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Summary: Wireless sensor networks (WSN) encompasses millions of nodes that work jointly, senses environmental information, and transmits to the Base Station (BS). "Energy hole" or "hot‐spot" is a problem in which sensor nodes near to the BS deceasetoo early which creates the partitioning of the network. This is due to the reason of the difference in the consumption of energy by the nodes in WSN which reduces the lifespan of the sensor network. The distribution of the cluster heads and the load balancing are the major pitfalls. In this paper to deal with the statedpitfalls, a hybrid of fuzzy logic technique and harmony search algorithm is proposed to lengthen the lifetime of the network. Unequal clustering is a technique proposed by the researchers to handle the hot‐spot issue, hence it is also included. The proposed algorithm forms unequal clusters. To prove the efficiency of the proposed algorithm, some well‐known unequal clustering algorithms and harmony search based algorithmare used to compare the proposed algorithm under different network settings. In all the settings, better performance is achieved by the proposed protocol. It prolongs the lifespan of the network as compared to the different algorithms proposed so far. [ABSTRACT FROM AUTHOR]

In this paper, we investigate resource allocation for IRS-assisted green multiuser multiple-input single-output (MISO) systems. To minimize the total transmit power, both the beamforming vectors at the access point (AP) and the phase shifts at multiple IRSs are jointly optimized, while taking into account the minimum required quality-of-service (QoS) of multiple users. First, two novel algorithms, namely a penalty-based alternating minimization (AltMin) algorithm and an inner approximation (IA) algorithm, are developed to tackle the non-convexity of the formulated optimization problem when perfect channel state information (CSI) is available. Existing designs employ semidefinite relaxation in AltMin-based algorithms, which, however, cannot ensure convergence. In contrast, the proposed penalty-based AltMin and IA algorithms are guaranteed to converge to a stationary point and a Karush-Kuhn-Tucker (KKT) solution of the design problem, respectively. Second, the impact of imperfect knowledge of the CSI of the channels between the AP and the users is investigated. To this end, a non-convex robust optimization problem is formulated and the penalty-based AltMin algorithm is extended to obtain a stationary solution. Simulation results reveal a key trade-off between the speed of convergence and the achievable total transmit power for the two proposed algorithms. In addition, we show that the proposed algorithms can significantly reduce the total transmit power at the AP compared to various baseline schemes and that the optimal numbers of transmit antennas and IRS reflecting elements, which maximize the system energy efficiency of the considered system, are finite. [ABSTRACT FROM AUTHOR]

Intelligent reflecting surface (IRS) is deemed as a promising solution to improve the spectral and energy efficiency of wireless communications cost-effectively. In this paper, we consider a wireless network where multiple base stations (BSs) serve their respective users with the aid of distributed IRSs in the downlink communication. Specifically, each IRS assists in the transmission from its associated BS to user via passive beamforming, while in the meantime, it also randomly scatters the signals from other co-channel BSs, thus resulting in additional signal as well as interference paths in the network. As such, a new IRS-user/BS association problem arises pertaining to optimally balance the passive beamforming gains from all IRSs among different BS-user communication links. To address this new problem, we first derive a tractable lower bound of the average signal-to-interference-plus-noise ratio (SINR) at the receiver of each user, termed average-signal-to-average-interference-plus-noise ratio (ASAINR), based on which two ASAINR balancing problems are formulated to maximize the minimum ASAINR among all users by optimizing the IRS-user associations without and with BS transmit power control, respectively. We also characterize the scaling behavior of user ASAINRs with the increasing number of IRS reflecting elements to investigate the different effects of IRS-reflected signal versus interference power. Moreover, to solve the two ASAINR balancing problems that are both non-convex optimization problems, we propose an optimal solution to the problem without BS power control and low-complexity suboptimal solutions to both problems by applying the branch-and-bound method and exploiting new properties of the IRS-user associations, respectively. Numerical results verify our performance analysis and also demonstrate significant performance gains of the proposed solutions over benchmark schemes. [ABSTRACT FROM AUTHOR]

The stationary supercapacitor energy storage system (SCESS) is one of effective approaches for the utilization of train's regenerative braking energy in urban rail systems. In this paper, the capacity configuration of SCESSs, the no-load voltage of substation, the control of onboard braking resistors and train operation diagrams are considered comprehensively. Based on the equivalent circuit model, the effects of traction power system parameters on the energy transmission between powering trains, braking trains and SCESSs are analyzed, and the relationships between the train operating parameters and system energy consumptions are revealed. A multi-variable synthetic optimization method is proposed to optimize the SCESS capacity, train operation diagrams and traction power system parameters collaboratively. In order to reduce the search space and improve the efficiency of the optimization algorithm, a hierarchical multi-objective optimization model is established, based on which the design variables and the control variables are optimized iteratively. And as the traffic density has a great impact on the regenerative braking energy distribution of the system, the optimization objective function fully considers the frequency distribution of headways throughout the day. Combined the Elitist Non-dominated Sorting Genetic Algorithm (NSGA-II) with the traction power flow simulator, the flowchart of the two-stage optimization algorithm is designed, and the pareto set of the multi-objective problem is obtained. The advantages of reducing system energy consumptions and configuration cost under the proposed algorithm are verified based on case studies of Beijing Batong Line. [ABSTRACT FROM AUTHOR]

Delta-orthogonal multiple access (D-OMA) has been recently investigated as a potential technique to enhance the spectral efficiency in the sixth-generation (6G) networks. D-OMA enables partial overlapping of the adjacent sub-channels that are assigned to different clusters of users served by non-orthogonal multiple access (NOMA), at the expense of additional interference. In this paper, we analyze the performance of D-OMA in the uplink and develop a multi-objective optimization framework to maximize the uplink energy efficiency (EE) in a multi-access point (AP) network enabled by D-OMA. Specifically, we optimize the sub-channel and transmit power allocations of the users as well as the overlapping percentage of the spectrum between the adjacent sub-channels. The formulated problem is a mixed binary non-linear programming problem. Therefore, to address the challenge we first transform the problem into a single-objective problem using Tchebycheff method. Then, we apply the monotonic optimization (MO) to explore the hidden monotonicity of the objective function and constraints, and reformulate the problem into a standard MO in canonical form. The reformulated problem can be solved by applying the outer polyblock approximation method. Our numerical results show that D-OMA outperforms the conventional non-orthogonal multiple access (NOMA) and orthogonal frequency division multiple access (OFDMA) when the adjacent sub-channel overlap and scheduling are optimized jointly. [ABSTRACT FROM AUTHOR]

