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Highlights • Geospatial maps are used to estimate the charging load of electric vehicles in cities. • Three distinct charging profiles are assumed in the city: Home, Work, and Other. • Charging stations belong to a mixture of profiles depending on their nearby buildings. • Using 22 kW chargers resulted in a load with a peak of 1.47 kW/electric vehicle. • Fast charging causes high variability in the load when many cars start/stop charging. Abstract In the recent years, the number of electric vehicles (EVs) on the road have been rapidly increasing. Charging this increasing number of EVs is expected to have an impact on the electricity grid especially if high charging powers and opportunistic charging are used. Several models have been proposed to quantify this impact. Multiple papers have observed that the charging stations are used by multiple users during the day. However, this observation was not assumed in any previous model. Moreover, none of the previous models relied on geospatial maps to extract information about the parking lots—where charging stations are installed—and the charging profiles of the potential users of these charging stations. In this paper, a spatial Markov chain model is developed to model the charging load of EVs in cities. The model assumes three distinct charging profiles: Work, Home, and Other. Geospatial maps were used to estimate the charging profile, or mixture of profiles, of the charging stations based on the nearby building types. A case study was made on the city of Uppsala, Sweden—a city with approximately 44,000 cars. The results of the case study indicated that the aggregate load of the EVs in the city reduced the charging impact. For example when using 22 kW chargers, the peak load in the city per EV was estimated to be 1.29 kW/car in case of spatio-temporally opportunistic charging, and 1.47 kW/car in case of residential only opportunistic charging. This is to say that the Swedish grid operators can expect that every EV in the city will increase the peak load by at most 1.47 kW due to aggregation; this is assuming that 22 kW chargers were used. In addition, we showed that the minute-minute variability of the charging load in cities might cause some future challenges. In our case, up to 3% of the EVs in the city simultaneously started charging. This caused a one-minute-ramp in the charging load of 1.1 MW—if charging using 3.7 kW. Charging with higher powers will exacerbate these ramps, e.g., charging with 22 kW will cause sudden one-minute-increases as high as 6.7 MW in the charging load. Such a finding indicates that using high charging powers might cause high variability in the charging load of EVs in cities. This high variability might limit the synergy potentials between EVs and variable renewable energy sources (RES). The proposed model can potentially be used along with RES models to estimate the spatio-temporal synergy potentials between the two technologies. Evaluating the synergy potentials might be of value to grid operators, policy makers, market traders, etc. [ABSTRACT FROM AUTHOR]

Highlights • Multi-objective model for wind-solar-thermal power generation system is proposed. • Trade-offs between economic benefit and environmental impact is analyzed. • 12 scheduling scenarios to handle natural limits of renewable sources are considered. • Real case in China to demonstrate the model practicality and efficiency is presented. Abstract The integration of renewable sources into traditional power networks through the development of hybrid systems has attracted increasing international attention. However, simultaneously considering the environmental impacts, the economic benefits, and the natural limits of renewable sources in hybrid systems has proven difficult. This paper proposes an optimal scheduling strategy that fully considers the contributions of wind farm, solar parks, and coal thermal power plants to determine economic benefit and environmental impact equilibrium in a hybrid generation system under natural limitations. By factoring in the seasonal fluctuations in wind power, the weather driven characteristics of solar power, and the fuzzy coal thermal plant parameters, the optimal strategy better depicts the system characteristics than current strategies. A case study from Hami, China is presented to demonstrate the practicality and efficiency of the optimization model. The calculation results for twelve scheduling scenarios under different wind speeds and weather conditions found that this multi-objective strategy for hybrid generation systems was a superior method for solving the conflicts between emissions reduction and profits under natural renewable source limitations. Compared with previous studies, the optimal strategy this paper proposed is more applicable for developing countries such as China, and provide system operators with less calculation burdens. Management recommendations including the application of equilibrium scheduling strategy, policy support from the government, and improvements in solar power installed capacity have also been proposed. [ABSTRACT FROM AUTHOR]

Highlights • Introduction of new proposed indicators and consolidation of available ones. • Proposition of adaptable indicators to various Renewable Energy System modes. • A "cradle-to-grave" study of an integrated photovoltaic and storage system. • Introduction of Stakeholders' needs as part of the proposed Life Cycle Analysis. Abstract During the last years a variety of numerical tools and algorithms have been developed aiming at quantifying and measuring the environmental impact of multiple types of energy systems, as those based on Renewable Energy Sources. Plenty of studies have proposed the use of a Life Cycle Assessment methodology, to determine the environmental impact of renewable installations when coupled with storage solutions, based on a pre-selected repository of Key Performance Indicators. The main scope of this paper is to propose a limited number of best fitting, and at the same time easily adaptable to various configurations, list of Key Performance Indicators for the case of renewable energy systems. This is done by capitalizing on the environmental and energy performance indicators tracked in the open literature (e.g. "Global Warming Potential", "Energy Payback Time", "Battery Total Degradation", "Energy Stored on Invested", "Cumulative Energy Demand") and/or other proposing new simple, scalable and adaptable ones, (e.g. "Embodied Energy for Infrastructure of Materials and for the building system", "Life Cycle CO 2 Emissions", "Reduction of the Direct CO 2 emissions", "Avoided CO 2 Emissions", "CO 2 equivalent Payback Time"). Moreover, the proposed indicators are distributed according to the individual phases of the entire life-cycle of a related component of a renewable energy system, each time the environmental impact refers to, i.e. manufacturing, operational and end-of-life. Apart from that, the current paper presents a necessary base grounded approach, which can be followed for a holistic approach in environmental point of view of renewable-based technologies, by addressing the potential competing interests of the relevant stakeholders (e.g. profit for the market operator in contrast to low-cost services for the consumer). All in all, the scalar quantification of the environmental impact of multiple energy systems, through a list of proposed assessment criteria, being evaluated in terms of the selected repository of indicators, enables the comparison on a fair basis of the available energy systems, irrespective if they are fossil-fuel or renewable based ones. As a typical example, a simple standard model of a photovoltaic integrated with an electric battery is selected, for which indicative indicators are provided. [ABSTRACT FROM AUTHOR]

Green total factor productivity is a vital measure of sustainable progress, holding immense implications for attaining both economic growth and environmental preservation in a mutually beneficial manner. Focusing on green total factor productivity, this paper examines its nexus with low-carbon energy technology using the Instrumental Variable Generalized Method of Moments (IV-GMM) model, based on a panel dataset in China during 2005–2018. This paper also examines the asymmetric impact of low-carbon energy technology on green total factor productivity. In addition, the moderating and mediating effects are checked. The main findings are that (1) low-carbon energy technology is a promoting factor for green total factor productivity. (2) Low-carbon energy technology exerts a more prominent marginal impact on green total factor productivity when green total factor productivity is at higher quantile levels. (3) Renewable energy generation not only drives green total factor productivity directly, but also further increases the promotion effect of low-carbon energy technology on green total factor productivity, showing a positive moderating role. (4) Energy structure transition and industrial structure transition are two internal impact mechanisms between low-carbon energy technology and green total factor productivity. These findings suggest that the government can promote green total factor productivity development by developing policies related to low-carbon energy technology and fully releasing the effects of technology on sustainable development. • This paper assesses the level of low-carbon energy technology (LCET) in China. • This paper examines the effect of LCET on green total factor productivity (GTFP). • LCET plays a significant role in enhancing GTFP. • Renewable energy generation positively moderates the nexus between LCET and GTFP. • Energy structure transition and industrial structure transition are two mechanisms. [ABSTRACT FROM AUTHOR]

Power systems now face new issues due to incorporating electric vehicles (EVs) and renewable energy resources (RERs). This paper proposes a novel Enhanced Coati Optimization Algorithm (ECOA) for obtaining the optimal solution of the probabilistic optimal power flow (POPF) problems. The ECOA is a metaheuristic optimization algorithm that is robust and efficient for solving complex problems. It is used to tackle the OPF problem, which considers the stochastic characteristics of RERs. Moreover, EVs are included in the presented power systems in this paper. The novel approach is tested and verified on the IEEE-57 and IEEE-118 networks. The effectiveness of the proposed method is demonstrated by making a comparison with other metaheuristic-based methods. To obtain a practical study, real data of wind speed, solar irradiance, and electric vehicles profile are incorporated in the dynamic analyses. The simulation results show that the ECOA is robust and efficient for solving the OPF problem. It can also improve the performance of power systems with RESs and EVs. The findings of this research demonstrate that the suggested approach is promising for power system optimization problems, including RERs and EVs. • This paper presents a novel optimal energy management of smart grids. • The model contains renewables and electric vehicles uncertainties. • A practical study is presented including renewables and EVs real data. • A novel enhanced metaheuristic algorithm is proposed to solve the problem. • The effectiveness of the proposed method is compared with other methods [ABSTRACT FROM AUTHOR]

