. In this paper, a multi-energy complementary absorption system is proposed to provide space heating in winter and cooling in summer. The system utilizes renewable energy that is abundant in rural areas, including solar and biomass, and balances the energy supply through natural gas replenishment. By adopting the cooling/heating medium circulation, multiple sets of indoor terminals are driven by a single set of absorption machine and outdoor unit. It is found that as long as the renewable energy ratio is higher than 0.2, the economic performance of the proposed system will be outstanding over the existing methods. When applied in Beijing, the annual operating cost for a typical household is reduced by 25.5–46.8% compared with traditional gas furnace/compression heat pump combined with air conditioner. Moreover, even when the proposed system is driven solely by natural gas, the carbon emission is reduced by 16–20% compared with traditional methods. When the renewable energy ratio is 0.4, the carbon reduction ratio reaches 41–44%, and the annual carbon emission for cooling and heating of a typical household reduces below 3000 kg. It is expected that by solving the problem of load and concentration variation during the year-round operation, the proposed system has great application potential in cold winter and hot summer areas away from the centralized cooling or heating network. [ABSTRACT FROM AUTHOR]

• Multi-agent dueling double deep Q-network optimization framework was established. • Prioritized experience replay and feasible action screening mechanism was applied. • Value-decomposition network was used to solve multi-agent reliability problems. • Simulation results prove the economy and doability of the method. Under the background of high global building energy consumption, meeting the ever-growing energy consumption demand of building energy system (BES) through renewable energy is one of the effective ways to promote the clean transformation of global energy structure and achieve "carbon neutrality". However, with the introduction of renewable energy, BES control becomes more complicated. The mismatch between supply and demand sides limits the further growth of renewable energy consumption, which is caused by fluctuation of renewable energy and randomness of load. Therefore, it is challenging to develop an efficient framework to realize the cooperative control of various controlled objects in supply and demand sides. To address this challenge, a multi-agent deep reinforcement learning framework was proposed to optimize the energy management of the building. In this paper, a dueling double deep Q-network was used for optimization of single agent, and value-decomposition network was put forward to solve the cooperation optimization of multiple agents. Also, considering the controlled characteristics of BES, prioritized experience replay and feasible action screening mechanism were introduced to accelerate the convergence and maintain stability of the algorithm applied to BES. Simulation results show that, the multi-agent cooperation algorithm can realize the control of variously different devices at the same time and achieve multi-objective cooperation optimization of BES. Moreover, the proposed approach reduced the uncomfortable duration by 84%, the unconsumed amount of renewable energy by 43%, and the energy cost by 8% compared with the conventional rule-based control approach. [ABSTRACT FROM AUTHOR]

This paper considers the nonlinear proton exchange membrane fuel cell model to precisely describe the system dynamics and presents a robust reset control method for regulating oxygen rate and optimizing the operation of the fuel cell system during fast load variations. The proposed reset law eliminates overshoot, shortens settling time, and thus improves the behavior of the closed-loop system, while achieving these goals is impossible by any linear controller. Due to the ability of field programmable gate array (FPGA) in providing high speed and efficient processing through software as well as near-real-time performance verification, the proposed control method is implemented through FPGA to confirm operational capability and high speed flexibility of controller. The results of the implementation of the proposed method and comparison with the hybrid fuzzy proportional integral derivative method exhibit smooth dynamic response, error reduction in terms of various criteria and better capability in tracking the desired oxygen rate despite the drastic changes in load current. [ABSTRACT FROM AUTHOR]

This paper examines the nexus between renewable energy consumption (REC) and Gross Domestic Product (GDP) in G7 countries. To this end, we analyse data from 1980 to 2020 employing Toda and Yamamoto (1995) time causality test, Lemmens et al. (2008) frequency causality test and Hatemi-J's (2012) asymmetric causality approach. The findings differ across countries and are as follows. The conservative hypothesis is validated for the US and Canada. Specifically, we find significant and positive causal effect from GDP to REC in the long-term horizons (periods greater than 15 years for the US and 7 years for Canada). The growth hypothesis is confirmed for Germany. Precisely, significant and positive causal impact is shown from REC to GDP in the long-run horizons (periods longer than 7 years). The neutrality hypothesis is supported for France, the UK, Italy and Japan as there is no significant causal link at any time horizon. This is likely because the clean energy consumption does not reach a threshold beyond which it will start to have considerable interaction with economic growth. This study provides policymakers a better understanding of the REC-economic growth nexus to formulate appropriate policies. • We examine the nexus between renewable energy consumption and economic growth in G7 countries. • We use Toda and Yamamoto (1995) and Lemmens et al. (2008) tests and Hatemi-J's (2012) asymmetric causality approach. • For the US and Canada, significant and positive causal effect is shown from GDP to REC in the long-term horizons. • For Germany, significant and positive causal impact is displayed from REC to GDP in the long-run horizons. • For France, the UK, Italy and Japan, no significant causal link is found between REC and economic growth. [ABSTRACT FROM AUTHOR]

A large deployment of energy storage solutions will be required by the stochastic and non-controllable nature of most renewable energy sources when planning for higher penetration of renewable electricity into the energy mix. Various solutions have been suggested for dealing with medium- and long-term energy storage. Hydrogen and ammonia are two of the most frequently discussed as they are both carbon-free fuels. In this paper, the authors analyse the energy and cost efficiency of hydrogen and ammonia-based pathways for the storage, transportation, and final use of excess electricity from an offshore wind farm. The problem is solved as a linear programming problem, simultaneously optimising the size of each problem unit and the respective time-dependent operational conditions. As a case study, we consider an offshore wind farm of 1.5 GW size located in a reference location North of Scotland. The energy efficiency and cost of the whole chain are evaluated and compared with competitive alternatives, namely, batteries and liquid hydrogen storage. The results show that hydrogen and ammonia storage can be part of the optimal solution. Moreover, their use for long-term energy storage can provide a significant, cost-effective contribution to an extensive penetration of renewable energy sources in national energy systems. • Assessment of hydrogen and ammonia for wind energy storage. • Accounts the effects of electricity price variability and renewable penetration. • Realistic case study to showcase the proposed framework. • Hydrogen and ammonia selected for high fuel prices and wind power penetration. • Specific policies need to promote renewable-based capacity market infrastructure. [ABSTRACT FROM AUTHOR]