Grouping the sensor nodes into clusters is an effective way to organize wireless sensor networks and to prolong the networks' lifetime. This paper presents a static clustering algorithm that employs predator–prey optimization (PPO) for identifying cluster heads as well as routes for sending data to the sink. The objective of the optimization algorithm is to reduce the energy consumed in data collection and transmission, to achieve equalization in energy utilization by the wireless sensor nodes and to prolong the wireless sensor network lifetime while avoiding the expenses of cluster reformation in each communication round. The novelty of this algorithm is to treat the identification of cluster heads and the choice of transmission paths a unified optimization problem of minimizing the total energy cost of the network, whereas existing algorithms consider them two separate optimization sub-problems. PPO algorithm is applied to select the most appropriate pair of cluster heads for each cluster. It also identifies the optimum communication path, which can be single or multiple hop. The energy consumed in data transmission is reduced and a uniformity in residual energy of the nodes is achieved. The performance of the novel algorithm has been evaluated by observing the patterns in which nodes consume their energies. The number of packets that are successfully delivered has been found to be better than the existing static clustering algorithms, and at par with the finest dynamic clustering algorithms. [ABSTRACT FROM AUTHOR]

The growing demand for cloud computing adoption presents more challenges for researchers to make cloud computing more efficient and affordable for infrastructure providers and end users. The management of cloud computing involves investing in IT infrastructure in the first phase and investing in energy, maintenance and space costs in the second phase. However, energy costs account for a large portion of cloud management costs, and saving energy consumption can significantly reduce overall cloud management costs. Server consolidation is a strategy to improve data center energy efficiency and resource utilization. Virtual machine (VM) placement is considered one of the main problems with VM consolidation. The VM placement problem aims to reduce the number of active physical machines in data center to reduce data center power consumption and maintenance costs. However, the waste of data center resources has a significant impact on the energy efficiency of the data center, so it should be considered in the VM placement strategy. This paper proposes a new method based on the Monarch Butterfly Optimization algorithm (MBO) called MBO-VM for new virtual machine placement, designed to maximize packaging efficiency and reduce the number of active physical servers. CloudSim toolkit is used to test the efficiency of the proposed MBO-VM approach under real cloud workloads as well as synthetic workloads. Simulation results show that MBO-VM produces significantly better results compared with known VM placement techniques. The proposed MBO-VM can reduce the number of active servers more effectively and maximize the packaging efficiency. [ABSTRACT FROM AUTHOR]

This paper investigates the energy efficiency (EE) optimization in downlink multi-cell massive multiple-input multiple-output (MIMO). In our research, the statistical channel state information (CSI) is exploited to reduce the signaling overhead. To maximize the minimum EE among the neighbouring cells, we design the transmit covariance matrices for each base station (BS). Specifically, optimization schemes for this max-min EE problem are developed, in the centralized and distributed ways, respectively. To obtain the transmit covariance matrices, we first find out the closed-form optimal transmit eigenmatrices for the BS in each cell, and convert the original transmit covariance matrices designing problem into a power allocation one. Then, to lower the computational complexity, we utilize an asymptotic approximation expression for the problem objective. Moreover, for the power allocation design, we adopt the minorization maximization method to address the non-convexity of the ergodic rate, and use Dinkelbach’s transform to convert the max-min fractional problem into a series of convex optimization subproblems. To tackle the transformed subproblems, we propose a centralized iterative water-filling scheme. For reducing the backhaul burden, we further develop a distributed algorithm for the power allocation problem, which requires limited inter-cell information sharing. Finally, the performance of the proposed algorithms are demonstrated by extensive numerical results. [ABSTRACT FROM AUTHOR]

Mobile edge computing (MEC) is a promising solution to support emerging delay-sensitive mobile applications, such as self-driving, augment/virtual reality, and various Internet of Things (IoT) applications. By deploying MEC servers at network edge, e.g., close to cellular base stations (BSs), the computational tasks generated by these applications can be offloaded to edge nodes (ENs) and be quickly executed there. At the same time, with the projected large number of IoT devices, the communication and computational resources allocated to each user can be quite limited, making it challenging to provide low-latency MEC services. In this paper, we investigate the problem of task partitioning and user association in an MEC system, aiming to minimize the average latency of all users. We assume that each task can be partitioned into multiple subtasks that can be executed on local devices (e.g., vehicles), MEC servers, and/or cloud servers; each user can be associated with one of the nearby ENs. The subtasks can be independent of or dependent on each other. For each case, we formulate the joint optimization of task partitioning ratios and user association as a mixed integer programming problem. Each problem is solved by decomposing it into two subproblems. The lower-level subproblem is task partitioning under a given user association, which can be solved optimally. The higher-level subproblem is user association, we propose a dual decomposition-based approach and a matching-based approach to derive near-optimal solutions. Simulation results show that compared to benchmark schemes, the proposed schemes reduce the average latency by about 50% and 40% for the cases of independent and dependent subtasks, respectively. [ABSTRACT FROM AUTHOR]

Position based routing has increasingly captured the focus of scientific researchers over the last decade. It is an issue in wireless ad hoc network since the nodes to collect the information about their positions as they have limited energy, consume extra energy. Therefore, MANET needs to develop innovative algorithms to deal with node positioning, communication and processing, to use energy efficiently. This paper presents fuzzy energy optimized position-based routing (FEPR) algorithm which selects a optimum path that minimize the distance between source node to destination node by selecting the next best forwarder node thereby reducing the unnecessary transmissions, leads to increase in network lifetime. Parameters such as degree of node, energy balance factor of a node, and forward distance to destination are supplied as the input to the fuzzy logic system. A proposed routing algorithm is presented to achieve fuzzy routing decision. Simulation results that FEPR outperforms MFR and LAR1 in terms of number of dead node, energy consumption, network lifetime and energy balance factor. [ABSTRACT FROM AUTHOR]

This paper proposes a multi-carrier energy system or energy hub in which natural gas and electricity resources are used as inputs. The output demand profile includes electrical, heating and cooling energy, which is evaluated in four seasons. The significant differences in simulations and evaluation of the considered energy hub in different seasons have led to climate variation, resulting in higher electrical energy consumption in warmer seasons than thermal energy, while it is contrariwise in the cold season. When the customers are willing to participate in the demand response programs, total energy consumption decreases, and this can only change the pattern of customer consumption in the warmer seasons. A mixed-integer linear programing (MILP) formulation for this optimization problem is proposed and solved using the CPLEX solver in general algebraic modeling system (GAMS). Simulations of the energy hub system, including renewable wind and solar sources, will confirm and verify that the model provided represents a growth in energy hub profit, reducing the cost of purchased power from electricity grid as well as decreasing cost of social welfare. [ABSTRACT FROM AUTHOR]