Distributed integrated energy system (DIES) will become the main energy supply method for end-users, however, the volatility of renewable energy makes energy consumption more difficult. To address the problem, this paper proposed an optimization method of DIES energy transaction based on Stackelberg game. Firstly, the typical structure of DIES is studied and a energy transaction framework of electric-heat energy is proposed by introducing integrated energy operator(IEO); secondly, with IEO as the leader and DIES as the follower, considering the highest profit of IEO and the lowest cost and carbon emission of DIES as the objectives, and combining the operational constraints of different subjects, a one-master-multiple-slave Stackelberg game model is established; thirdly, the existence of Nash equilibrium of this game model is demonstrated, and a solution algorithm based on Particle swarm optimization nested Non-dominated Sorting Genetic Algorithm-II is proposed; Finally, to verify the validity of this model, two modes are designed and simulated, and the results show that the proposed method can improve the renewable energy consumption by 2.92%, reduce the DIES energy cost by 3.94%, and reduce the interactive power with the grid by 30.91%, while the sensitivity analysis shows that the method can better cope with energy price changes. • The main contributions of this paper can be summarized as follows: • Analyzed the typical architecture of DIES and proposed a new energy trading framework. • A Stackelberg game model with IEO as the leader and DIES as the follower is developed. • The solution algorithm of PSO nested NSGA-II is proposed. • This method can reduce the cost of energy use and increase the consumption of renewable energy. [ABSTRACT FROM AUTHOR]

The trend towards reducing greenhouse gas emissions by dipping the use of traditional sources of energy in marine power grids, as well as the rapid expansion of renewable energy sources (RESs), were the driving forces behind the incorporation of RESs in maritime microgrid systems and enquiry of the ensuing prevalent control mechanism. The frequency stability of a Marine Microgrid System (MMGS) is a critical aspect that directly affects its reliable and efficient operation. This study describes a method for frequency stabilization in independent maritime microgrids consists of diverse renewable energy resources including wind turbine generators, sea wave energy/tidal power generation, solar generation, bio-diesel generator and energy storage systems. This paper aims to develop a new optimal cascaded order proportional integral based proportional derivative for marine load frequency control. Due to the fact that the performance of the controller is highly dependent on the parameters of the controller, optimizing these coefficients can have a significant impact on the output performance of the LFC control. In light of this, this paper presents the sewing training-based optimization (STBO), a new human-based metaheuristic algorithm to optimize the coefficient of the suggested controller. The essential stimulation of STBO is the teaching procedure of sewing to beginner tailors. By utilizing a population of candidate solutions, the algorithm iteratively refines the control parameters to find the optimal set that minimizes frequency deviations and enhances system stability. The responses of the cascaded PI-PD controller are linked to those of the PID and PI controllers in order to demonstrate its superiority. To evaluate the efficiency of STBO, it is compared to well-established recent techniques such as fitness dependent optimization, grey wolf optimization and Jellyfish search optimization. From the results, it is observed that our proposed approach improved the settling time by 25.35%, 45.89%, and 29.35%, reduced peak overshoot by 78.34%, 67.71%, and 78.23%, and similarly reduced undershoot by 81.56%, 56.22% and 76.56% as compared to FDO, GWO and JSO techniques respectively. Finally, the sensitivity analysis is performed under ±50% load variation and ± 40% power system parameters to prove the robustness of the proposed controller. • A diverse renewable energy resources including renewable energy resources and energy storage systems (ESS,s) incorporating with battery energy storage, capacitive energy storage, and flywheel energy storage system has been considered for marine microgrid. • A novel cascaded order PI-PD controller has been designed and implemented for the suggested distinct renewable and energy storage based maritime microgrid. • The coefficient of the proposed controller is optimized with a new human-based meta-heuristic algorithm known as sewing training-based optimization (STBO). • Multi objective functions including integral time absolute error (ITAE), integral time square error (ITSE) and integral square error (ISE) are formulated for the proposed power system. • Finally, sensitivity testing of the STBO adjusted PI-PD controller under uncertain parameters such as turbine gain variation (K CE), droop factor (R), and inertia constant (M) is performed to prove the robustness of the proposed frequency control technique. [ABSTRACT FROM AUTHOR]

Wind power prediction technology is important for building novel power systems with a high proportion of renewable energy. The quality of Numerical weather prediction (NWP) has a significant impact on the accuracy of ultra-short-term wind power prediction (USTWPP). However, existing NWP do not reflect the adaptability of different weather processes, because of it' s forecasting errors. In view of this, this paper proposes an USTWPP method based on NWP wind speed correction and division of transitional weather process. The combined prediction method was first used to correct the NWP wind speed, and then we use the double clustering method to divide the transitional weather processes to establish a model for USTWPP based on different scenarios, the overall method was finally applied to a wind farm in west inner Mongolia, China. Compared to the pre-correction, the wind speed forecasted RMSE was reduced by 1.702 and the MAE by 1.366. Based on the wind power ultra-short-term prediction method proposed in this paper, the average reduction in RMSE is 5.93% and in MAE is 4.82% compared to the various comparison methods in the four seasons. The USTWPP method combining wind speed correction and double clustering division of transitional weather scenarios can significantly improve accuracy of USTWPP. • Propose a new NWP correction mechanism to improve the NWP forecasted errors. • Propose a multiple clustering method to improve the wind power prediction accuracy. • Propose weighted evaluation indicators of sequential and non-sequential clustering. • It provides new ideas for wind power prediction under different weather scenarios. • Proposed power prediction idea reduces the RMSE and MAE on average 5.93% and 4.82%. [ABSTRACT FROM AUTHOR]

• A hierarchical approach is proposed considering quartile method for optimization. • Proposed CEEMDAN-DMD can regress the energy based on pre-set energy proportion. • Proposed hierarchical allocation strategy can be used for severe power volatility. The development of renewable energy dominated power system and the integration of large-scale wind-storage energy system are inevitable trends of the future society. This paper developed a new methodology of hierarchical power allocation strategy considering wind power fluctuation. A novel idea is presented that fully considers the fluctuation and energy proportion. Based on the Quartile Method (QM) and Hampel-Savitzky Golay (HSG) strategy, a layered-based method that considers the volatility interval to effectively overcome the limitations of a single strategy caused by severe volatility interval is proposed. Then, an improved power division method based on Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) and Dynamic Mode Decomposition (DMD) is proposed where CEEMDAN is used to decompose the Intrinsic Mode Function (IMF) of the remaining fluctuations. Furthermore, according to the required setting of energy proportion, DMD strategy is used for power fitting of the hybrid energy storage system (HESS). Through experimental verification, the hierarchical strategy proposed in this paper can effectively reduce the probability of severe volatility interval or distortion interval and ensure the sequence in the ideal interval. Moreover, the proposed CEEMDAN-DMD strategy can realize the power division according to the energy proportion demand. In model verification, the volatility improvement ζλ 1 min = 85.4766%. Set the energy demand ≥ 80%, the fitting energy proportion accounts for 87.7533%, which outperforms the traditional power division method. In addition, it is demonstrated that the theoretical method proposed in this paper is reliable and promising. [ABSTRACT FROM AUTHOR]

• A TDS-UC model incorporating the dynamic characteristics of electricity-gas-heat IES is proposed. • A distributed and parallel analytical target cascading algorithm is developed to decouple the integrated TDS-UC model. • An improved one-dimensional piecewise linear approximation method is employed to to linearize the Weymouth equation. The traditional hierarchical scheduling and operational model of transmission and distribution networks has failed to fully utilize the adjustable resources of the entire grid, consequently limiting the integration of renewable energy. To fully excavate the potential of transmission and distribution (T&D) coordination in enhancing the level of renewable energy assimilation and operational efficiency, in this paper, transmission grid and distribution network in the framework of synergetic unit commitment (TDS-UC) considering the dynamic characteristics of the electricity-gas-heat integrated energy system is proposed. The framework developed in this paper allows for the integration of multiple energy sources, including electricity, gas, and heat, into the dispatch strategy of the TG. To improve scheduling flexibility, the dynamic characteristics of the natural gas and heating networks are also considered in the TDS-UC model. A coordinated interactive optimization model for T&D is formulated, taking into account the multi-energy coupling relationship of the IES. To solve the TDS-UC model, a decentralized analytical target cascading algorithm is used to obtain an independent TG optimization problem and multiple ADNs local optimization problems that can be solved in parallel. The proposed model is transformed into an easy-to-solve mixed-integer linear programming (MILP) problem using an improved one-dimensional approximation piecewise linearization method to deal with the non-convexity nonlinear natural gas Weymouth equation. Numerical experiments are conducted on both a T6D2 and a T118D10 test system to demonstrate the efficiency and practicality of the proposed methodology. © 2017 Elsevier Inc. All rights reserved. [ABSTRACT FROM AUTHOR]

• A net-zero DN model composed of solely non-dispatchable DGs and BESSs has been considered to tackle severe windstorms. • The proposed framework is particularly effective in DNs with high shares of converter-based DGs. • The proposed three-stage resilience assessment can provide up to 25 percent load shedding reduction. • A detailed spatiotemporal representation of the DN was implemented to accurately estimate the status of the grid. Increasing incidences of extreme weather events pose significant challenges to the electrical grids in terms of the ability to withstand those high-impact eventualities. This paper proposes a resilience assessment framework for net-zero active distribution networks (ADNs) to tackle the impacts of extreme windstorms where only renewable energy resources (RESs) and battery energy storage systems (BESSs) are considered. To accurately capture the influence of windstorms on the grid, a detailed spatiotemporal representation of grid system assets and their exposure to the wind has been implemented. The suggested day-ahead resilience assessment is based on a three-stage approach. The first stage computes the probabilities of failure of each line and determines the most vulnerable ones. The second stage obtains the optimal grid configuration based on the outcomes of the first stage given the available non-dispatchable RESs and commits the available resources in each island to minimize the loss of load during the windstorm. If such a value is still larger than zero after the second stage, a novel voltage regulation scheme is applied in the third stage, taking advantage of the RESs and BESSs in each island. The proposed resilience assessment has been evaluated using the IEEE 33-bus test system with the meteorological data retrieved from an actual windstorm event occurred in the UK on the 20th of February. The outcomes of this paper underscore that a significant reduction in load shedding during such extreme events can be achieved, thus having a notable enhancement to the overall resilience of the system. Finally, the performance of this approach is compared with other resilience-oriented methods for windstorms, where the benefits of using the scheme reported in this paper are highlighted and quantified. [ABSTRACT FROM AUTHOR]