In this paper, we present a wind speed simulation method by detecting change points in multivariate nonstationary wind speed time series data. The change point detection method identifies changes in the covariance structure and decomposes the nonstationary multivariate time series into stationary segments. Parametric and nonparametric techniques are also provided to model and simulate new time series within each stationary segment. The proposed simulation approach retains the statistical properties of the original time series (as obtained from a Numerical Weather Prediction system) and therefore, can be employed for simulation-based analysis of power systems planning and operations problems. We demonstrate the capabilities of the change point detection method through computational experiments conducted on wind speed time series at five-minute resolution. We also conduct experiments on the economic dispatch problem to illustrate the impact of nonstationarity in wind generation on conventional generation and location marginal prices. • Change point method-based model for nonstationary subhourly wind time series. • Parametric and nonparametric block bootstrap methods for wind simulation. • Scenario generation for stochastic programming approaches in power systems. [ABSTRACT FROM AUTHOR]

• Inequality of renewable technology innovation is evaluated by Gini coefficients. • Contributors to renewable innovation inequality are decomposed by Shapley method. • Variables of regional attributes, policies and official characteristics are proposed. • Knowledge stock and R&D expenditure are the greatest contributors. • Energy-related experience and tenure of officials may be potential contributors. Regional inequality of technology innovation associated with renewables may lead to insufficient innovation growth over the long-term, which impedes the deployment of renewable energy and slows down low-carbon transition. This paper aims to explore the major contributors to regional inequality of technology innovation in renewable energy by taking China as a representative example. Three groups of variables including regional attributes, supporting policies, and local official characteristics are proposed as potential explanatory factors. A set of panel data covering both the photovoltaic and wind energy sectors in 31 Chinese provinces from 2008 to 2017 are collected for the empirical study. Gini coefficient is used to quantify the inequalities of renewable technology innovation, while Shapley decomposition method is applied to evaluate the contribution of each significant factors. The results show that the overall Gini coefficients of photovoltaic and wind technology innovation in China are 0.65 and 0.62, respectively, indicating medium-level regional technology innovation inequality in both sectors. Knowledge stock (46.44%), R&D expenditure (36.56%), green fixed industrial investment (13.53%), electricity consumption (1.33%), and the energy-related experience of local officials (0.05%) are significant contributors to regional inequality of photovoltaic innovation. Knowledge stock (45.20%), R&D expenditure (37.90%), electricity consumption (8.63%), feed-in tariffs (1.02%), and the tenure of local officials (0.18%) are significant contributors to regional inequality of wind innovation. Therefore, eliminating regional inequality of technology innovation in renewables should start with balancing regional R&D expenditure and knowledge stock. [ABSTRACT FROM AUTHOR]

• A scheme for optimal next-day power scheduling in isolated microgrid is proposed. • The microgrid has wind turbines, PV arrays, microturbines, fuel cell and battery storage. • Uncertainty of renewable power and demand power is analyzed. • Both active and reactive powers are planned. • Microgrid reconfiguration is implemented. • Demand response is integrated. This paper proposes a new operation management scheme (OMS) for next-day complex power scheduling in an islanded reconfigurable microgrid. Energy supply in the microgrid comes from wind turbines and photovoltaic generators as renewable dispersed generators (RDG). Meanwhile, there are two microturbine units and a fuel cell unit as dispatchable distributed generators (DDG). Also, a battery bank energy storage system (BESS) is integrated. The microgrid has a number of controllable tie-switches to change the microgrid configuration and supplies residential, commercial, and industrial loads with different electrical models. Some of loads participate in a demand control program. The OMS is mathematically formulated as a multi-period mixed-integer stochastic nonlinear optimization. It has to decide the hourly power share of each DDG, the states of the control switches, and specifications of demand response. The OMS aims to minimize the total operation cost, unmet load, and curtailed renewable power maintaining all technical constraints. The uncertainty of renewable resources and load is analyzed in the OMS by Monte-Carlo simulation while scenario-reduction approach is used to reduce the computational burden. Besides, a new version of bat algorithm (IBA) is developed as a robust optimization solver. Simulation results and comparative evaluation of the OMS are presented using a 45-bus case study system to prove the merits of the method. [ABSTRACT FROM AUTHOR]

Nowadays, multi-energy systems are receiving special attention from smart grid community owing to their high flexibility potentials integrating with multiple energy carriers. In this regard, energy hub is known as a flexible and efficient platform to supply energy demands with an acceptable range of affordability and reliability by relying on various energy production, storage and conversion facilities. Given the increasing penetration of renewable energy sources to promote a low-carbon energy transition, accurate economic and environmental assessment of energy hub, along with the real-time automatic energy management scheme has become a challenging task due to the high variability of renewable energy sources. Furthermore, the conventional model-based optimization approach requiring full knowledge of the employed mathematical operating models and accurate uncertainty distributions may become impractical for real-world applications. In this context, this paper proposes a model-free safe deep reinforcement learning method for the optimal control of a renewable-based energy hub operating in multiple energy carries while satisfying the physical constraints within the energy hub operation model. The main objective of this work is to minimize the system energy cost and carbon emission by considering various energy components. The proposed deep reinforcement learning method is trained and tested on a real-world dataset to validate its superior performance in reducing energy cost, carbon emission, and computational time with respect to the state-of-the-art deep reinforcement learning and optimized-based approaches. Moreover, the effectiveness of the proposed method in dealing with model operation constraints is evaluated on both training and test environments. Finally, the generalization performance for the learnt energy management scheme as well as the sensitivity analysis on storage flexibility and carbon price are also examined in the case studies. • A smart energy-hub integrating with multi-energy carriers is investigated. • A novel safe deep reinforcement learning method is proposed. • The proposed method deals effectively with physical constraints. • The proposed method achieves energy cost and carbon emission benefits. [ABSTRACT FROM AUTHOR]