In Wireless Sensor Networks (WSNs), where power consumption is a huge concern, the improvement of the network's lifetime is an area of constant study and innovation. The battery units of the sensor nodes cannot be recharged or replaced. Therefore, the need for energy efficiency in WSNs is ever-present. This paper proposes a Firework inspired Clustering Algorithm (FCA) to generate well defined and load-balanced clusters with the sensor nodes and gateways. The gateway works as cluster head (CH) for each cluster. The algorithm considers each cluster as a firework where the CH is the center of the firework and each sensor node is a 'spark' emitted by the firework. The goal of the FCA is to maximize the lifetime of the sparks which in turn will maximize the lifetime of the network. Simulations of the proposed algorithm are performed and compared with a few existing algorithms. The results show that the proposed algorithm outperforms under different evaluation metrics such as average energy consumed by sensor nodes vs number of rounds, number of active sensors vs number of rounds, first gateway die and half of the gateways die. [ABSTRACT FROM AUTHOR]

Mobile edge computing (MEC) has been envisioned as a promising approach for enabling the computation-intensive yet latency-sensitive mobile Internet services in future wireless networks. In this paper, we investigate the secrecy based energy-efficient MEC via cooperative Non-orthogonal Multiple Access (NOMA) transmission. We consider that an edge-computing device (ED) offloads its computation-workload to the edge-computing server (ECS) subject to the overhearing-attack of a malicious eavesdropper. To enhance the secrecy of the ED’s offloading transmission, a group of conventional wireless devices (WDs) are scheduled to form a NOMA-transmission group with the ED for sending data to the cellular base station (BS) while providing cooperative jamming to the eavesdropper. We formulate a joint optimization of the ED’s offloaded workload, transmit-power, NOMA-transmission duration as well as the selection of the WDs, with the objective of minimizing the total energy consumption of the ED and the selected WDs, while subject to the ED’s latency-requirement and the selected WDs’ required data-volumes to deliver. Despite the nature of mixed binary and non-convex programming of the formulated problem, we exploit the vertical decomposition and propose a three-layered algorithm for solving it efficiently. To further address the fairness among different WDs, we investigate a system-wise utility maximization problem that accounts for the fairness in the WDs’ delivered data and the total energy consumption of the ED and WDs. By exploiting our previously designed layered-algorithm, we further propose a stochastic learning based algorithm for determining each WD’s optimal data-volume delivered. Numerical results are provided to validate the effectiveness of our proposed algorithms as well as the performance advantage of the secrecy based computation offloading via NOMA. [ABSTRACT FROM AUTHOR]

In this paper, we investigate the problem of wireless service provisioning through a rotary-wing UAV which can serve as an aerial base station (BS) to communicate with multiple ground terminals (GTs) in a boost demand area. Our objective is to optimize the UAV control for maximizing the UAV.s energy efficiency, in which both aerodynamic energy and communication energy are considered while ensuring the communication requirements for each GT and backhaul link between the UAV and the terrestrial BS. The mobility of the UAV and GTs lead to time-varying channel conditions that make the environment dynamic. We formulate a nonconvex optimization for controlling the UAV considering the practical angle-dependent Rician fading channels between the UAV and GTs, and between the UAV and the terrestrial BS. Traditional optimization approaches are not able to handle the dynamic environment and high complexity of the problem in real-time. We propose to use a deep reinforcement learning-based approach namely Deep Deterministic Policy Gradient (DDPG) to solve the formulated nonconvex problem of UAV control with continuous action space that takes into account the real-time of the environment including time-varying UAV-ground channel conditions, available onboard energy of the UAV, and the communication requirement of the GTs. However, the DDPG method may not achieve good performance in an unstable environment and will face a large number of hyperparameters. We extend our approach to use the Trust Region Policy Optimization (TRPO) method that can improve the performance of the UAV compared to the DDPG method in such a dynamic environment. [ABSTRACT FROM AUTHOR]

Existing pieces of literature on previous studies advocate the research focus by various researchers to reach the benchmark of energy efficiency of Wastewater Treatment Plants (WWTPs). The driving factors to improve the energy efficiency and mitigate the energy self-sufficiency of WWTP have been identified as the increase in population growth, rising energy costs, and tightening effluent discharge requirements. WWTP economy is directly related to energy consumption and thus affects smart grid economy. There has been limited research on energy self-sufficiency and optimizing the energy demand and cost of WWTP, where the significant contributing factors are in development an optimal pumping system and advanced motor technologies. Moreover, high quality WWTP's effluent depends on the concentration level of biochemical oxygen demand (BOD) and total kjedhal nitrogen (TKN). Even so, WWTPs are always subject to evident nonlinearities and uncertainties, making it difficult to define proper optimization objectives. Besides, controllers and control systems have a strong influence on the performance of the plants. Therefore, existing research gaps to achieve optimal efficiency with minimized energy consumption and costs are the design of the WWTP plant, pumping system, motors, selection of suitable controllers and control systems, and their parameter optimizations to get the optimal output from the plant, switching techniques, challenges and uncertainties associated with plant are highlighted in this paper. Thus, as a novel contribution to the literature, this study aims to review and analyze the history, current issues, and future directions of WWTP control technologies in the context of sustainable development. The rigorous study by the authors in this paper will definitely lead the academic researchers and industry partners toward the development of optimal WWTP technologies with improved efficiency and minimized energy consumption and costs. • Effective control techniques and monitoring of the wastewater treatment plant. • Optimal energy-efficient WWTPs with renewable sources and the utility grid. • Uncertainties of WWTP can be considered for improved operation. • Effective policies should be imposed by combining academics and industry. • Reduced energy consumption and cost can be achieved. [ABSTRACT FROM AUTHOR]

Cellular technologies have evolved continuously from the 1st to the 5th generation (5G) to meet the exponentially growing needs of bandwidth, throughput and latency. However, the energy consumption experienced a proportional rise generation-wise when new hardware to support additional applications were incorporated. 5G, which already consumes four times more energy than 4G, is expected to bring about a significant spike in the conventional trajectory of energy consumption. 5G is therefore triggering a major concern for energy efficiency and with an even higher technical and network complexity, 6G will pause an unprecedented challenge to energy efficiency and sustainability. This paper focuses on the energy consumption at the base station and access network levels, which amount to around 80% of energy consumption in mobile networks. Machine learning techniques can be employed in several ways to improve the energy efficiency in these components. In this paper, efficient base station deployment strategies and adaptive operational modes as well as access network technologies such as massive MIMO and millimeter waves, which employ machine learning to enhance the energy efficiency, have been reviewed in depth. Since existing research works have focused mostly on a single optimization strategy at either the base station or access network level, this paper proposes a framework, which combines efficient base station deployment methods and machine learning-based switching between different operation modes based on traffic load. Moreover, an adaptive beamforming methodology through identification of hotspots and user association, sub-channel and power allocation in heterogeneous networks is discussed in detail. [ABSTRACT FROM AUTHOR]