• The flexibility of power planning on external tie lines is greatly enhanced. • The novel flexible load aggregation model improves the accuracy response curve. • Strategies focus on the PFS in NZCPGs rather than the distribution and microgrids. • Offset the actual ETLPF to the largest extent and reduce NZCPGs operating pressure. In a net-zero carbon power grid (NZCPG) with a relatively high proportion of renewable energy sources, significant power fluctuations occur, severely constraining the development of both the NZCPG and the New Urban Power Grid (NUPG). In the NZCPG, the proportion of traditional hydro and thermal power units used to mitigate power fluctuations has decreased. It is necessary to explore the load resources in the NUPG to stabilize the power fluctuation between the NUPG and the NZCPG, and to reduce the operating pressure of the NZCPG. Firstly, this paper proposes a method for developing external tie line schedules based on the load operation band (LOB) to relax the constraints on the schedule for the NZCPG. Secondly, A revised load response aggregation model is proposed to improve the aggregation accuracy under uncertainty factors by online correcting their impact on the load response characteristics. Thirdly, considering the flexible loads in NUPG can only compensate for a part of the power fluctuation, this paper proposes a method to judge the power fluctuation that these loads can offset. Two PFS optimal strategies are proposed among NUPG partitions considering deviation penalty and within each NUPG partition reducing the number of participated load-users. This paper focuses on the PFS in bulk NZCPGs other than in the distribution grids and microgrids. Our work can effectively smooth the power fluctuation within NUPG regulation capability meanwhile reducing the number of participated load-users. Fourthly, a method is proposed to judge the power fluctuation beyond NUPG regulation capacity in NUPG. Using the actual load response capacity, the over-amplitude/time layered smoothing strategy with optimal load is proposed, which can offset the power fluctuation of the bulk NZCPG to the largest extent. Finally, the IEEE-39 system is used to verify the effectiveness of the proposed methods. © 2017 Elsevier Inc. All rights reserved. [ABSTRACT FROM AUTHOR]

Electromagnetic Pulse (EMP) poses a significant threat to the normal operation of power systems, especially with the increasing penetration of renewable energy. Without adequate protection, EMP can severely damage equipment or result in circuit breakdowns or short circuits. Solar photovoltaic (PV) facilities are particularly susceptible to EMP since PV systems are outdoors and exposed to EMP radiation. To assess and mitigate this threat, this paper summarizes various models and tests used to study the effects of EMP on PV systems, assesses the nature of the threat, and identifies measures to mitigate it. The paper provides a comprehensive review by categorizing analytical methods based on their pros and cons, introducing and describing EMP-related events, and providing a taxonomy of analytical methods for studying EMP. This paper compares the processes of modeling, testing, and mitigating EMP at both the component and system levels of PV systems. It also presents a case study that reveals the vulnerability of solar inverters to EMP using the pulse current injection method. Finally, the paper discusses some of the remaining challenges that should be considered in future solar PV system design and deployment. • The impact of the Electromagnetic Pulse (EMP) on the PV system is discussed. • Modeling, testing, and mitigation strategies are summarized and compared. • A PCI case is given to reveal the immunity and vulnerability of solar inverters. [ABSTRACT FROM AUTHOR]

Shared energy storage is widely recognized as an energy hub for the coordinated operation of regional integrated energy systems (RIESs). Multi-energy systems (MESs) share centralized energy storage to store excess renewable energy sources (RESs). This paper proposes a novel energy-sharing framework considering hydrogen trading, which is suitable for residential, industrial, and commercial RIES structures. Shared energy storage operator (SESO) promotes hydrogen energy transactions by formulating time-of-use (TOU) hydrogen prices. The proposed hydrogen energy trading method can be regarded as a master-client structure, and a hierarchical optimal scheduling method based on the Stackelberg game relationship is designed. Finally, a case study is performed based on an optimal scheduling algorithm combining particle swarm optimization (PSO) and mixed integer linear programming (MILP). The cases show the electricity‑hydrogen shared energy storage mechanism in RIESs can improve the RESs utilization rate and effectively reduce the operating costs of each system. Moreover, compared with RIESs with a single centralized electric energy storage, the TOU hydrogen price mechanism can further lower the energy prices and improve the economy of the RIES. The cases show that hydrogen energy sharing reduces the operating cost of RIES by 9.96% and increases the regional energy utilization rate by 2.97%. And the shared energy storage business model can satisfy the economy of both SESO and RIES. It confirms the rationality of the electric hydrogen shared energy storage project. • A sharing energy storage mechanism in multi-energy systems is presented in this paper. • The hydrogen trading methods based on time-of-use hydrogen prices are considered in this paper. • A hierarchical optimal scheduling method based on the Stackelberg game is designed. [ABSTRACT FROM AUTHOR]

The district heating system (DHS) is one of the essential carriers for coordinating renewable energy with fossil energy and achieving flexible energy consumption. Considering the uncertainty of both the renewable energy units' power and the consumers' aggregate loads impact the transport process of the DHS, the thermal characteristics of the DHS transportation and the node temperature response under the source-load uncertainty need to be quantified and analyzed. This paper proposed a method to calculate the nodes' temperature dynamic response of the DHS under multiple source-load uncertainty scenarios. The prediction and combination methods of supply and demand probability intervals of source and load nodes under varying credibility levels were investigated. Additionally, the effects of source-load uncertainty boundary conditions on the DHS transport process were analyzed. The temperature response of the nodes was calculated based on the established dynamic DHS model under multiple source-load uncertainty scenarios. A DHS in Beijing was selected for case validation and analysis, and it has 90 nodes and 109 pipes. The simulation results showed that the relative average error of node flow rate in the dynamic DHS model was 3.02%, and the average difference in node temperature was 1 °C. The probability distribution semi-analytical method and the Bayesian credible interval calculation method can accurately describe the uncertainty on both source and load sides. The models and algorithms proposed in this paper could effectively quantify the nodes' dynamic temperature response under the source-load uncertainty. • Proposed uncertainty quantification models for the source and load nodes of DHS. • Developed a novel credible interval calculation method for nodes' thermal power. • Quantified nodes' temperature dynamic response considering source-load uncertainty. [ABSTRACT FROM AUTHOR]

This paper proposes a novel method for integrating renewable energy sources (RES) and power-to-gas (P2G) technologies to optimize energy utilization efficiency as RES capacity increases. The method introduces the potential RES penetration index (PRPI) to select the appropriate capacity of P2G facilities for improved RES acceptance rates. In order to establish input constraints for RES and P2G facilities, this study determines the saturation point of RES (R-SP) and the saturation point of P2G (PG-SP). Additionally, the paper introduces the marginal capacity rate of RES to P2G (MCRG) as a useful tool for evaluating the adequacy of planned facilities. This paper's index is limited to PV as a renewable energy source. By incorporating PRPI into the input plan of P2G facilities, the proposed method improves PV's acceptance rate, contributes to facilities' economic planning, and streamlines optimization complexity. Previous studies have demonstrated the potential for P2G technology to optimize the integration of PV into the grid. However, the proposed method considers the PV's acceptance rate and provides system planners with necessary information on RES and P2G facility capacity. The method proposed in this paper can be extended to include other RES. • Novel Index for PV-P2G integration (PRPI). • Enhancing Acceptance rate of PV applying PRPI • Reduced optimization complexity. • Adaptable to various renewables. [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]