Energy policy is too often not designed for energy consumers in a low-cost and consumer-friendly manner. This paper proposes a novel Stackelberg game and Blockchain-based framework that enables consumer-centric decarbonization by automating iterative negotiations between policy makers and consumers or generators to reduce carbon emissions. This iterative negotiation is modeled as a Stackelberg game-theoretic problem, and securely facilitated by Blockchain technologies. The policy maker formulates carbon prices and monetary compensation rates to dynamically incentivize the carbon reduction, whereas consumers and generators schedule their power profiles to minimize bills and maximize profits of generation, respectively. The negotiating agreement is yielded by reaching a Stackelberg equilibrium. The exchanged information and controlling functions are realized by using smart contracts of Blockchain technologies. Case studies of GB power systems show that the proposed framework can incentivize 9% more bill savings for consumers and 45.13% more energy generation from renewable energy sources. As a consumer-centric decarbonization framework, it can at least reduce carbon emissions by 40%. [Display omitted] • A novel consumer-centric and negotiation-based framework to decarbonize power systems. • A Blockchain based platform to integrate the exchanged information and controlling functions of the power system scheduling into data structures, nodes, and smart contracts of Blockchain networks. • How Blockchain based platform can prevent cyber attacks is discussed. • Case studies demonstrate the effectiveness of the proposed framework in the context of Great Britain power systems. [ABSTRACT FROM AUTHOR]

This paper shows that interventions based on social norms and on increasing the visibility of people's decisions to others ("decision observability") present promising pathways of generating public support for renewable energy development. In a laboratory experiment (n = 300), we show that social norms and decision observability increase support for renewable energy, even at a financial cost to oneself: When exposed to pro-environmental social norms, participants donated 35% more money to an existing renewable energy initiative than participants in the control condition (Cohen's d = 0.35). Participants whose decisions were observable to others donated 23% more compared to control (d = 0.23). And participants exposed to both treatments (their decisions being observed by others and learning about norms) donated 69% more compared to control (d = 0.67). In addition, our treatments had a positive effect on participants' post-decisional emotions of happiness and pride, which partly alleviates existing concerns about possible adverse side-effect of social influence interventions. Suggestions for policy makers and for future research in this area are presented. [ABSTRACT FROM AUTHOR]

The research on microbial fuel cells (MFCs) is rising tremendously but its commercialization is restricted by several microbiological, material, and economic constraints. Hence, a systematic assessment of the research articles published previously focusing on potential upcoming directions in this field is necessary. A detailed multi-perspective analysis of various techniques for enhancing the efficiency of MFC in terms of electric power production is presented in this paper. A brief discussion on the central aspects of different issues are preceded by an extensive analysis of the strategies that can be introduced to optimize power generation and reduce energy losses. Various applications of MFCs in a broad spectrum ranging from biomedical to underwater monitoring rather than electricity production and wastewater treatment are also presented followed by relevant possible case studies. Mathematical modeling is used to understand the concepts that cannot be understood experimentally. These methods relate electrode geometries to microbiological reactions occurring inside the MFC chamber, which explains the system's behavior and can be improved. Finally, directions for future research in the field of MFCs have been suggested. This article can be beneficial for engineers and researchers concerned about the challenges faced in the application of MFC. [Display omitted] • Basic principles and mathematical modeling of MFC technology are discussed. • Limitations in various electrode and membrane materials are outlined. • Mass transfer limitations as well the ways to overcome are provided. • All potential applications of MFC technology are included with case studies. • Need for MFC standardization to indicate power performance is explored. [ABSTRACT FROM AUTHOR]

[Display omitted] • Classifications and strategies of NZEBs were reviewed. • Multi-criteria analysis supports NZEB designing. • Renewable energy supply options for NZEBs are summarized. • Cost-benefit analysis and life cycle assessment needed for criterion evaluation. Decarbonizing the building sector is extremely important to mitigating climate change as the sector contributes 40% of the overall energy consumption and 36% of the total greenhouse gas emissions in the world. Net-zero energy buildings are one of the promising decarbonization attempts due to their potential of decreasing the use of energy and increasing the total share of renewable energy. To achieve a net-zero energy building, it is necessary to decrease the energy demand by applying efficiency enhancement measures and using renewable energy sources. Net-zero energy buildings can be classified into four models (Net-Zero Site Energy buildings, Net-Zero Emissions buildings, Net-Zero Source Energy buildings, and Net-Zero Cost Energy buildings). A variety of technical, financial, and environmental factors should be considered during the decision-making process of net-zero energy building development, justifying the use of multi-criteria decision analysis methods for the design of net-zero energy buildings. This paper also discussed the contributions of renewable energy generation (hydropower, wind energy, solar, heat pumps, and bioenergy) to the development of net-zero energy buildings and reviewed its role in tackling the decarbonization challenge. Cost-benefit analysis and life cycle assessment of net-zero energy building designs and their challenges were reviewed to shape the priorities of future development. It is important to develop a universal decision instrument for optimum design and operation of net-zero energy buildings. [ABSTRACT FROM AUTHOR]

• A risk-averse deep learning method for real-time emergency load shedding. • Deep learning algorithm to enhance prediction accuracy on load shedding strategy. • A shedding amount inference model to speculate the stability after load shedding. • A risk-averse loss function to minimize the risk of load under-cutting events. • Experiments on multiple power systems to verify the practical values of the method. Emergency load shedding is an effective and frequently used emergency control action for power system transient stability. Solving the full optimization models for load shedding is computational burdensome and thus slow react to the intense system variations from the increasing renewable energy sources and the more active demand-side behavior. Other sensitivity-based methods impair the control accuracy and may not guarantee global optimality. Artificial intelligence methods, as the data-driven approaches, have recently been well-recognized for its real-time decision-making capability to tackle the system variations. The existing artificial intelligence methods for emergency load shedding are based on shallow learning algorithms and can lead to both load under-cutting and over-cutting events. However, when the loads are under-cut, the power system will be exposed to a high risk of post-control instability that can propagate into cascading events, which incurs significantly higher cost than an over-cutting event. Being aware of such unbalanced control costs, this paper proposes a risk-averse deep learning method for real-time emergency load shedding, which trains deep neural network towards the reluctance to load under-cutting events, so as to avoid the huge control cost incurred by control failure. The case studies on two renewable power systems demonstrate that, compared to the state-of-the-art methods, the proposed risk-averse method can significantly improve the control success rate with negligible increase in prediction error, ending up with lower overall control cost. The results verify the enhanced control performance and the practical values of the proposed method. [ABSTRACT FROM AUTHOR]