The synthesis of optimal motion profiles has shown to be a successful and virtually inexpensive solution for enhancing energy efficiency of mechatronic systems. A typical application is the design of point-to-point motion profiles for one-degree-of-freedom mechatronic systems. This paper proposes a new method for designing minimum energy trajectories for servo-actuated systems. The problem is solved by exploiting the knowledge of the structure of the optimal solution. That allows to solve the motion design problem by solving a set of nonlinear equations and, if needed, some basic optimization procedures, formulated after some suitable continuity conditions. The herein proposed method applies to systems with and without energy regeneration capability, for maximum adaptability to most industrial applications. The method also handles jerk, acceleration, and velocity constraints, which are typical requirements in many practical applications. The number of equations and thereby the computational time depends on the number of active constraints and on whether negative power is dissipated or regenerated. Overall, the method results to be suitable for Real-Time applications, also in the most challenging case in which all the constraints are active and the system cannot regenerate negative electric power. The accuracy and the effectiveness of the planning method is tested numerically, by comparing the solution to the one obtained by a general purpose optimal control solver and then also experimentally using a lab prototype. • Planning efficient trajectories is effective to save energy in servo-actuated systems. • Pre-defined motion laws do not ensure energy optimality. • The semi-analytic optimization procedure allows to cope with on-line requirements. [ABSTRACT FROM AUTHOR]

An increasing number of federal and state regulations and financial incentive programs have been enacted in recent years to promote renewable energy systems integration in new buildings. This requires designers to analyze the initial and lifecycle costs of feasible renewable energy systems in order to optimize their integration in buildings during all design phases. The goal of this study is to support designers in generating optimal building schematic designs to maximize the integration and generation of onsite renewable energy. To accomplish this goal, this paper presents the development of a novel multi-objective optimization model that can be used during the schematic design phase to identify optimal building design decisions including dimensions, orientation, location on site, solid-to-void ratio, and the type and size of onsite renewable energy systems. The optimization objectives of the developed model focus on maximizing the generation of onsite renewable energy, maximizing savings-to-investment ratio of all integrated renewable energy, and minimizing both construction and renewable energy costs. An application example of optimizing the schematic design of a 6500 m2 building was analyzed to highlight the model original contributions and its novel capabilities. The results of this analysis highlight the original contributions of the developed model and its novel capabilities that can be used to maximize the generation of onsite renewable energy and its savings-to-investment ratio while minimizing building cost by optimizing building schematic design and renewable energy (RE) technology decisions. • Optimizing building schematic design with renewable energy integration. • Maximizing RE generation, savings-to-investment ration, and minimizing bilding costs. • Complying with building design requirements and renewable energy constraints. • Optimal building design parameters and renewable energy technologies. • Analyzing the impact of design decisions on renewable energy and building costs. [ABSTRACT FROM AUTHOR]

The applicability of the traditional energy-saving renovation method for greenhouses is limited and influenced by the subjective decisions of researchers. Therefore, in this paper, a multifactor and multiobjective unbiased evaluation method is proposed based on the orthogonal test and entropy methods to study energy-saving transformation of solar greenhouses. The results show that the thickness of the gas layer, type of front roof glass and thermal insulation material of the rear roof greatly influence energy consumption, accounting for 45.5 %, 34.4 % and 15.3 %, respectively, of the total energy consumption, followed by the type of gas, thickness of glass and thermal insulation material of the heat storage wall, accounting for 3.2 %, 1.1 % and 0.5 %, respectively, of the total energy consumption. Based on energy savings, carbon emission reduction, investment cost and payback period, all renovation cases are comprehensively analyzed by the entropy weight method. The results show that Case m is the best renovation case, i.e., the front roof comprises three-layer glass, the glass thickness is 3 mm, the width of the cavity is 6 mm, which is filled with krypton gas, the rear roof insulation material is 100-mm thick extruded polystyrene (XPS), and the heat storage wall insulation material is 100-mm thick expandable polystyrene (EPS). This renovation method provides a systematic and objective theoretical basis for energy-saving renovation of solar greenhouses in cold regions. • Solar greenhouse renovation cases were constructed through orthogonal experiments. • Impacts of various factors on greenhouse energy consumption were analyzed. • Renovation cases were valuated by multi-objective entropy weight method. [ABSTRACT FROM AUTHOR]

Due to the stochastic characteristics of wind, keeping high-quality power into the grid-dependent hybrid wind energy systems is important for effective energy utilization, especially when there are non-linear loads involved. In this paper, a dual layer random features kernel mean p-power (MRFKMP) adaptive control for the grid interfaced converter of a DFIG-PV based hybrid energy system is implemented for the purpose of maintaining power balances and quality power at both the source and grid ends while operating at unity power factor. The introduced control extracts the weight of stator and load current components with finer convergence and lower computational complexity, excluding complex reference transformations. The dual layer adaptive control improves grid side converter functionality by improving power quality, interruption of loads, taking into account reactive power and harmonic remediation under steady-state and dynamic scenarios. In the secondary step, to achieve optimal DFIG-PV control response, the stability of the DC-link, which is the main coupling element for power transfer between DFIG, solar PV, and battery energy storage (BES), is reinforced by tuning the DC link voltage controller with the white shark optimization (WSO) algorithm. Under the worst-case scenario of large step wind change, the primacy of the envisaged WSO algorithm for tuning DC-link PI controller has been evidenced. It is discovered that WSO tuning positively affects the settling time and oscillatory response of the DC-link voltage not just at the outset, but also throughout the subjugated dynamic wind speed change. Power flow to the grid is stable due to optimized DC-link voltage performance. WSO parameter tuning improved efficiency is validated using MATLAB simulation results and compared to GA and PSO techniques in this study. [ABSTRACT FROM AUTHOR]

With the development of fluid transmission and distribution network in the direction of intelligent pipe network, it is urgent to solve the power supply problem of many instruments in the pipe network. However, solving the problem of power supply for intelligent pipe network systems in remote areas and poor site conditions is a challenging task, and the power supply problem can be solved if the fluid energy in the pipe network can be harvested to power instruments such as sensors. Valve is essential equipment in pipe network system, and installing impeller in their spool can harvest fluid energy for power generation. In this paper, in order to improve the ability of the impeller to harvest fluid energy, the blade profile is optimized by parametrically defined method based on the Isight platform. The energy harvesting under different parameters is obtained through CFD simulation software, the Kriging model is established according to the sample data, and the NSGA-II algorithm is used to optimize. After optimization, the impeller shaft power is increased by 7.22%, the energy harvesting efficiency is increased by 7.50%, and the optimal blade profile is obtained. The ability of the impeller to harvest fluid energy is significantly improved. [ABSTRACT FROM AUTHOR]