[Display omitted] • This paper presents a new dynamic 2D fusion model and analysis method for analyzing the dynamic characteristics of floating photovoltaic modules under continuous changes in motion, irradiance, and temperature caused by actual wind and waves conditions. • The coupling relationship between the motion state and the environmental factors is established to achieve accurate measurement of floating photovoltaic modules. • The complementary filter is used to eliminate the low-frequency interference signals of the gyroscope and the high-frequency interference signals of the accelerometer. • A new dynamic 2D fusion model of floating photovoltaic modules is set up to obtain the dynamic output characteristics, which integrates mechanical motion state information with the electrical parameters. • The experimental results show that proposed model and analysis method have high performance, and power generation efficiency can be increased by 2.5%. As a new application form, floating photovoltaic power generation is a green and sustainable energy source with immense development potential. However, built on marine or riverine surface, the floating photovoltaic system not only faces challenges from environment factors that land-based photovoltaic system meets, but also faces challenges from the motion of the floating bodies which are caused by the continuous external forces of wind and waves. Modelling and analyzing the output characteristics of floating photovoltaic modules is desirable and useful to improve power generation efficiency. This paper presents a new dynamic 2D fusion model and analysis method for analyzing the dynamic characteristics of floating photovoltaic modules under continuous changes in motion, irradiance, and temperature caused by actual wind and waves conditions. To achieve accurate measurement of floating photovoltaic modules, the coupling relationship between the motion state and the environmental factors is established. Then the motion state modeling of floating photovoltaic modules based on multi-sensors information fusion is established to acquire the absolute attitude angle with the continuous superposition of high and low frequency motion in the geographical coordinate system. On this basis, a new dynamic 2D fusion model of floating photovoltaic modules is set up to obtain the dynamic output characteristics, which integrates mechanical motion state information with the electrical parameters. Finally, the proposed modelling and analyzing method is evaluated with experimental data. The experimental and analytical results show that the accuracy of the 2D fusion model is higher than 98.7%, the output efficiency of floating photovoltaic modules is only 94.2% compared to the static state, and power generation efficiency can be increased by 2.5% by using the proposed method. The new modelling method proposed in the paper can accurately obtain the output characteristics of the floating photovoltaic system under actual operating conditions, which can effectively improve the efficiency of power generation. [ABSTRACT FROM AUTHOR]

[Display omitted] • A wireless-energy green city concept integrating WPT, renewable energy, and infrastructure is proposed. • Security-electrification, digitization, and intelligence are bolstered by the incorporation of WPT into urban settings. • The SDGs are advanced by the wireless-energy green city's energy, environment, and social economy benefits. The significance of energy stability in urban development has increased as the use of intelligent devices continues to expand and extreme weather poses a threat to the integrity of electric networks. Wireless power transfer technology is well-known for its dependability, security, and adaptability, and has reached billions of market share in China alone in the cell phone and electric vehicle sectors over the past decade, with the electric vehicle wireless charging market expected to grow at a compound annual growth rate of over 30% by 2023. Combining WPT with renewable energy, electric vehicles, and recharge infrastructure is a promising new application for smart city development. This paper examines the history, characteristics, and six classifications of WPT technology through the lens of urban development, in addition to its successful implementations in seven contexts, such as portable devices and transportation, based on a survey of 161,425 papers. We propose a dependable and efficient research-to-application life cycle for high-power WPT operations, which is capable of achieving efficiencies of approximately 90 percent at approximately 50 percent of the original cost. In addition, the significance of incorporating WPT technology, renewable energy sources, and urban infrastructure into the design of sustainable cities is highlighted. This study determined that WPT is advantageous for promoting the construction of space and ground photovoltaic plants, as well as photovoltaic rooftops, and also serves to accelerate the promotion of electric vehicles, smart devices, and charging roads. The findings of this study cast light on three problem areas and six future prospects of WPT, as well as its multifaceted benefits in terms of energy, the environment, and the social economy of sustainable cities. [ABSTRACT FROM AUTHOR]

Integrating electric vehicles (EVs) into the electric power system poses significant challenges to grid operation and planning due to the potential constraints on the power system. To minimize grid congestion, the best option is to increase the production of photovoltaic (PV) energy for domestic use and EV charging stations. This paper addresses the nexus of technical, financial, and environmental effects of customer involvement in economic development and load management. In this paper, a new model design of solar-powered EV charging stations is proposed and implemented in HOMER Grid, and a case study has explored how economic, technical, and energy management benefits can be achieved through customer energy involvement and the integration of PV-based charging stations. The proposed PV-based charging stations contribute toward the energy management of the region, and the study observes the real-time optimal charging and discharging strategy of PV-based grid-connected charging stations. The case study results show that the investigated area can produce 1,070,804.096 MWh/year of energy through maximum customer involvement, consequently reducing overall energy costs. Moreover, the study demonstrates that the selected region can produce 86,961,688 kWh/year through the PV system, achieving 363,899 charging sessions per year, offering maximum advantages and facilitation for EV charging. The proposed model applicability shows that large-scale customer involvement can bring significant techno-economic benefits and attract regional investments. • This paper proposes a new design of a grid-tied renewable energy system. • It investigates the energy economics and environmental effects for a practical location. • The research examines energy management including PV systems and electric vehicles. • The results of proposed model are compared with other models. [ABSTRACT FROM AUTHOR]

Under the background of "carbon neutrality" and "carbon peak" concepts, China desires to develop a new power system based on renewable energy sources (RES), which will be the primary energy support in prospective China. Thus, it is imperative to improve the peak shaving capability of power system to address the problem of random fluctuation and intermittency of RES by developing various energy storage systems. An innovative approach to enhance the flexibility of the conventional thermal power unit (TPU) through the utilization of flywheel energy storage array (FESA) is presented, simulated, and evaluated in this paper. Using the relatively practical and specific model of TPU and FESA, modified frequency modulation and automatic generation control (AGC) instruction tracing operation scenario models are constructed. A comprehensive control strategy of FESA and a well-designed operation coordinated control method for TPU with FESA are proposed and applied. The simulation results are distinctively processed and evaluated by the methods in "two documents", aiming to assess the real-world operation performance of the model proposed in this paper. The comprehensive analysis of the indicators demonstrates that the FESA can improve the flexibility and economy of the units. This research may guide the usage of FESA to improve the operational flexibility of TPU. • Composition and Control Methods of the flywheel energy storage array are provided. • A coordinated control scheme for the thermal power unit with flywheel energy storage array is proposed. • Frequency modulation and AGC instruction tracking scenario models are constructed and simulated. • AGC regulation indicators are conducted and analyzed to evaluate the unit's performance. [ABSTRACT FROM AUTHOR]

In the present situation, renewable energy has become a vital source to mitigate energy crises and environmental threats. In order to ensure that women's contributions, knowledge and opinions represent a significant portion of the expanding energy sector, it is crucial to approach the development of renewable energy from a gender perspective. Bibliometric analysis was performed to identify prior research on two primary terms, "Gender" and "Renewable Energy", from 1995 to 2022. Identifying the most influential countries, authors, publications, journals, and co-occurrence of keywords helps to understand the evaluation and trends of the research through network visualisations by bibliometric approach. This paper presents a comprehensive bibliometric review of the influence of gender on renewable energy. The result of this paper gives details picture of the gender impact on renewable energy during the period from 1995 to 2022. The paper centred on bibliometric and cluster analysis based on the two most important databases, Web of Science and Scopus. The study involved utilising various bibliometric tools, including R Studio, VOSviewer, and Biblioshiny. The study revealed an increase in publications and citations during the third period since 2017, after the United Nations set the target of the Sustainable Development Goal. Most influential countries and productive authors belong to China, the United States of America, and the United Kingdom. Energy Research and Social Science are predominated in publications on the research topic. In addition, the co-occurrence of keywords found major keywords related to this topic, such as renewable energy, gender, women, knowledge, sustainability, climate change and sustainable development. Finally, the study observed a huge research gap because developing nations are more affected by the lack of gender-influencing policies on the growth of renewable energy. Thus, the study provides a comprehensive overview of research in this field that can help researchers and stakeholders identify potential ways for future research in gender considerations on renewable energy access by formulating effective policies to change society and reach the Sustainable Development Goals. • Gender has become a focal point of interest within the renewable energy sector. • Using network visualisation, bibliometric analysis examines research trends by keyword occurrence and country collaborations. • Publication, citation, journal, and authorship contribution trends are to be analysed. • The research growth across different periods was discussed for potential future studies. [ABSTRACT FROM AUTHOR]

• A new efficient control strategy for superconducting magnetic energy storage (SMES) systems is proposed. • Currently, SMES systems are widely integrated in modern power systems due to their economic and technical advantages. • The problem is how to mitigate the fluctuation in the power generated by hybrid power generation systems caused by the variable weather conditions. • A SMES system with its controllers was proposed in this research in order to solve the mentioned problem. • The proposed SMES system with its controllers is very effective in smoothing out the fluctuations of power provided by the hybrid power generation system to the power grid. Recently, with the rising electrical energy demand, renewable energy resources (RERs) are considered an optimal solution for satisfying consumption needs. The power generated from RERs has an intermittent and unpredictable nature, given that it depends directly on weather conditions. Thus, the amount generated power from RERs will be taken in fluctuated curve form. This paper focuses on addressing the issue of fluctuation in the power output of a Solar-Wind hybrid power generation systems (HPGSs) resulting from variable weather conditions. Superconducting magnetic energy storage (SMES) system provide a viable solving to the issue of power output fluctuations in HPGSs due to their unique characteristics. To this aim, this paper proposes two robust controllers for SMES systems to smooth out the power provided by a HPGS. In order to evaluate the proposed SMES system efficiency's during significant and fast fluctuations (at level of up to ∼1 MW in the range and seconds in the duration), computer simulations were implemented using the MATLAB/Simulink software package. The results obtained from the simulations have confirmed that the controllers of the proposed SMES system could efficiently reduce power output fluctuations from HPGSs, minimize power losses on the transmission lines, and regulate the voltage at the point of common coupling (PCC) at an acceptable standard level. [ABSTRACT FROM AUTHOR]