Rather than using individually distributed energy storage frameworks, shared energy storage is being exploited because of its low cost and high efficiency. However, proper sizing and operations approaches are still required to take advantage of shared energy storage in distribution networks. This paper proposes a bi-level model to optimize the size and operations of shared energy storage in hybrid renewable-resource power generation systems. The upper-level model maximizes the benefits of sharing energy storage for the involved stakeholders (transmission and distribution system operators, shared energy storage operators and the various power plant owners) and the lower-level model minimizes the hybrid system operating costs. The benefits of this system were found to be: (1) reductions in wind and solar power curtailment and coal-fired generation costs; (2) peak shaving; (3) frequency regulation; and (4) a deferral of upgrades to power transmission and distribution facilities. To fully realize the long-term planning and short-term operational interactions of shared energy storage, a bi-level nested genetic algorithm was designed to solve the proposed model. By continuously updating the solutions, a set of optimal solutions were generated. To validate the approach, numerical tests were conducted, with the results showing that by properly sizing and operating the shared energy storage in distribution networks, the wind curtailment rate was reduced by about 10.2%, the solar curtailment rate was reduced by 14.2%, and the stakeholder benefits were around 154 million dollars. Further discussion is given on the benefits of shared energy storage investments. • Shared energy storage is planned and modelled in distribution networks. • A bi-level model is established for shared energy storage sizing and operation. • An interactive bi-level nested genetic algorithm is designed. • A comparative analysis is conducted to validate the shared energy storage feasibility. [Display omitted] [ABSTRACT FROM AUTHOR]

Biofuel manufacturing from renewable biomass through a smart manufacturing system is an important alternative to fossil fuels, which helps to decrease dependability on conventional fuel and decrease carbon emanations. By utilizing efficient labors, smart machines, and minimized energy utilization, the conventional biofuel manufacturing framework can be converted into a smart sustainable manufacturing framework. The existing conventional fuel demand can be replaced by biofuel. This study efforts to make pure biofuel with less amount of carbon emanations and energy utilization through a smart multi-type biofuel manufacturing framework, where the demand of the biofuel is selling price dependent. The different energy costs including air handling cost, lighting cost are calculated in this work-in-process inventory. The carbon emissions cost is included in every stage of this model. A variable demand has introduced for the maximization of profit. To reduce energy consumption and carbon emissions, a two-stage inspection cost with a variable manufacturing rate is taken to make the manufacturing process flexible such that the amount of impure biofuel is minimized. Although a random manufacturing rate is applied through a smart manufacturing system, still impure biofuel is manufactured. The impure biofuel is remanufactured again through refining just after the well-planned manufacturing ends. In this model, the multi-delivery technique is used such that a fixed amount of biofuel of n segments of the pure biofuel is transported to market places at predetermined intervals at the time of delivery frame. The classical optimization technique is used for continuous and differentiable variables and the mixed integer programming technique is utilized for discrete variables to maximize the total profit globally. To validate the model's usefulness, four numerical observations are studied. It is shown that by utilizing the said procedure, the percentage of impure biofuel can be decreased truly through the minimized energy consumption. The graphical representation and the sensitivity experiment show the impact of every parameter with the whole cost of the research paper. Numerical results assist to get the maximum profit and the optimum selling price of biofuel globally in a sustainable smart multi-type biofuel manufacturing system. [Display omitted] [ABSTRACT FROM AUTHOR]

The potential of renewable energy should be fully exploited in the transportation sector to achieve a cleaner production. Therefore, this paper proposes an on-grid hybrid hydrogen refueling and battery swapping station powered by wind energy. This novel concept can promote the development of low-carbon emission vehicles including hydrogen-based vehicle and battery electric vehicle. During the daily operation of the station, the multiple uncertainties may lead to a higher operational cost. To address this problem, a hybrid stochastic/distributionally robust optimization method is proposed to handle different uncertainties for the energy management problem. The first type of uncertainties can be depicted by a certain distribution, i.e. electricity price and wind power, which is processed by a stochastic optimization method. The second type of uncertainties is associated with human behaviors and is difficult to find its probability distribution, i.e. the hydrogen demand of hydrogen-based vehicles, so the second type is processed by a distributionally robust optimization method. The overall objective is to minimize the total operational cost of the station, which also considers the battery swapping station overstock punishment. Because a reasonable battery swapping scheduling can reduce the waiting time of users and operational cost of the station. The results indicate that the proposed method can effectively address the conservatism of solutions as its total operational cost is 4.4% lower than that of the hybrid stochastic/robust optimization method under a high confidence level. • An on-grid hybrid hydrogen refueling and battery swapping station is introduced. • A hybrid stochastic/distributionally robust method is proposed. • A chance-constrained energy management model for the hydrogen balance is developed. [ABSTRACT FROM AUTHOR]

• End-use of electricity consumption in residential buildings in Gulf Cooperation Council countries was presented. • Discrepancies in certain indicators have been observed by the end-use of electricity consumption. • Improving the efficiency of air conditioners and thermal insulation of the walls are the best measures to conserve electricity in residential buildings. • The results can be used in areas with a similar climate, living standard, buildings types, and the inhabitants' behavior. Energy conservation and improvement in energy efficiency yield multiple technical, economic, environmental, and social benefits and realize comfort and sustainability of residential buildings (RBs). This review paper presents the end-use of electricity consumption in RBs and the results of economic analysis. In addition, a description and discussion of studies on energy conservation measures, Net Zero Energy Building, and renewable energy in RBs are discussed. The percentage of end-used energy consumption for different purposes obtained in many studies are often not consistent. Moreover, discrepancies in certain indicators have been observed, necessitating the need to clarify the factors in the consumption of electricity in RBs. Further, a few studies used the term energy consumption in buildings (total or electrical); however, specifying the type of buildings to identify and use the necessary indicators of energy consumption in each type of building are desirable. Furthermore, most studies emphasized improving the efficiency of air conditioners and thermal insulation of the walls as these are the best measures to conserve electricity. [ABSTRACT FROM AUTHOR]