Electric vertical takeoff and landing (eVTOL) aircraft is a promising solution for future urban air mobility, but it suffers from insufficient energy and power density as opposed to fossil fuel-based aircraft. This paper proposes a bi-layer design optimization method for electric propulsion systems. To tackle the issue of high-dimension design parameters and multiple performance constraints, the design process is divided into two parameter optimization problems. In the upper layer, only scalar parameters of components are considered to determine a feasible sizing result subject to energy-flow limits. The outcome of this layer further serves as the initial guess of the lower design layer. The constraints of power conservation and conversion among components are taken into account. The optimization is scheduled in a cascaded paradigm that maximizes the total energy efficiency with minimized total weight. The blade-element momentum method is employed to estimate propeller thrust and torque coefficients. The proposed method is applied to a tilt-wing eVTOL. The sizing results shows that the eVTOL mass is reduced by 11.66% as opposed to the benchmark method. The proposed method provides a generic framework for sizing and design optimization independent of configuration and mission profiles. • Sizing and design of eVTOL are cascaded as bi-layer optimization problems. • Data-driven iterative sizing method is used to minimize eVTOL mass. • Shaft power-based design optimization is used to maximize propulsive efficiency. • The blade-element momentum theory is used estimate propeller performance. • The voltage drop of lithium-ion batteries is considered in optimization cost. [Display omitted] [ABSTRACT FROM AUTHOR]

A heat pump driven atmospheric water harvesting system, which can be operated under direct cooling mode and desiccant wheels assisted cooling mode, was proposed in this paper. Investigations were carried out to find out the optimal operation mode and operation parameters under different ambient conditions and daily water harvesting requirements. Optimization studies were firstly carried out on each operation mode to find the recommended operation parameters, namely evaporation temperature and daily operation hours, for different ambient conditions, daily water harvesting rate and maximum operating hours. Then, the optimal operation modes, which has lower daily power consumption under their own recommended operation parameters, were suggested on the psychrometric chart. The results showed that, for different combinations of daily water harvesting rate and maximum operating hours, there exist a critical humidity ratio and a critical relative humidity ratio for each case. When ambient air relative humidity and humidity are both higher than the critical values, it is recommended to use the direct cooling mode. Otherwise, the desiccant wheels assisted cooling mode should be used. Lastly, performances of the system in different climate zones were analyzed. Under the optimal operation mode, the annual average water harvesting efficiency was from 1.08 to 1.22 kg/kWh in Urumqi, from 1.50 to 1.78 kg/kWh in Beijing, and from 2.22 to 3.72 kg/kWh in Haikou, respectively, when daily water harvesting rate is from 50 to 200 kg/day and maximum operating hours is from 6 to 10 h. • Propose a water harvesting system that can be operated under two operating modes. • Study the optimized operating parameters and performances for each operating mode. • Suggest optimal mode based on ambient conditions and water harvesting requirements. • Study annual performances in three typical cities with different climate conditions. [ABSTRACT FROM AUTHOR]

Filling phase change material (PCM) in the glazed system is crucial to achieving thermal inertia enhancement and solar radiation regulation. However, most previous investigations focused on the energy performance of the PCM glazed system (PCMGS) in typical periods, and the optimization of PCMGS was mainly conducted by control variables to obtain the optimal value of a single parameter, which cannot accurately guide for the long-term application of PCMGS. In this paper, a global optimization program coupled with a numerical model of PCMGS and differential evolution algorithm (DEA) was developed. The annual energy performance of PCMGS was simulated as the objective function to obtain the optimal values of 11 parameters. Subsequently, the climatic applicability of PCMGS was demonstrated by comparing the annual energy performance with that of the traditional double-glazed system (TDGS). The results indicate that the objective function value converges to optimal after 2000 iterations. PCMGS performs preeminent adaptability in hot regions, followed by intermediate regions. The energy consumption of the building when PCMGS is used is reduced by 13.80% and 1.58% respectively compared to the TDGS. However, in cold regions, PCMGS reveals deplorable adaptability, as the energy consumption increased by 36.58% compared to the TDGS. [ABSTRACT FROM AUTHOR]

Due to increasing amounts of energy being supplied by intermittent renewable energy sources, future energy systems need to be able to react flexibly to sudden changes. To address this challenge, a large body of research is devoted to improving the utilization of energy flexibility of various types of energy resources on the supply and demand side. This is commonly done by applying mathematical models. The objective of this paper is to review such models and identify common modeling approaches used to quantify and optimize the use of flexible energy resources. The literature review is conducted systematically, encompassing 215 publications that feature 694 models of different energy resources. The main distinction of this work, in comparison to other reviews on the topic of energy flexibility lies in its focus on the mathematical formulation of models of energy resources and the quantification of the models' level of detail. The aim is to identify common modeling approaches and the corresponding research problems they address. The review shows that the majority of research efforts are directed towards the management and optimization of distributed energy resources. The most commonly employed modeling approach involves abstract black-box models with limited levels of detail. Linear programming and meta-heuristics are frequently used to optimize energy flexibility. A critical reflection of the current common practice is conducted and recommendations for future research are formulated. [Display omitted] • Literature review of 215 reports presenting model approaches for energy flexibility. • Categories of research problems identified and deduction of two main research focuses. • Presenting models of common energy resources classified into different model types. • Mostly low level of detail seen in modeling energy flexibility. • Optimization models are used to represent and quantify flexibility. [ABSTRACT FROM AUTHOR]

Three-way catalytic converters have shown superiority in redox exhaust gases. However, how to improve its redox capability is a huge challenge in gasoline engines fueled by gasoline-ethanol blended fuels. This paper investigated the effect of a Palladium/cerium dioxide (Pd/CeO 2) three-way catalytic converter on the combustion and emission characteristics of a gasoline engine. The emission characteristic of gasoline oxidation catalyst was investigated in terms of total hydro carbons (THC), carbon monoxide (CO), and nitrogen oxides (NO x) emissions. Engine combustion characteristic was also studied in terms of brake specific fuel consumption (BSFC), brake power, fuel consumption rate, and exhaust gas temperature. The experiments were conducted on a four-cylinder direct injection gasoline engine with rotational speed between 2000r/min and 3000r/min, gasoline-ethanol blending ratio not exceeding 30%, and torque not exceeding 100 N·m. In addition, an optimization method integrated with the response surface method (RSM) and non-dominated sorted genetic algorithm II (NSGA II) was used to optimize the objective functions including BSFC and NO x emission. The regression equation of the response was obtained by RSM and brought into NSGA II to obtain Pareto optimal solutions. Different frontiers were implemented and the 100th iteration number was chosen as the Pareto frontier. The findings indicated that the optimal BSFC and NO x emission values were 380 g/(kW·h) and 350 ppm, respectively, which were attained with a torque of 45 N·m, a gasoline-ethanol blending ratio of 17%, and a rotational speed of 2027r/min. • Effect of three-way catalytic converter on combustion and emission characteristics are investigated in this work. • The optimization method integrated with the RSM has been developed. • The NSGA II is employed to investigate the multi-objective optimization. • The three-way catalytic converter plays a very important role. [ABSTRACT FROM AUTHOR]