The curtailment of renewable energy worsens with increasing penetration in power systems, so it is necessary to explore the upper limit value of the penetration of renewable energy (PRE). This paper uses artificial neural networks (ANN) to study the historical data of California independent system operator (CAISO), analyze the fluctuation balance strategy of wind and solar power, and predict the upper limit value of the PRE, which will peak at 40.5% in 2025. In addition, this paper also simulates the inclusion of energy storage unit (ESU) with an installed capacity of 3 GWh in the grid to reduce curtailments, and analyzes the grid operating conditions and the upper limit value of the PRE, showing that the storage units with an installed capacity of only 3% of the average daily power output of solar energy recover 58% of the annual curtailments (2021), and the maximum PRE is 41.2%. [ABSTRACT FROM AUTHOR]

The alarming rate at which the deposit of non-renewable or fossil energy resources are depleting around the world has been nudging us in the direction of green energy for quite a long time. Considering the current and upcoming global energy crisis, we have no choice but to pay our serious attention to renewable resources to meet the energy demand of an ever-growing global population and industry. Rice husk has about 20% mass percentage of rice, with low energy and bulk density. Due to these properties, it is not viable to directly combust rice husk as a fuel. Different thermochemical pretreatment technologies can be effectively implemented to improve the fuel characteristics. However, there have been no comprehensive review article on different pretreatments of rice husk. This paper reviews most common rice husk pretreatments technologies namely, gasification, torrefaction, pyrolysis, and hydrothermal carbonization including multiple sub-categories of each technology. All of these processes are successful in improving the fuel characteristics of rice husk such as higher heating value or calorific value, moisture content, fixed carbon, etc. Each of them serves the purpose of converting a low energy-density biomass into an energy-rich material like coal differently. Location, resources, available technology, etc. should also be considered while deciding which pretreatment would be optimum for a given particular case. A comparison has been presented on how much energy can be extracted from rice husk by implementing different technologies given they can be upscaled to an industrial scale. For 527,534 kg of rice husk produced from a sample location in Bangladesh per annum, wet torrefaction before pyrolysis of rice husk yields 85 MW of power which is the highest among the 4 pretreatments discussed in this paper. • Review of different thermochemical processes conducted on rice husk. • Study on gasification, pyrolysis, torrefaction, hydrothermal carbonization process. • Comparison in terms of moisture content, fixed carbon, and heating value gain. • A case study of power generation potential of rice husk produced in Bangladesh. [ABSTRACT FROM AUTHOR]

As the built environment is among the main contributing sectors to climate change, it is needed to investigate new paradigms to push decarbonization efforts towards the ambitious objectives defined internationally. It is a shared understanding that shifting the perspective from the single building to the district perspective is required to fully take into consideration the complexity of all interactions undergoing within the built environment, thus the concept of Positive Energy District emerged as a district with annual net zero energy import and net zero CO 2 emission working towards an annual local surplus production of renewable energy. In this framework, this paper explores the investigation of the potential for achieving the level of Positive Energy District in a group of non-residential buildings in Balaguer, Catalonia, Spain. These buildings, occupying 8,825 m2 in the city centre, require significant refurbishment for improved energy performance. The analysis includes building energy modelling and simulation, renovation studies, several alternative balance calculations, and carbon emissions assessment. The paper also considers mobility and embodied energy and their impact on energy/carbon balances. The results show that Positive Energy Districts carbon and primary energy balances are not met with rooftop PV installations when retrofitting an existing district towards the Positive energy target but further significant PV areas (roughly + 50%) are required to meet merely the use stage balances: negative results are traced when mobility and embodied energy are computed. A formulation alternative to the simple mathematical balance to facilitate the diffusion of Positive Energy District as catalyst of urban decarbonisation could be needed, including context factors and alternative systems (e.g., rating systems). [ABSTRACT FROM AUTHOR]

Increasing use of volatile renewable energy sources causes challenges in balancing supply and demand. Therefore, demand-side flexibility has rising importance for system operators and balancing authorities. Flexibility management methods are needed to integrate loads like ventilation systems of different buildings (e.g., residential and commercial) into flexibility service. However, the available methods described in research papers require further development for implementation in practice. Heating and cooling systems have received much attention from researchers, but the potential of ventilation systems has been left out of focus. Therefore, this paper provides a complete set of novel flexibility management methods for ventilation systems created from an aggregator's viewpoint. The flexibility is quantified through capacity (e.i. the amount of power consumption that can be altered), forced ventilation rate duration, and the tendered price for the service. The proposed methods were tested on a building model constructed and simulated in IDA ICE. The data processing and flexibility management methods were applied in MATLAB. Two types of ventilation systems with different sensor configurations were considered: constant and variable air volume. Forced ventilation rate duration is calculated using energy and mass balance analysis where the root means squared error was 10 to 33 min, depending on the system type, measured parameter, and sensor location. The flexibility service pricing model was tested on the 2022 years' manual frequency restoration reserve (mFRR) activation and balance energy market data. [ABSTRACT FROM AUTHOR]

Water scarcity is a major global challenge, with 2.3 billion people living in water-stressed countries and the potential for 700 million people to be displaced by 2030 due to widespread water scarcity. The increasing effects of climate change and overpopulation are exacerbating the problem, particularly in arid and remote regions. One potential solution is the harvesting of atmospheric water, which is estimated to be a vast source of freshwater at 12,900 km3. This study aims to provide a comprehensive and up-to-date review of the latest research in the field of atmospheric water harvesting systems. The purpose of this review is to present the various types of harvested water, the use of solar energy, the methods of gathering it, and the mathematical models used in the literature. The paper has covered numerous types of atmospheric water harvesting techniques, including harvesting dew and fog. In addition, the paper has also discussed water harvesting technologies that rely on sorbent materials. The primary focus of the paper is to present recent advances in water harvesting systems, such as dehumidifying, condensing, vapor compression refrigeration cycles (VCRCs), thermoelectric coolers (TECs), air conditioning units, fuel cells, and integrated systems. The paper aims to provide a comprehensive understanding of the latest developments in atmospheric water harvesting systems. The paper also presents an assessment of the efficiency and effectiveness of these water harvesting systems. The paper concludes by comparing various approaches to provide accurate descriptive information regarding the amount of water harvested. Additionally, the article proposes suggestions to enhance current water harvesting systems and outlines potential future initiatives. • Water harvesting from fog and dew is a promising source of water. • Sorbent materials have the capacity to absorb moisture and generate water from the atmosphere. • TECs are suitable for collecting atmospheric water across many global regions. • Top water-collecting materials absorb moisture, convert it to water in few in minimal time. • VCRC-based devices offer substantial atmospheric water harvesting potential, analysis shows. [ABSTRACT FROM AUTHOR]

As a large number of renewable energy resources are connected to power systems, the operation, planning, and optimization of power systems have been becoming more and more complex. Power flow calculation, unit commitment, economic dispatch, energy pricing, and power system planning are essentially computation problems. A lot of computing resources are required for these problems, which are non-trivial, especially for large-scale power systems with the high penetration of renewable energy. Traditionally, the calculation and optimization of power systems are completed by classical computers based on the classical computing theory and the von Neumann architecture. However, with Moore's law getting closer and closer to the limit, the importance of quantum computing has become increasingly prominent. Quantum computing has been applied to some fields to a certain extent, yet the applications of quantum computing in power systems are rare. As the power industry is the foundation of the national economy, introducing quantum computing into the power system has far-reaching and crucial significance, such as improving the penetration of renewable energy, enhancing the computing efficiency, and helping in achieving the goal of net zero and climate neutrality by 2050. This paper first introduces the core concepts, essential ideas and theories of quantum computing, and then reviews the existing literature on the applications of quantum computing in power systems, and puts forward our critical thinking about the applications of quantum computing in power systems. In brief, this paper is dedicated to a tutorial on quantum computing targeting power system professionals and a review of its applications in power systems. The main contributions of this paper are: (1) introduce quantum computing into the field of power engineering in a thoroughly detailed way and delineate the analysis methodologies of quantum circuits systematically without losing mathematical rigor; (2) based on Dirac's notation, the related formulae are derived meticulously with sophisticated schematic diagrams; (3) elaborate and derive some critical quantum algorithms in depth, which play an important role in the applications of quantum computing in power systems; (4) critically summarize and comment on the existing literature on the applications of quantum computing in power systems; (5) the future applications and challenges of quantum computing in power systems are prospected and remarked. • Introduce quantum computing into the field of power engineering in a thoroughly detailed way and delineate the analysis methodologies of quantum circuits systematically without losing mathematical rigor. • Based on Dirac's notation, the related formulae are derived meticulously with sophisticated schematic diagrams. • Elaborate and derive some important quantum algorithms in depth, which play an important role in the applications of quantum computing in power systems. • Critically summarize and comment on the existing literature on the applications of quantum computing in power systems. • The future applications and challenges of quantum computing in power systems are prospected and remarked. [ABSTRACT FROM AUTHOR]