[Display omitted] • We simulate renewables pathways under different support phase-out scenarios. • We extend an investment algorithm by preferences and calibrated returns. • Our improved algorithm incorporates more heterogeneity resulting in stronger effects. • Further auction support is necessary for most capacity targets in the case countries. • Countries should coordinate policy changes due to cross-border effects. Support schemes have been central to the expansion of renewable electricity globally and in the European Union. As technologies mature, individual member states may decide to phase out these policies. While previous research has shown that such policy changes affect investors' decisions, we investigate how they affect pathways and electricity prices by simulating investment decisions in an agent-based model in two case countries. This paper contributes and applies an adapted investment decision algorithm that incorporates empirically observed technology and return preferences and is calibrated by return observations. The new algorithm yields more refined and stronger effects compared to its predecessor. Results show that the phase-out of auctions in Germany and the Netherlands slows down their deployment of renewable capacity by up to ∼60% and ∼35%, respectively. With the exception of photovoltaics and onshore wind projects in the Netherlands, the targeted capacities can only be reached by continuing support in both countries. Furthermore, ending support in a large country like Germany leads to higher electricity prices and fosters a market-driven but insufficient capacity expansion in smaller neighbours like the Netherlands. As the electricity grids in many countries are strongly interconnected, such cross-border effects are of international relevance. Our findings suggest that continued auctions may be necessary and that countries should coordinate policy changes to stay on track for meeting their renewables targets. [ABSTRACT FROM AUTHOR]

This paper presents a validation of atmospheric reanalysis data sets for simulating onshore wind generation time series for large-scale energy system studies. The three reanalyses are the ERA5, the New European Wind Atlas (NEWA) and DTU's previous generation European-level atmospheric reanalysis (EIWR). An optional scaling is applied to match the microscale mean wind speeds reported in the Global Wind Atlas version 2 (GWA2). This mean wind speed scaling is used to account for the effects of terrain on the wind speed distributions. The European wind power fleet for 2015–2018 is simulated, with commissioning of new wind power plants (WPPs) considered for each year. A generic wake model is implemented to include wake losses that are layout agnostic; the wake model captures the expected wake losses as function of wind speed given the technical characteristics of the WPP. We validate both point measurement wind speeds and generation time-series aggregated at the country-level. Wind measurements from 32 tall meteorological masts are used to validate the wind speed, while power production for four years from twelve European countries is used to validate the simulated country-level power production. Various metrics are used to rank the models according to the variables of interest: descriptive statistics, distributions, daily patterns, auto-correlation and spatial-correlation. We find that NEWA outperforms ERA5 and EIWR for the simulated wind speed, but, as expected, no model is able to fully describe the auto-correlation function of the wind speed at a single point. The mean wind speed scaling is found to be necessary to match the distribution of generation on country-level, with NEWA-GWA2 and ERA5-GWA2 showing highest accuracy and precision for simulating large-scale wind generation time-series. • Several reanalyses (ERA5, NEWA and EIWR), with and without micro-scale wind speeds scaling. • Wake losses modeled based on information of individual wind power plants. • Measured power generation is not used as part of the model calibration. • Microscale wind speed scaling reduces the bias and model uncertainty of the wind speed predictions. • ERA5 produces accurate large-scale wind energy generation simulations. [ABSTRACT FROM AUTHOR]

• The optimal allocation of PV and DSTATCOM. • Cost minimization and net saving maximization. • Applying the two recent optimization algorithms for solving the studied optimization problem. • Determining the candidate losses based on sensitivity analysis. • Studying the annual total cost reduction at different penetration levels. This paper introduces an efficient two-stage procedure for assigning the optimal sites and sizes of the photovoltaic-based distributed generator (PV-DG) and distribution static synchronous compensator (DSTATCOM). This procedure is applied for radial distribution network (RDN) under seasonal variations of irradiance and load demand. In the first stage, the voltage loss sensitivity index (VLSI) is utilized to identify the high potential buses for integration of the PV-DG and DSTATCOM while in the second stage, two recent optimization algorithms including the lightning attachment procedure optimization (LAPO) and the equilibrium optimizer (EO) are utilized to identify the best sizes and locations of the PV-DG and DSTATCOM from the candidate nodes. The proposed algorithms are tested on a 118-bus distribution system and the obtained results are compared with the Whale optimization algorithm (WOA) and sine cosine algorithm (SCA). The computational study shows the superiority of the application of the new optimization techniques (LAPO and EO) compared with WOA and SCA. [Display omitted] [ABSTRACT FROM AUTHOR]

This paper investigates the application of a passive harmonic filtering to improve the harmonic-constrained hosting capacity (HC HC) of renewable distributed generation (DG) systems in harmonically polluted distribution systems. Concurrently, the optimal parameters of a C-type passive filter (CTPF) and the rating of the DG system are set. The optimum HC of the distorted grid for DG is achieved while reducing total filter cost (FC) to a minimum. Using the newly suggested multi-objective Pareto-based bat algorithm (pb-MOBA), a best-of-breed solution is obtained. The formulated problem of multi-objective optimization (MOO) is dealt with under a number of system performance indices (PIs) constraints such as individual as well as total harmonic distortion (THD) in the line current and the point of common coupling's (PCC) voltage, distribution line ampacity under harmonic current overloading, steady-state voltage profile, load power factor (PF), and so on and a handful that is linked to the passive power filter (PPF) itself. The Pareto optimal front is produced using the MOBA extension, and the trade-off between the objectives is examined. The efficiency and accuracy of pb-MOBA, in dealing with the designated MOO problem, is confirmed by making a comparison of the acquired solution and three computed PIs viz. diversity metric (DM), generational distance (GD), and convergence metric (CM) with those achieved by Multi-objective Pareto-based PSO (pb-MOPSO), Non-dominated Sorting Genetic Algorithm (NSGA). [ABSTRACT FROM AUTHOR]