The design requirements for the propellers of vertical take-off and landing (VTOL) UAVs are contradictory in the cruise phase and VTOL phase. To alleviate the contradiction, the concept of passive variable-pitch propeller (PVPP) using the aerodynamic moment to change the pitch was proposed by previous researchers, which can adaptively adjust the pitch as the advance ratio changes. However, this type of PVPP has not been widely used due to its complex balance module. This paper constructs a new framework to design such PVPP to simplify its balance module and increase its utility. We proposed new methods to parameterize the shape of PVPP and established mathematical models for each optimization in the design process. Subsequently, a case study verified the feasibility of the design method to simplify the trim module. By optimizing the position of the blade-pivot axis, the design results can work efficiently in multiple working conditions and can also achieve the highest efficiency at a specific advance ratio, which is of practical significance for VTOL UAVs to reduce energy consumption and increase payload in mission profiles. We conducted additional analysis on the characteristics of PVPP based on two aspects: (A) the influence of the blade airfoil on the pitching moment characteristics of PVPP; (B) the comparison between the PVPP with normal layout and the PVPP with canard layout. Overall, the methodology and general rules proposed in this paper provide a theoretical basis and technical support for designing PVPP for UTOL UAVs. [ABSTRACT FROM AUTHOR]

• The paper is dedicated to proposed a cluster-based routing protocol for WBAN. • The paper utilizes the benefits of machine learning. • A novel approach, MGWOQL, is proposed for cluster head selection and updation. • The multi-objective function is designed to select the optimal cluster head node. • The result shows better network longevity, residual energy, and path loss. Recently, Wireless Body Area Networks (WBAN) have been developed to advance Internet-of-Things (IoT) that play an essential role in biomedical applications. While deploying these applications practically, there may arise associated issues. Among all the available problems, the primary concern is energy utilization among these resource-limited sensors during data communication. These sensors continuously sense the signal and send messages to other nodes. There is a need to optimize the energy utilization in WBAN. This paper proposes a cluster-based routing protocol for WBAN with the benefits of machine learning to predict energy wastage. A Modified Grey Wolf Optimization with Q-Learning (MGWOQL) is proposed for cluster head selection and updating. The proposed protocol used different objective functions to minimize the energy utilization of clusters by selecting the optimal cluster head (CH). The simulation was performed on the MATLAB platform under different conditions. The result analysis shows its efficiency in terms of energy for WBAN. [ABSTRACT FROM AUTHOR]

• Improve the performance of micro-grid based on demand response programs. • Reduce operating costs of micro-grid based on probability distribution function models. • Improving the speed and accuracy of dynamic response system based on the Modified Shuffled Frog Leaping Algorithm. • Improving micro-grid performance in the presence of stochastic and non-stochastic power generation sources based on probability models. In this paper, an energy consumption optimization problem is investigated for a representative Micro-Grid (MG) system including Distributed Energy Resources (DERs) such as Wind Turbines (WTs), Photo-Voltaic (PV) arrays, Combined Heat and Power (CHP), Fuel Cell (FC), and boiler to supply the electricity and thermal load demand instantaneously. This problem is mathematically formulated considering resources and load uncertainties using Beta, Normal, and Weibull distribution function with suitable coefficients. In this regard, a model based on the minimization of objective function consisting of operating costs, pollution, and distribution lines electrical losses is presented and solved using the Modified Shuffled Frog Leaping Algorithm (MSFLA). The main objective of this paper is to choose the best collection of DERs and other resources in MG to study which of them is optimum to achieve the objective function. Demand response program (DR) based on the electricity price is considered as a Time of Use (TOU) tariff in which the simultaneous simulations will be figured out. The comparison of the proposed method with the existing method has verified the superiority of MSFLA approach to solve this optimization problem. [ABSTRACT FROM AUTHOR]

The resistance reduction optimal design of amphibious vehicles is essential to improve efficiency and reduce energy consumption. In this paper, the hydrodynamic characteristics of an amphibious transport vehicle are investigated by Computational Fluid Dynamics (CFD) method based on the Reynolds-averaged Navier-Stokes (RANS) solver. A set of towing tests are conducted to verify grid independence and computational accuracy. Four typical shape control parameters are selected as optimal design variables and the independent effects of each variable are analyzed. The sample set is obtained using Latin Hypercube Sampling (LHS), and corresponding three-dimensional models are generated based on the parameters of each sample point. CFD calculations are then used to obtain the resistance characteristics of the models. The Kriging method is utilized to establish an approximate model, and the credibility of the model is verified. The Non-Dominated Sorting Genetic Algorithm-II (NSGA-II) is used to find a global optimal solution or set based on approximate models. Two optimized configurations, Opt1 and Opt2, are obtained, corresponding to single- and multi-objective optimization results, respectively. At the design speed of the amphibious transport vehicle, Opt1 has the highest resistance reduction rate of 12.7907% under the full load condition, while Opt2 has resistance reduction rates of 11.0863% and 8.5293% with and without load, respectively. [Display omitted] • The resistance characteristic of an amphibious transport vehicle is investigated. • The RANS method is adopted for resistance prediction. • Kriging approximation models are established based on LHS. • NSGA-II method is used for the optimal design. • The optimized models have significant improvements in resistance. [ABSTRACT FROM AUTHOR]

Thesolid oxide fuel cell (SOFC), as an economically friendly power generation system, shows a promising prospect for the future while hydrogen supply as its fuel is one of the main challenges. In this paper, an integrated system is described and evaluated by energy, exergy, and exergoeconomic, aspects. To find an optimum design state three models were analyzed to reach higher energy and exergy efficiency while system cost is at its lower value. After the first and main models, a Stirling engine reuses the first model's waste heat to generate power and enhance efficiency. In the last model, a proton exchange membrane electrolyzer (PEME) is considered for hydrogen production purposes by using the surplus power of the Stirling engine. The components validation is performed in comparison with the data presented by related studies. Optimization is applied by exergy efficiency, total cost, and hydrogen production rate considerations. The results show that the total cost of the model (a), (b), and (c) is 30.36 ($/GJ), 27.48 ($/GJ), and 33.82 ($/GJ), and the energy efficiency is 31.6%, 51.51%, 46.61% and the exergy efficiency is 24.07%, 33.0.9%, 29.28% respectively with the cost of at the optimum condition achieved by 2708 A/m2 current density, 0.84 utilization factor, 0.38 recycling anode ratio, 1.14 air blower and 1.58 fuel blower pressure ratio. The optimum rate of hydrogen production will be 138.2 kg/day and the overall product cost will be 57.58 $/GJ. In general, the proposed integrated systems show a good performance in both thermodynamics and environmental and economic aspects. [Display omitted] • To model a Stirling engine coupled with a fuel cell. • Simulation of a biomass gasification-based hydrogen production system. • Using nanomaterial for enhanced performance of the hydrogen production. • To analyze the first law efficiency and exergy efficiency of the system. [ABSTRACT FROM AUTHOR]