• A strategy for distributed Peer-to-Peer (P2P) energy trading was proposed. • A quantification strategy for flexible resources was proposed. • A pricing strategy with privacy and intelligence was proposed. • The impact of renewable energy uncertainty on bidding decisions was considered. • The strategy protects privacy, improves benefits, and enhances local energy balance. With the widespread application of distributed power generation, traditional energy consumers are becoming active prosumers. Giving full play to the role of the electricity market in resource allocation, peer-to-peer (P2P) energy trading between multiple prosumers and consumers helps increase the revenue of electricity sales and reduce the cost of electricity consumption. The participation of flexible resources in P2P energy trading achieves local power complementarity, improves benefits, and reduces the operating pressure of lines. To ensure the benefits of prosumers and stimulate their enthusiasm, it is necessary to propose an effective trading strategy to fully allow flexible resources to participate in P2P energy trading. Therefore, this paper establishes a P2P energy market based on the continuous double auction (CDA) mechanism. Based on the model predictive control (MPC) theory, we propose a minimum cost rolling optimization model to optimize the bidding quantity of flexible resources. Moreover, the Automatic Learning (AL) pricing strategy with privacy and learning is proposed. The AL bidding price strategy enables interactive learning among prosumers. In addition, the influence of bidding price on bidding quantity during the trading process is also considered. Furthermore, this paper considers the impact of renewable energy source (RES) uncertainty on the bidding decisions of prosumers. Finally, the effectiveness of the proposed strategy is verified by the simulation results of cases. The simulation results demonstrate that the proposed strategy can improve the benefits for prosumers, enhance the local balance ability, achieve price learning, and increase the transaction rate. [ABSTRACT FROM AUTHOR]

• An intraday rolling dispatch framework for an integrated electricity-gas system (IEGS) is designed. • An accurate mixed-integer linear power to gas (P2G) operating model is proposed. • The dynamic model of natural gas flow based on partial differential equations (PDEs) is established. • Two-level constraints are applied to constrain the inevitable deviation of gas flow in the intraday and day-ahead dispatch. • The IEGS rolling dispatch promotes renewable energy accommodation and enhance system flexibility. Rolling dispatch strategy is widely used in power systems to handle the uncertainty and volatility of renewable energy. Meanwhile, flexible gas-fired units and emerging power-to-gas (P2G) facilities integrate the power system and natural gas system as an integrated electricity-gas system (IEGS). Hence, rolling dispatch should be applied to the whole IEGS. This paper designs the rolling dispatch framework in the IEGS and proposes the partial-differential-equations-involved (PDEs) mathematical models that accurately formulate the natural gas network dynamics and the operation properties of P2G facilities to retain the facticity of the IEGS in the rolling dispatch process (e.g., the linepack characteristics of natural gas networks and the physical limits of the P2G facilities). The impact of uncertain wind power on the IEGS operation is also considered. In order to reduce the computational burdens, this paper employs a combination of implicit difference method and the technique of linearization to transform the original rolling dispatch model with PDEs and chance constraints into mixed-integer linear programming which is solvable by off-the-shelf solvers. The results of the case studies demonstrate that the proposed rolling dispatch strategy promotes renewable energy accommodation, enhances the flexibility, security, and efficiency of the IEGS, and mitigates the impact of renewable energy's uncertainty and volatility on the IEGS. [ABSTRACT FROM AUTHOR]

• Optimal dynamic economic dispatch of power system considering multivariate uncertainties is investigated. • A novel graph representation method is proposed to expressed the operation state of power system. • A novel scalable GRL is investigated to develop the optimal policy of DED. • The method facilitates economy of power system even when the topology and structure of the power system change. • A comprehensive comparison of the proposed method and other popular policies. Due to the increasing penetration of renewable energy source, power system is facing significant uncertainties. How to fully consider those uncertainties in dynamic economic dispatch (DED) has become a crucial problem to the safe and economic operation of power system. Reinforcement learning (RL) based approaches can provide the dispatch policy in response to uncertainty. However, current RL uses traditional Euclidean data representation, which greatly reduces the scalability and computational efficiency of economic dispatch algorithms. To address such obstacle, this paper develops a novel graph reinforcement learning (GRL) method for DED of power system. Firstly, DED is formulated as a Markov decision process and formulated as a dynamic sequential decision problem. Secondly, a novel graph-based representation of system state is proposed. Using graph data to represent dispatch operation data with non-Euclidean characteristics can effectively capture the implicit correlation of the uncertainty corresponding to the system topology. Thirdly, a GRL algorithm is proposed to learn the optimal policy which maps graph represented system state to DED decision. Compared with the traditional deep reinforcement learning (DRL), the GRL proposed in this paper has certain generalization ability and scalability, and can achieve higher quality solutions in online operation. Case studies illustrate that the optimality of the proposed method is 98.04%, which is 15.13% higher than that of the existing learning methods. The algorithm is scalable and improves the sample efficiency. [ABSTRACT FROM AUTHOR]

Microgrids are useful solutions for integrating renewable energy resources and providing seamless green electricity to minimize carbon footprint. In recent years, extreme weather events happened often worldwide and caused significant economic and societal losses. Such events bring uncertainties to the microgrid energy scheduling problems and increase the challenges of microgrid operation. Traditional optimization approaches suffer from the inaccuracy of the uncertain microgrid model and the unseen events. Existing reinforcement learning (RL) - based approaches are also hampered by the limited generalization and the increasing computational burden when stochastic formulations are required to accommodate the uncertainties. This paper proposes a new parallelized reinforcement learning (PRL) method based on the probabilistic events to handle the microgrid energy uncertainties. Specifically, several local learning agents are employed to interact with pertinent microgrid environments in a distributed manner and report outcomes to the global agent, which will optimize microgrid energy resources online during extreme events. The stochastic microgrid energy optimization problem is reformulated to include all possible scenarios with probabilities. The advantage estimate functions of learning agents are designed with a backward sweep to transfer the outcomes to the value function updating process. Two simulation studies, stochastic optimization and online testing, are performed to compare with several existing RL approaches. Results substantiate that the proposed PRL method can achieve up to 20% optimization performance improvement with 4 and 28 times less computation cost than Q-learning with experience replay and multi-agent Q-learning approaches, respectively. • The paper proposes a new parallelized RL method based on probabilistic events. • The knowledge aggregation assembles the state and action from the local agents. • The global state/action vectors are built for online decision-making process. • The advantage estimate functions of learning agents are designed. [ABSTRACT FROM AUTHOR]

• A two-stage market clearing problem is formulated for local energy trading e-LAN. • A decentralized approach is proposed to clear energy market in a decentralized way. • A Nash bargaining based approach is proposed to resolve path conflicts efficiently. Local energy trading is envisioned as a promising energy management approach for distributed renewable energy sources. To efficiently facilitate the novel local energy trading, the concept of energy local area network (e-LAN) has been proposed as an innovative framework for future community power grid, which aims to improve the efficiency of end-to-end power delivery by the novel technique of energy routing. Under this framework, a proper market clearing approach is required for e-LAN to manage decentralized local energy market and coordinate end-to-end energy routing paths. In this paper, a decentralized market clearing approach with cooperative energy routing is proposed for local energy market in e-LAN. Due to the nonconvexity of the original market clearing problem, the proposed approach formulates a new problem for market clearing in e-LAN, which is approximately equivalent to the original problem. The new problem consists of two subproblems, a market clearing problem and a cooperative energy routing problem. For the market clearing problem, this paper proposes a dual decomposition based approach to clear local energy market in a decentralized way so as to avoid privacy violation. The cooperative energy routing problem is solved by proposing a Nash bargaining based approach, where each trading pair can obtain more benefits from cooperation than from noncooperation. Simulation results are presented to verify the effectiveness of the proposed approach in avoiding physical constraint violation, resolving path conflicts and improving social welfare. [ABSTRACT FROM AUTHOR]

The increasing penetration of intermittent renewable energy sources such as solar and wind is creating new challenges for the stability and reliability of power systems. Electrochemical battery energy storage systems offer a promising solution to these challenges, as they permit to store excess renewable energy and release it when needed. This paper reviews the integration of battery energy storage systems for increasing the penetration of variable sources into power grids. It highlights the impacts of high penetration of intermittent sources on the power system. The paper provides an overview of battery energy storage systems and their characteristics, applications, and technologies suitable for grid-scale use. The article highlighted that lithium and flow batteries are the most widely used technologies. Battery integration to the power grid has the potential to help achieve a penetration rate of 40–50% of variable renewable energies, as this rate may vary depending on the specific characteristics of each electrical system. The paper also proposes an approach to incentivize the use of BESS to increase the share of variable renewable energies in weakly interconnected power grids such as those in Sub-Saharan Africa. • Li-ion, Na–S and VRFB are the most promising technologies for grid applications. • Penetration rates between 40 and 50% of VRE could be achieved with grid-tied batteries. • For power quality applications, ZnBr, VRFB and Li-ion batteries are well-suited. • For power regulation applications, Na–S and Li-ion batteries are well-suited. [ABSTRACT FROM AUTHOR]