The classical optimal power flow problem is usually formulated with only thermal generators, in which the fuel used to generate power is limited and emissions from the network system are often ignored. Due to several promising features like renewability, richness, and cleanness, renewable energy sources have been drew growing attention. As a result, more and more renewable energy sources are penetrated into the electrical grid. In this paper, the standard IEEE-30 bus system is modified by integrating renewable energy sources as the case study, where the traditional thermal generators on buses 5 and 11 are replaced by wind generators, and bus 13 is replaced by solar generators. In addition, to address the intermittence and uncertainty of renewable sources, the Weibull probability density function is used to calculate the available wind power. Meanwhile, the lognormal probability density function is employed to calculate the available solar power. The optimal power flow with stochastic wind and solar energy is formulated as a multi-objective optimization problem. A multi-objective evolutionary algorithm based on non-dominated sorting with constraint handling technique are presented to solve it. In addition, another larger test system i.e., IEEE-57 bus system is selected to further verify the performance of the proposed approach in handling large dimensional problem. Simulation results indicate that proposed approach can obtain competitive compromise solution on different optimization objectives. • OPF incorporating renewable energy sources such as wind and solar is formulated. • The intermittent and uncertain of wind and solar power is matched by different density function. • The superiority of feasible solutions (SF) is used to effectively handle constraints in OPF. • Two effective constraint multi-objective methods are proposed. [ABSTRACT FROM AUTHOR]

The growing concerns about environmental impacts, electricity costs, and energy required of data centers are mounting. To overcome these challenges, the data center needs to integrate high-efficiency and environmentally friendly technologies to cover these issues. The combined cooling, heating, and power (CCHP) system refers to the synchronous generation of electricity and valuable heating and cooling energy through multi-energy conversion, which can address many of the data center's challenges. Hence, this paper focuses on the optimal energy management of the CCHP-based data center integrated with renewable energy, multi-energy storage, and multi-energy conversion to minimize daily electricity fees. The presence of multiple electrical and thermal consumptions in the data center creates an ideal opportunity to implement integrated demand response (IDR) for both types of demands. Besides the local workload in the data center, the operator seeks to optimize the resource scheduling to serve the electrical, heating, and cooling demands in nearby buildings and participate in the gas and power markets. Hence, the robust framework is extended in the proposed energy management to control the market price uncertainty, which denotes the decision-maker's conservatism level. The proposed model is examined in the sample data center structure. Simulation and results are carried out in the GAMS software environment and solved via CPLEX solver, which is discussed for several cases studies. Under the coordinated robust energy management with the incentive-based IDR and multi-energy conservation technologies, a 13.2% cost-saving can be achieved for the data center. • Proposing the optimal energy management strategy for the CCHP-based data center. • Providing the IDR for both electrical and heat demands in the data center to enhance the system's flexibility. • Introducing robust optimization in data center's energy management model to capture price uncertainty. • Investigating multi-energy technologies to serve data center's workloads, besides consumptions of nearby buildings. [ABSTRACT FROM AUTHOR]

• Cloud computing technologies applied to microgrid management. • Supervised machine learning strategies associated with the energy management system. • Used architecture by the energy management system to control a cluster of microgrids. • Considered elements in developing a scalable and autonomous architecture. The way to organize the generation, storage, and management of renewable energy and energy consumption features has taken relevance in recent years due to demands that define the social welfare of this century. Like demand increases, other factors require grid infrastructure improvement, updates, and opening to other technologies that assuage the final customer needs. Precisely, the interest in renewable energy sources, the constant evolution of energy storage technologies, the continuous research involving microgrid management systems, and the evolution of cloud computing technologies and machine learning strategies motivate the development of this article. Tasks associated with a microgrid cluster like the integration of a considerable number of heterogeneous devices, real-time support, information processing, massive storage capabilities, security considerations, and advanced optimization techniques usage could take place in an autonomous and scalable energy management system architecture under a machine learning perspective running in real-time and using Cloud resources. This paper focuses on identifying the elements considered by different authors to define a cloud-based architecture and ensure the appropriately supervised learning functionality under a microgrids cluster environment. Namely, it was necessary to revise and run microgrid simulations, real-time simulation platforms usage, connection to a virtual server for microgrid control and set the energy management system using cloud computing and machine learning. Based on the review and considering the scenarios mentioned, this article presents a scalable and autonomous cloud-based architecture that allows power generation forecast, energy consumption prediction, a real-time energy management system using machine learning techniques. [ABSTRACT FROM AUTHOR]

The high penetration of renewable energy sources in modern power systems introduces opportunities and challenges to power system operators. The frequency, which is a global variable in power systems, is being affected due to the increase of the renewable energy shares. This paper suggests dynamic models of power systems for assessing the frequency response due to different types of disturbances and uncertainties from renewable energy sources. The introduced model is built based on the sub-transient model of synchronous generating units. Likewise, a simplified system frequency response model is presented, in which the demand-side participation in providing ancillary services is considered. Different types of power systems are considered to verify the introduced models. The results show the effectiveness of the presented models in evaluating the impact of different parameters on power system frequency response. The impacts of increasing the renewable energy shares and its uncertainties are also investigated. [ABSTRACT FROM AUTHOR]

• It evaluates the effect of China's ETS on non-fossil energy development. • It analyzes ETS's effects from energy type, pilot, and pilot-energy type aspects. • The ETS has the most significant promoting effect on hydropower and photovoltaics. • Carbon price plays a positive role in moderating the effect of China's ETS. Emissions Trading Scheme (ETS) can internalize the environmental costs of fossil energy consumption and encourage enterprises to consume non-fossil energy, which is crucial for People's Republic of China (PRC) to achieve the contribution target of the Paris Agreement cost-effectively. However, whether the ETS in PRC can promote non-fossil energy development remains to be answered. This paper uses a difference-in-differences model to evaluate the effect of the ETS on non-fossil energy development in PRC. Using provincial monthly panel data from January 2004 to December 2019, we find that (1) the ETS has significantly promoted non-fossil energy development in PRC, increasing the monthly average share of non-fossil energy power generation in total power generation by 2.326 percentage points, which has been verified by a series of robustness tests. (2) The effect of the ETS on non-fossil energy development is heterogeneous by energy type, pilot region, and pilot-energy type. The ETS has significantly increased the share of hydropower and photovoltaic power generation, and has significantly promoted non-fossil energy development in Guangdong; meanwhile, the ETS has significantly increased the share of hydropower and nuclear power in Guangdong, and the share of wind power and photovoltaic power in Hubei. (3) The higher the carbon price, the greater the role of the ETS in promoting non fossil energy development. [ABSTRACT FROM AUTHOR]