• The structure scheme of hybrid pumping unit is proposed for the first time in this paper. New forms of wind energy can effectively reduce oil field energy consumption by 90%. • The Mechanical-hydraulic energy storage transmission scheme is proposed for the first time. The coupling of wind energy and electric energy can be realized through the new hybrid transmission structure. • A double pump energy supply system is proposed for the first time in this paper. Double pump energy supply system can improve the utilization efficiency of wind energy. Wind energy can replace electric energy to drive pumping units. Wind power generation is used in oil fields, but the energy saving effect is not obvious. This paper proposes a new method of wind energy utilization. Wind energy directly drives pumping unit by new mechanical-hydraulic transmission system without the intermediate conversion into electricity for the first time. This new system can realize hybrid power of wind and electricity and adjust the output energy of the system to meet load requirements. It can effectively address the problems of wind energy utilization and is especially suitable for oil fields in remote areas where electricity is insufficient and its exploitation is difficult. This paper studies the performance of wind-pump system and mechanical-hydraulic system, respectively. Mathematical model is verified by numerical simulation and experiment studies. The economic matching model is established and the parameters of the system are optimized. Finally, this paper puts forward the optimization matching design theory of wind-motor hybrid system. Wind energy utilization rate is higher 60% under optimal parameter combination. [ABSTRACT FROM AUTHOR]

The growing interest in fully decarbonizing worldwide energy systems requires abandoning traditional generation expansion planning in favour of other flexibility-enabling energy system planning tools allowing the integration of energy storage and sector coupling. Therefore, this paper proposes a mixed-integer linear programming formulation focused on enabling flexibility provision on integrated energy systems targeting independent sizing of power and energy capacities for simultaneous generation and storage expansion planning. The optimization problem minimizes investment, maintenance, operation and emissions costs over a 20 year horizon with hourly resolution. In this context, islands represent suitable study cases for the energy transition due to their exceptional renewable availability, and fast-paced development; despite being regions with extreme external dependence and isolation. In particular, the island of Santiago, Cape Verde is selected as study case given its existing targets regarding reaching 50 and 100% renewable shares in 2030 and 2040, its data availability, and the extreme seasonal variation in wind and solar resources. Four scenarios are considered. The first and second dismiss flexibility: business as usual (BAU), and another one aligned with the government goals (Green). Then, the other two exploit it: one equivalent to Green, FlexG, and a last one finding the Optimal. In addition, three energy demand growth levels are considered in order to reduce uncertainty, corresponding to 1, 3 and 5%. Last, a sensitivity analysis with three additional scenarios is performed to provide a thorough view of Cape Verde's energy future. The results highlight the importance of flexibility exploitation which provides up to 85% savings and allows to decarbonize other sectors via electrification. • Solar and hydro pumped storage drive the transition. • Wind power's role increases in hydrogen intensive and surface constraint scenarios. • Integrating flexibility into generation expansion planning halves power needs. • The current paradigm is the most expensive option, doubling emissions in 20 years. • The Optimal configuration achieves 96% renewable penetration. [Display omitted] [ABSTRACT FROM AUTHOR]

Solar energy is expected to be a significant contributor to meet the increasing global energy demand. Rooftop photovoltaic (PV) systems account for a substantial portion of the global solar energy potential. However, optimizing the size and layout of these systems remains challenging. Existing approaches either focus on maximizing energy generation, heavily restrict the space of potential layouts, ignore inverter-type implications, or neglect practical aspects, such as minimizing self-shading. This paper presents a mixed-integer programming (MIP) model to address these limitations for PV systems installed on flat rooftops. The proposed model optimizes the net present value (NPV) and can produce multi-azimuth layouts while accounting for practical considerations, including mitigating self-shading, and ensuring rooftop walkability. The proposed model is adapted for systems that utilize micro-inverters or string-inverters. Two case studies are conducted to evaluate the performance of the proposed model. In one case study, the model is applied to a residential area. It is numerically shown that in some instances of capital costs and billing policies, the use of multi-azimuth layouts could significantly improve the NPV compared to the use of single-azimuth layouts with parallel rows of panels. The proposed model solutions are compared to an existing optimized installation in the second case study. The proposed model multi-azimuth layout solution improves the NPV by 10.17%. When restricted to single-azimuth layouts, the proposed model produces the same design as that of the existing installation in only a few seconds. • A novel model for optimizing the size and layout of rooftop PV systems is presented. • Sizing is obtained by optimizing the NPV of the PV system. • A graph representation allows the model to produce solutions with multi-azimuth layouts. • The produced solutions mitigate self-shading, ensure rooftop walkability, and consider inverter type. • Two case studies demonstrated the advantages of systems with multi-azimuth layouts. [ABSTRACT FROM AUTHOR]

Since solar energy is intermittent, finding the best solutions for solar operated devices is a challenge. Multiple techniques exist to reach the best solutions for optimal solar operated devices. A thorough review of solar energy systems' optimization methods and tools is presented in this work. The intelligent optimization techniques for solar energy systems are discussed, including their functions, constraints, contributions, mathematical models, and analysis methods. Optimization studies using new and traditional generation techniques are analyzed, and a few optimization methods, including combined hybrid algorithms, are presented. New generation artificial intelligence algorithms have been most widely used during the last decade, needing less computational time. They have good convergence and better accuracy than traditional optimization methods. They can scan local and global optima and do robust calculations. Solar system optimization has demonstrated remarkable benefits in size, load demand, and electricity output. The improvements reduce operating expenditures, power losses, and peak output integration and controllability. With a 50% rise in power prices, the optimal number of solar collectors rises by approximately 25%. However, with adjustment as per optimization techniques, the solar absorption cooling system's maximum thermal efficiency can be increased up to 75%. The present study recommends using two or more algorithms to overcome the curbs of a single algorithm. The main aim of the optimization strategies, according to this assessment, is to reduce capital expenditures, operation and maintenance expenses, and emissions while improving system reliability. The paper also briefly describes several solar energy optimization challenges and issues. Lastly, some practical future approaches for establishing a reliable and efficient solar power system are proposed for developing the complex renewable energy-based hybrid system. • A thorough review of solar energy systems' generic optimization objectives. • Background and importance of solar energy and its various applications are reviewed. • Intelligent optimization approaches for solar energy systems, including their functions, constraints, and contributions. • Optimization methods, including combined hybrid algorithms, are presented. • Practical future approaches for developing a reliable and efficient solar power system are proposed. [ABSTRACT FROM AUTHOR]