• The optimization problem of EPS and flexibility-enhanced DHSis formulated. • The DHS is retrofitted with BCT and uses a variable mass flow operation strategy. • A conditional-DRO is employed to hedge against the uncertainty of renewable energy. • Propose adouble-layer distributed algorithm to achieve the distributed solution. In an integrated electricity and heating system (IEHS), building flexibility-enhanced district heating systems (DHSs) that use advanced flexibility-enhanced technologies are effective for promoting renewable energy accommodation. However, it is challenging to comprehensively utilize these flexibility-enhanced technologies in IEHS dispatch. This paper proposes a distributed conditional-distributionally robust coordination method for an electrical power and flexibility-enhanced DHS. The DHS is retrofitted with bypass compensation technology and uses a variable mass flow operation strategy to enhance its flexibility and promote renewable energy accommodation. Moreover, conditional-distributionally robust optimization is used to consider the inherent dependency between the forecasting error of a wind farm and the forecast output information, which goes beyond the state-of-the-art models. Additionally, a novel reformulation method is adopted to transform the problem with random variables and bilinear constraints into a more tractable programming problem, yielding a conservative approximative model of the original model that guarantees feasibility. Finally, to protect the privacy of electrical power system and DHS operators, this paper proposes a double-layer distributed algorithm to solve the problem in a distributed fashion while guaranteeing feasibility. Case studies demonstrate that the proposed model with flexibility-enhanced DHSs significantly promotes renewable energy accommodation and reduces dispatch costs, and the proposed double-layer distributed algorithm effectively solves the dispatch problem in a distributed fashion and guarantees the feasibility of dispatch results. [ABSTRACT FROM AUTHOR]

Carbon neutrality is one of the main goals in current power system planning and operation. Many different actions and solutions are presented and implemented so far, incorporating renewable energy sources at all levels of the system. With the raising prices of natural gas and its negative impact on the environment, particularly emphasised over the past two years, new technologies and energy vectors are emerging as potential for its replacement. High importance of the hydrogen energy vector, especially in the decarbonisation of the industry sector, is being put forward due to its advantages of zero greenhouse gas emissions, its capacity for storing energy and capability to balance the production of renewable energy sources. This paper brings a detailed mathematical model of a price driven, demand responsive, multi-energy industry facility, as a logic first implementer of hydrogen technologies due to its high and multi-energy consumption nature. The systematic analyses are conducted over a set of scenarios of local production, considering different hydrogen technologies as well as range of natural gas and electricity prices. The findings of the paper conclude that hydrogen technology implementation into a realistic industrial consumers processes results in zero local emissions production, high level of autonomy and resistance from the market disturbances. When compared with classic industrial layouts, the overall CO 2 footprint is reduced from around 30% to around 85%, depending on the scenario. The sensitivity analysis has proven that hydrogen layouts are comparable to natural gas layouts in terms of total costs, showing that hydrogen options result in lower cost in the range from 25% to 58%, depending on the observed scenario. • Integration of renewables and hydrogen technologies in industrial plant. • Price sensitivity analysis showcasing economic benefits of different hydrogen technologies. • Carbon footprint reduction up to 80% from the business-as-usual approach. • Complete reduction in local GHG emissions using hydrogen technologies. [ABSTRACT FROM AUTHOR]

The microgrid market is growing significantly due to several drivers, such as the need to lower greenhouse gas emissions by integrating higher shares of distributed renewable energy sources, falling costs of microgrid components, the need for more reliable power supply infrastructures, and new off-grid solutions to foster electricity access in developing economies. Coordinated management of the microgrid components is crucial for their effectiveness, and this can be very challenging when hosting solar or wind generation. This paper studies the energy management problem of a microgrid based on reinforcement learning algorithms. The advantage of using these algorithms against other optimization and machine learning techniques is that they do not need past experiences to learn a strategy. The learning is based on trial and error experiences, which facilitates its easy implementation to other microgrids while demonstrating their facility to be applied in real cases. In particular, this paper proposes an implementation for an Energy Management System (EMS) in microgrids using the Twin-Delayed Deep Deterministic Policy Gradient (TD3) algorithm. Moreover, it compares the proposed algorithm with the Deep Q-Network (DQN). This comparison evaluates the improvement over exploiting the continuous nature of the decision variables against a discretization of the same since the DQN cannot make actions over a continuous space. [ABSTRACT FROM AUTHOR]

The low inertia of electrical power networks as renewable energy penetration levels increase is regarded as a significant issue confronting worldwide ambitions for the anticipated renewable energy transition. Load frequency control (LFC) is an efficient way of improving the frequency stability and dependability of renewable energy-based power networks. Therefore, this paper introduces a novel three-degree-of-freedom (3 DoF) load frequency control method for two-area-linked power systems. To implement the suggested LFC approach, the proposed control employs an enhanced hybrid fractional order control method (namely FOTPID), and the tilt integrator differentiator with filter (TIDF) is employed in the frequency deviation and tie-line power loop feedforward compensation. As a consequence, this study proposes a novel 3DoF cascaded FOTPID-TIDF controller. Furthermore, this work proposes a modified method of the recently developed Manta-Ray Foraging Optimizer (MRFO) to optimize the parameters of the new suggested 3DoF cascaded FOTPID-TIDF controller. The paper includes extensive performance comparisons and discussions to validate the superior performance metrics of the new proposed control and optimization methods. The results reveal that by taking into account the characteristics and uncertainties of renewable sources, interconnected loads, and grid inertia, the results perfectly mitigate various existing deviations in frequency and tie-line power compared to existing contemporary LFC approaches. [ABSTRACT FROM AUTHOR]

As the demand for renewable energy continues to rise, the significance of electrocatalysis research for hydrogen production has also increased. To explore the developmental trends in this field, a scientometric-assisted review was conducted using clustering analysis of keywords from 43,564 papers published between 2013 and 2022. The results of the study demonstrate a globally diverse research landscape, with contributions from Asia, Europe, North America, and the Middle East. China emerges as the most prolific contributor, with consistent growth in research productivity over the past decade. The USA, South Korea, India, and Germany were among the top five leading countries in electrocatalysis research. Singapore, USA, Australia, Saudi Arabia, and Germany lead research impact, measured by total citations per total paper. China has the highest collaboration in electrocatalysis research, with collaborations with the USA, Australia, Singapore, Saudi Arabia, Japan, the UK, Germany, Canada, Korea, and India. Importantly, the study reveals six clusters of electrocatalysis research, with different author keywords, each covering a range of topics and materials. The implications of each of the six clusters in electrocatalysis are examined critically. This study offers valuable insights for researchers, policymakers, and funding organizations in making critical decisions by identifying the main themes and trends in hydrogen evolution through electrocatalysis research. • To explore the developmental trends in this field, a scientometric-assisted review was conducted on renewable energy • The results of the study demonstrate a globally diverse research landscape. • This study offers valuable insights in hydrogen evolution through electrocatalysis research. [ABSTRACT FROM AUTHOR]

Unlike traditional offshore oil and gas projects which generally involve a limited number of structures and are confined to a relatively small seabed footprint, renewable energy projects such as offshore wind farms can comprise many tens or even hundreds of structures dispersed across an extensive area. Driven by the need for offshore wind to reduce costs while also minimising project risk, there is currently a strong focus within the offshore industry on identifying new ways to extract the full benefit from all available site investigation data and carry this benefit through to engineering design. This paper provides a demonstration of two such approaches that utilise geo-data from offshore Western Australia, potentially representative of conditions of future offshore wind farms; the first is an integrated approach combining geophysical and geotechnical data through a seismic inversion process, while the second encompasses statistical analysis of geotechnical cone penetrometer and soil strength test data combined with a Bayesian compressive sampling-based spatial interpolation method. The demonstrations yielded useful findings about the methodologies and associated input requirements. It is envisaged this work will lead to the development of an efficient integrated framework for interpreting geo-data that will inform future offshore site investigation and geotechnical design practice. • There is currently a focus within the offshore industry on identifying new ways to extract the full benefit from site investigation data. This paper provides a demonstration of two such approaches that utilise geo-data from offshore Western Australia. The first approach combines geophysical and geotechnical data through a seismic inversion process. The second approach encompasses statistical analysis of geotechnical cone penetrometer and soil strength test data. It is envisaged the study findings will feed into future site investigation practice and development of integrated interpretation approaches. These approaches will lead to a framework for integrating geophysical and geotechnical data and implementing the output into foundation design. [ABSTRACT FROM AUTHOR]

Wind energy is a promising and efficient renewable energy source for electrical energy production. Globally, there has been a significant increase in the development of wind energy conversion systems (WECS). The world is interested in grid codes applying for facing various problems because of large capacity WECS integration with the grid, including the stability and reliability of electrical grid and power quality. Low voltage ride through (LVRT) capability is an important requirement of grid codes. LVRT means that the wind turbine is still connected to the grid during grid voltage sags. This is essential for ensuring that no generated power by wind turbines is lost due to grid disturbances. To deal with these issues simultaneously, this paper presents a comprehensive review of LVRT methods for the most common wind generators; squirrel cage induction generator (SCIG), doubly fed induction generators (DFIG), and permanent magnetic synchronous generator (PMSG). Moreover, the overall view of optimization techniques that are used to enhance LVRT capability for WECSs and the configurations of WECS are investigated. A comprehensive and a useful summary for the recent work in literature regarding LVRT methods for these three wind generators with comparisons and discussion are introduced. To achieve this aim, more than 200 articles are organised and only 135 papers are presented in this review. • Comprehensive review of low voltage ride through methods for the most common wind generators. • Overall view on optimization techniques that are used to enhance LVRT capability. • The configurations of wind energy conversion system are investigated. • Useful summery for the recent work in literature regarding LVRT methods is introduced. [ABSTRACT FROM AUTHOR]