Optimization algorithms are tools used in the planning and operations of renewable energy-based distributed power systems. Mixed integer linear programming as a classical optimization method is considered in the literature for sizing nanogrid systems due to simplicity and speed. However, the method has limited capabilities in implementing multi-configurational analysis and requires large formulations. In this paper, nested integer linear programming is proposed to decompose the large formulations and simplify the multi-configurational sizing of residential nanogrid in a semiarid zone. The proposed method is aimed at optimal sizing for energy cost reduction and increased supply availability. The method is implemented in multi-stage hybridization of relaxation and integer methods of linear programming to achieve optimal sizes of the nanogrid components using photovoltaics, wind turbines, and battery. The method realizes $72,343 net present cost and 0.3755 $/kWh levelized cost of energy indicating 33% and 11% reductions compared to mixed integer linear programming and particle swarm optimization. System availability of 99.97% is envisaged to achieve 6677–7782 kWh per capita electricity consumption in residential buildings against the existing 150 kWh. Three configurations analyzed indicated the robustness of the proposed method and the multi-configurational designs clarify options against factors such as space, logistics, or policies. • Nested integer linear programming algorithm is proposed for nanogrid sizing. • The method improved the reference algorithm through multi-configurational designs. • Levelized costs of energy were reduced by 7.7–39.7% in the three configurations. • The net present costs were reduced by 11.2–46.4% in two configurations. • The method optimized the nanogrid's capacity by 54.8–68.1%. [ABSTRACT FROM AUTHOR]

Averting the impending harms of climate change requires to replace fossil fuels with renewables as a primary source of energy. Non-electric renewable potential being limited, this implies extending the use of electricity generated from wind and solar beyond the power sector, either by direct electrification or synthetic fuels. Modeling the transformation towards such an energy system is challenging, because it imposes to consider fluctuations of wind and solar and the manifold ways the demand side could adjust to these fluctuations. This paper introduces a graph-based method to formulate energy system models to address these challenges. By organizing sets in rooted trees, two features to facilitate modeling high shares of renewables and sector integration are enabled. First, the method allows the level of temporal and spatial detail to be varied by energy carrier. This enables modeling with a high level of detail and a large scope, while keeping models computationally tractable. Second, the degree to which energy carriers are substitutable when converted, stored, transported, or consumed can be modeled to achieve a detailed but flexible representation of sector integration. An application of the formulation demonstrates that the variation of temporal detail achieves an average reduction in computation time of 70%. • A graph-based formulation for modeling renewable energy-systems is introduced. • The method allows the level of temporal and spatial detail to vary by energy carrier. • The degree to which energy carriers are substitutable can be modeled. [ABSTRACT FROM AUTHOR]

Nowadays, we are moving forward to more sustainable energy production systems based on renewable sources. Among all Photovoltaic (PV) systems are spreading in our cities. In this view, new models are needed to forecast Global Horizontal Solar Irradiance (GHI), which strongly influences PV production. For example, this forecast is crucial to develop novel control strategies for smart grid management. In this paper, we present a novel methodology to forecast GHI in short- and long-term time-horizons, i.e. from next 15 min up to next 24 h. It implements machine learning techniques to achieve this purpose. We start from the analysis of a real-world dataset with different meteorological information including GHI, in the form of time-series. Then, we combined Variational Mode Decomposition (VMD) and two Convolutional Neural Networks (CNN) together with Random Forest (RF) or Long Short Term Memory (LSTM). Finally, we present the experimental results and discuss their accuracy. • A combination of VMD and CNNs provide a novel methodology to forecast GHI. • GHI forecast aid the development of more accurate policies for energy management. • Methodologies to forecast GHI for short and long-term horizons are becoming crucial. [ABSTRACT FROM AUTHOR]

From the highest political ruling institute through the Amir of the State of Kuwait, the country announced its keenness to meet 15% of the local energy demand from renewable sources by 2030. Since the announcement in 2012, there has not been an effective assignment toward the goal. Up to date, the country's annual total energy generation from renewable technology systems is less than 1%. Furthermore, current indications imply that Kuwait will not reach the renewable energy target by 2030. This paper models the current system structure in pursuing the transition toward energy sustainability in Kuwait, focusing on renewable energy. The model development method is carried out by utilizing data and information on the performance and trends of Kuwait's energy system and related implications. Moreover, it incorporates efforts that local institutions have taken toward the development of a sustainable energy system. The model is based on a "push-pull" concept that identifies the main gaps in the current system and the barriers facing the deployment of renewable energy technologies. The development system lacks enablers involving legal and regulatory frameworks, supporting infrastructure, proper financing policies, and above all, a political will that fosters these enablers. Evidence suggests that renewable energy and clean technologies do not have sufficient potential impact on the country's economy and, therefore, they are trivial to the country's political system. Policy acts are suggested to imbed renewable and clean technologies within the country's economy to gain political popularity. Consequent implications may lead to profitable investments and social benefits, mitigating negative geopolitical impacts on the country's energy security. • Kuwait is targeting 15% of its energy mix from renewable sources by 2030. • Renewable target will not be met due to political system's lack of interest. • Renewable energy deployment process is modeled through factor-push/market-pull. • Policy measures are proposed to stimulate the political system. [ABSTRACT FROM AUTHOR]