This paper presents a review and analysis of pricing strategies in peer-to-peer (P2P) energy trading to provide new insights into the design of pricing mechanisms and the establishment of sustainable and effective P2P energy markets in distribution networks. P2P energy trading is emerging as an effective management scheme for prosumers with renewable energy sources in distribution networks. Several studies have focused on designing pricing mechanisms for P2P energy trading with a focus on energy pricing but not on network service pricing. To understand the extant pricing mechanisms, 169 papers were systematically reviewed. These papers, published between 2015 and 2022, were retrieved by academic search engines. The review showed that energy pricing can be categorized into synchronous and asynchronous, while network service pricing can be classified into ex-ante and ex-post schemes. Synchronous energy pricing provides economic efficiency. Asynchronous energy pricing offers Pareto optimality and price transparency and accommodates market participant preferences. Market participation can be reduced by conflicts of interest in network service pricing, between system operators and market participants owing to the settlement of network service costs, and among participants due to pricing methods. Energy and network service pricing compatibility between different methodologies relies on market results to distinguish matched counterparties. [Display omitted] • Systematic classification of energy and network service pricing suggested. • Procedure and function of energy-pricing methods clarified. • Potential conflicts of interest in network service pricing discussed. • Compatibility between energy and network service pricing presented. • Key challenges and opportunities for future research identified. [ABSTRACT FROM AUTHOR]

Renewable energies are a key to reducing carbon emissions and enabling access to affordable energy for rural populations in many low- and middle-income countries. For this reason, many governments have started to introduce auctions for off-grid renewable energy. Despite their relevance for clean, accessible energy, the factors for successful or failed off-grid renewable energy tenders have not been broadly studied yet. This paper investigates the success factors for the successful implementation of off-grid energy auctions to enhance rural energy access in low- and middle-income countries. For the scope of this research, the off-grid energy tenders are considered successful if they lead to cost-efficient outcomes, have a high implementation rate and improve the equality of access to energy. Using success measures and success factors from the state of knowledge on energy auctions, three real-world cases from Peru, Uganda and Senegal are analysed and compared. The investigations show that auctioneers should strike a balance between 1) economies of scale and streamlining administrative procedures, 2) allowing adequate product designs for a diverse group of beneficiaries and 3) attracting enough bidders in this relatively young sector. Moreover, this paper confirms that balancing the developers' risks and public risks is essential in designing off-grid energy tenders and that there are several ways to do this. Furthermore, the advantages and limitations of different support payment types are illustrated. Finally, regarding governance, the real-world cases in this research underscore the importance of clarity about responsibilities and procedures, without which significant delays can be incurred. • Off-grid energy auction organizers must balance developers' risks with public risks. • Fixed subsidies provide predictability to policymakers regarding public expenses. • Results-based financing encourages further expansion of projects. • Lack of clarity about responsibilities and procedures can lead to significant delays. [ABSTRACT FROM AUTHOR]

• International Atomic Energy Agency (IAEA) guidelines for newcomers. • Overview of Saudi Arabia's nuclear energy ambition. • Regulatory framework for Saudi Arabia's nuclear energy program. This research article explores the Kingdom of Saudi Arabia's (KSA) efforts to meet its increasing electricity demand and reduce its reliance on fossil fuels by integrating nuclear energy into its energy mix. KSA aims to achieve a clean energy mix of 50% by 2030, including nuclear energy. The paper discusses KSA's progress in developing its nuclear power infrastructure, including the establishment of the King Abdullah City for Atomic and Renewable Energy (KA-CARE), and Nuclear and Radiological Regulatory Commission (NRRC). KSA has followed the IAEA "Milestones Approach" and conducted the first Integrated Nuclear Infrastructure Review (INIR) to ensure compliance with international standards and regulations. The article highlights the Kingdom's commitment to fulfilling international conventions and treaties related to nuclear power generation and its determination to achieve its VISION 2030 nuclear power generation targets. The paper provides an overview of the progress made by KSA towards introducing nuclear energy and underscores the measures taken to ensure the safety and security of the nuclear program. [ABSTRACT FROM AUTHOR]

Hydrogen (H 2) as a clean and environmentally-friendly carrier can be introduced as the fuel of future to mitigate the air pollution caused by industrial anthropogenic activities. With the aim of fulfilling the need of a green H 2 green production, disparate technological processes have been developed in current decades. Purposeful review of these H 2 production processes can allow the expert readers as well as non-expert readers to properly perceive the limitations and future perspectives for future research. H 2 may be manufactured from renewable or non-renewable energy sources. Despite significant advancements in the techniques of H 2 production, the emergence of various operational/technical challenges in obtaining a steady state and stable H 2 economy via increasing the process yield and decreasing production costs has motivated the researchers the study more to develop more efficient processes. Therefore, development of a scientific techno-economic analysis for all existed H 2 production techniques is of great importance to highlight the future perspective of this important energy source. The main objective of this paper is to comprehensively review the advantages and disadvantages of various H 2 production techniques. Moreover, the economic dimensions of each technique along with the role of nanotechnology in the production of H 2 are aimed to be reviewed in this paper. • Review of limitations and future perspectives for future research of H 2 production. • Operational/technical challenges in obtaining a steady state and stable H 2. • Techno-economic analysis for all existed H 2 production techniques. • The role of nanotechnology in the production of H 2. [ABSTRACT FROM AUTHOR]

Agricultural greenhouses can provide a suitable microclimate for crops to thrive under extreme weather conditions. The operations of these greenhouses are expensive due to the energy requirement of the active thermal conditioning systems required to maintain the growing environment for crop production. Engineering of these greenhouses to utilize clean renewable energy sources is critical and necessary to mitigate their carbon footprint, paving the way to a more sustainable agricultural industry. This paper presents numerical modelling of a net-zero passive solar greenhouse in Alberta, Canada with winter temperatures below freezing. The indoor microclimate of the greenhouse is modelled using the detailed radiation model of a transient simulation tool, TRNSYS. The paper investigates the effects of ground parameters and the solar-to-air fraction on the numerical results. The paper includes a cost comparison between crop production in the traditional and passive style greenhouse. [Display omitted] • The temperature profile of a net zero passive solar greenhouse in Alberta is simulated. • The effects of the solar-to air factor and ground parameters are studied. • The daily spike in the indoor temperature profile is due to the solar-to-air factor. • The indoor temperature profile is not sensitive to the changes in ground temperature. • Tomatoes production in the greenhouse is economically feasible. [ABSTRACT FROM AUTHOR]

The utilization of renewable energy has attracted much attention with the deterioration of the environment. Solar energy is widely distributed and has huge reserves. But, solar energy is also volatile and intermittent. This brings difficulties to the large-scale grid connection of solar energy and the distribution of power resources. The key to solve this problem is to provide accurate and stable short-term prediction for solar power generation. For this purpose, we present an attention-based Bayesian sequence to sequence (Seq2Seq) model within decomposition-ensemble strategy. Firstly, an influential factors analysis is performed on the data about meteorological conditions, operation status of the photovoltaic panels and historical solar power generation sequences to obtain the optimal combination of the influential factors. Secondly, this paper proposes a novel decomposition-ensemble framework based on complete ensemble empirical mode decomposition with adaptive noise and independent component analysis to mine the intrinsic modes of the solar power generation time series. Thirdly, this paper presents an attention-based Bayesian Seq2Seq method for modeling the relationships between solar power generation and influential factors. Using a real-world dataset from State Administration of Electricity Investment Science and Technology of China, a case study, comparative analysis and robustness checks are conducted, through which the contributions of the methods in the proposed model and the superior performance of the model are demonstrated. • Short-term solar power generation prediction model established. • An influential factors analysis is designed to obtain the optimal combination of the influential factors. • A novel decomposition-ensemble framework is proposed to mine the intrinsic modes of the solar power generation sequence. • An attention-based Bayesian Seq2Seq method is presented. [ABSTRACT FROM AUTHOR]

• The investigation of the modular smart transformer structure based on a five-level cascaded H-bridge rectifier at its medium voltage stage, six dual active bridge converters at its high-frequency stage, and a three-phase four-leg inverter at its low voltage stage, as well as six MVDC and LVDC buses. • The vector-oriented control, sliding mode control, and adaptive backstepping control have been developed and implemented for the studied smart transformer model. • A voltage balance control loop is introduced to these control strategies in order to prevent the voltage unbalance problems of cascaded H-bridge rectifier cells and MVDC bus voltages. • The performance and robustness evaluation of these controllers under various operating conditions, namely source voltage variations, parametric variations, and fault occurrence. This paper proposes a Smart transformer (ST) model comprising a five-level Cascaded H-Bridge (CHB) rectifier at its medium voltage stage, dual active bridge converters at its high-frequency stage, and a four-leg inverter at its low voltage stage. Indeed, the CHB rectifier enables the improvement of ST modularity and the integration of many Renewable Energy Sources (RESs). Nevertheless, the high integration of RESs can lead to voltage fluctuations due to their intermittent energy production which can affect the ST behavior. Therefore, a robust controller is required to improve the ST's dynamic performance. In this context, vector-oriented control, Sliding Mode Control (SMC), and adaptive backstepping control are studied in this paper. To select the most appropriate controllers, a series of simulations under anomalous conditions, including voltage variations, parametric variations, and fault occurrence, are performed using Matlab software. The obtained simulation results demonstrate that SMC provides the best performance and the greatest robustness. [Display omitted] [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]