This paper proposes a multi-level multi-energy management framework for the coordinated and interactive operation of heterogeneous multi-microgrids (MMGs) based on many-criteria optimality. With the proposed framework, the highly nonlinear and complex MMG multi-energy management (MMGMEM) problem is formulated into tri-level scheduling subproblems with multi-energy couplings and multi-level interactions, in which the multi-energy trading with energy networks and multi-energy couplings within MGs are optimized in the upper and middle level, and a middle level is added to correct scheduling decisions of the upper level for coordinating the MMG multi-energy sharing. Then, a multi-step matrix decomposition technique is developed to decompose the high dimensional multi-energy coupling matrix of MMGs into the sum of three linear and sparse submatrices for improving the computation efficiency and scalability. Furthermore, a many-criteria decision making (MCDM) model is proposed for the multi-energy sharing problem to achieve an optimum tradeoff in which all microgrids (MGs) can benefit from electricity-gas exchanges, and an evolutionary many-objective optimization based on hyperplane transformation algorithm is used to solve the MCDM problem. Simulation results verify that the proposed framework can achieve a cost saving for each MG (over 19%), and validate its scalability in solving large-scale MMGMEM problems. • A tri-level interactive framework is proposed for the coordinated operation of MMGs. • An extended multi-energy coupling matrix with MMG interactions is formulated. • A multi-step matrix decomposition technique is used to reduce the problem complexity. • A many-criteria optimality model is proposed for the tradeoff of all MGs' benefits. • The utilization of renewables can be enhanced with the minimal total operation cost. [ABSTRACT FROM AUTHOR]

The growing human population in this modern society hugely depends on the energy to fulfill their day-to-day needs and activities. Renewable energy sources, especially solar energy, can satisfy the global power demand while reducing global warming caused by conventional sources. Solar irradiance is an essential component in solar power applications. The availability of solar irradiance is influenced by several factors, such as forecasting horizon, weather classification, and performance evaluation metrics, which also need consideration. The accurate forecasting of solar irradiance is of utmost importance for the power system designers and grid operators for efficient management of solar energy systems. The intermittent and non-stationary nature of solar irradiance makes many existing statistical and machine learning approaches less competent in providing accurate predictions. In this context, deep learning models have been proposed by several researchers to reduce the limitations of existing machine learning models and improve prediction accuracy. In this work, an extensive and comprehensive review of deep learning-based solar irradiance forecasting models is presented. The effectiveness and efficacy of several deep learning models, including long short-term memory, deep belief network, echo state network, convolution neural network, etc. have been reviewed. The results obtained in the reported studies proved the superiority of deep learning models in solar forecasting applications. Few researchers have proposed deep hybrid models to improve the prediction performance further. A study reported that the hybrid of CNN-LSTM can enhance the prediction accuracy by 3.62%, 25.29%, 34.66%, 37.37% and 26.20% over CNN, LSTM, GRU, RNN and DNN, respectively. Overall, this paper offers preliminary guidelines for a detailed view of deep learning techniques that researchers and engineers can use to improve the solar photovoltaic plant's modeling and planning. [Display omitted] • A review of deep learning solar irradiance forecasting models is presented. • Factors that influence the accuracy of forecast models are also studied. • A comparative analysis of several deep learning models is conducted. • Hybrid deep learning models performed well over basic deep learning models. • Hybrid deep learning models are recommended for enhanced performance. [ABSTRACT FROM AUTHOR]

Women empowerment is counted as the fifth sustainable development goal known as gender equality (SDG 5), and it is affected by many factors. In East Sumba, renewable energy development as the seventh sustainable development goal (SDG 7: Affordable and clean energy) provides opportunities and challenges in empowering women. Rural microfinance institutions (MFIs) enable women to increase their empowerment by managing energy. This paper explores the direct and indirect (mediated) effect of renewable energy on women's empowerment via green MFIs. We employed Partial Least Squares-Structural Equation Model (PLS-SEM) with bootstrapping technique and Sobel test to perform the mediating effect. The findings showed the direct relationship of renewable energy on green MFIs and provided empirical evidence on the mediating effect of green MFIs. It confirms green MFIs sustainably, which operates based on the integration model of gender mainstreaming in the renewable energy sector. The results imply that the pro gender policy of local government in response to the availability of renewable energy supported by developing green MFIs. • Green microfinance institutions' transformation supported by renewable energy. • Green microfinance institutions enhance women's empowerment. • Green microfinance institutions mediate energy and gender integration. • Green microfinance institutions support pro gender policy. • There is a spillover effect from clean energy utilization on gender equality. [ABSTRACT FROM AUTHOR]

• Performance evaluation of GCPV system from monitoring of two years PV productions. • Calculation of PV derating factor from actual PV system output. • Error between simulation and actual performance is reduced using derating factor. • Performance of GCPV system increases 4.8% using optimal tilt angle and orientation. This paper presents the real performance of a 7.8 kWp grid-connected rooftop photovoltaic (PV) system from a field monitoring at a residential house under the feed-in-tariff scheme. The performance parameters of PV system were assessed based on the two-year energy production in 2018–2019. PVsyst software was used to find the best orientation and tilt angle for the selected area to increase the system performance. Next, the PV derating factor was calculated from the two-day data collection. The derating factor was used in the simulation of the system using HOMER Pro, and the economic parameters for 21 years project lifetime were predicted. The results show that the performance of the installed system was better for 2019 compared to 2018 due to the higher solar irradiance in 2019 and the malfunction of inverter in July to August 2018. The best performance ratio obtained from the recorded data was 75.72% and the average loss was 1.68 kWh/kWp/day. The annual capacity factor and overall system efficiency were in the range of 13%–16% and 10%–12%, respectively. The best tilt angle and orientation was 5° with the increment of annual energy production up to 4.8%. From the simulation, the error between the simulation and measurement results was high when the derating factor was assumed to be 100%. When the value used was based on the calculation obtained from data collection, the annual error was 1.7%, which is acceptable. Thus, the software is considered reliable assuming no component failure in the PV system. From the economic analysis, the annual net profit can be obtained was almost RM 3,000, and the payback period was in the range of 5–7 years. In addition, the installed PV system on the single house has the potential to reduce the CO 2 emission of approximately 7.45 tons/year. [ABSTRACT FROM AUTHOR]