• Analysis on a multi-source (air/sun/ground) heat pump for domestic heating. • Employment of a storage tank which can be sensible (water) or latent (PCM) • Multi-objective optimization to maximize seasonal performance and minimize costs. • Thermal and photovoltaic solar sources more convenient in warmer climates. • Geothermal source more convenient for colder climates. The main target of this paper is to numerically study a multi-source (air/sun/ground) heat pump with the implementation of a thermal energy storage, using either water or PCM, for residential space heating. The system was modelled considering several sub-models for each of the components (compressor, solar panels, storage tank, heat exchangers etc.). A control strategy has been established to decide which operating mode of the system provides the highest coefficient of performance (COP). A multi-objective optimization through genetic algorithm of several decisional variables of the system was carried out, in different configurations and climate conditions, by considering different scenarios in terms of total investment and energy consumption costs, to optimize seasonal performances and investment cost of the entire system. Results show that solar thermal and solar photovoltaic collectors coupled with water storage tank give higher seasonal energy performance, especially in warmer climates, whereas the exploitation of the ground source can be more advantageous for colder climates. From the optimization analysis, it results that optimal non-dominated solutions characterized by a SCOP increase between 50% and 250% are characterized by higher investment costs between 215% and 730%, depending on the climate conditions. None of the solutions employing a PCM storage tank results economically feasible, due to a slight effect on system performance, and a much higher effect on investment costs. Finally, several cost scenarios in terms of incentives on investment costs and increased energy prices were analysed, for which the employment of scenarios with higher capital investment can be more advantageous in terms of lower total costs. [ABSTRACT FROM AUTHOR]

With the development of renewable energy, the number of microgrid systems is gradually increasing, which puts forward higher requirements for the power grid. How to make full use of the internal resources of microgrid, guarantee power supply and relieve power grid's burden is an urgent problem to be solved. In this paper, a planning and optimization strategy of a microgrid cluster consisting of shared hydrogen storage is proposed. This study is mainly divided into three stages. In the first stage, capacity planning and daily operation optimization of energy storage schemes in different scenarios are carried out through a two-layer optimization model. In the second stage, the comprehensive benefits of different scenarios are calculated by comparing whether the microgrids are in a cluster and whether the energy storage is shared. The third stage is to reasonably distribute the benefits of the members of the microgrid cluster. The results show that the complementarity between microgrids and the configuration of hydrogen energy storage can eliminate the phenomenon of power failure, reduce the dependence of microgrids on the superior grid, improve the peak regulation capacity, promote the consumption of renewable energy, and obtain economic benefits. • A microgrid cluster is constructed, realizing the mutual aid of resources. • Reinforcement learning is used for microgrid optimization. • The comprehensive benefits in shared hydrogen storage are calculated. • The benefits are reasonably distributed among participants. [ABSTRACT FROM AUTHOR]

Wave energy technologies have the potential to play a significant role in the supply of renewable energy worldwide. One of the most promising designs for wave energy converters (WECs) are fully submerged buoys. In this paper, we explore the optimisation of WEC arrays consisting of three-tether buoys. Such arrays can be optimised for total energy output by adjusting the relative positions of buoys in a wave farm. As there are complex hydrodynamic interactions among WECs, the evaluation of each parameter setting is computationally expensive and thus limits the feasible number of full model evaluations that can be made. Furthermore, these WEC interactions make up a non-convex, multi-modal (with multiple local-optima), continuous and constrained optimisation problem. This problem is challenging to solve using optimisation methods. To tackle the challenge of optimising the positions of WECs in a wave farm, we propose a novel multi-swarm cooperative co-evolution algorithm which consists of three meta-heuristics: the multi verse optimiser (MVO) algorithm, the equilibrium optimisation (EO) method, and the moth flame optimisation (MFO) approach with a backtracking strategy, we introduce a fast, effective new surrogate model to speed up the process of optimisation. To assess the effectiveness of our proposed approach, 11 state-of-the-art bio-inspired algorithms and three recent hybrid heuristic techniques were compared in six real wave situations located on the coasts of Australia, with two wave farm sizes (four and nine WECs). The experimental study presented in this paper shows that our hybrid cooperative framework exhibited the best performance in terms of the quality of obtained solutions, computational efficiency, and convergence speed compared with other 14 state-of-the-art meta-heuristics. Furthermore, we found that the power output of the best-found 9-buoy arrangements were higher than that of perpendicular layouts at at 4.15 % , 3.29 % , 3.62 % , 9.2 % , 5.74 % , and 2.43 % for the Perth, Adelaide, Sydney, Tasmania, Brisbane, and Darwin wave sites, respectively. Our investigations reveal that the best-found arrangement at the Tasmania wave site was able to absorb the highest level of wave power relative to the other locations. • A new Multi-swarm Cooperative optimisation framework is proposed to optimise wave farms performance. • A new fast surrogate model is developed to speed up expensive optimisation process of wave farm. • To improve the placement of converters, a symmetrical backtracking search algorithm is proposed. • To handle the infeasible wave converters layout during the optimisation, a new and effective repair function is developed. • The proposed optimiser enhances significantly the total power output at six wave farms compared with previous methods. [ABSTRACT FROM AUTHOR]

This paper is a research article for finding the optimal control of smart power substations for improving the network parameters and reliability. The included papers are the most essential and main studies in the field, which propose a different approach to reach the best performance in electrical power systems. The parameters for improvement are the ability for tracking of the reference signal, stabilizing the system, reducing the error in steady state and controlling the behavior in transient state. The research focuses with the reaching a better transient stability considering voltage and frequency dynamic parameters. The optimal model for the control is focused on minimizing energy consumption but maintaining the controllable parameters, exploring some optimization techniques to find the optimal control, with of aim of minimizing the response time, the energy consumption, and maximizing the reliability by means of improving the controller to be more robust. [ABSTRACT FROM AUTHOR]

Effective recovery of ethane from natural gas plays an increasingly important role as ethane is a good raw material for ethylene production. The conventional cryogenic separation process (CryoPro) is energy intensive. Absorption-adsorption coupling separation process (SorbPro) using ZIF-8 slurry may be a more energy-efficient technique as its operating conditions are milder (i.e., 273.15 K, ∼1 MPa). This paper aims to conduct the process modelling and optimization of SorbPro for CH 4 /C 2 H 6 separation. The equilibrium stage method is used to model the mass transfer units (absorption-adsorption column, desorber, etc) and the key process operating parameters are optimized simultaneously by genetic algorithm. The results show that the ethane mole fraction can be enriched from 22 mol% in the feed gas to 90.04 mol%, the ethane recovery ratio reaches 90.34%, and the energy consumption per Nm3 ethane is 0.6792 kW h/Nm3, which is 25.6% lower than that of the CryoPro (0.9128 kW h/Nm3). It indicates that the SorbPro may be a more promising approach for recovering ethane from natural gas. • Novel process (SorbPro) using ZIF-8 slurry is developed for CH 4 /C 2 H 6 separation. • Process modelling and multi-parameter optimization for SorbPro is conducted. • Ethane recovery ratio of SorbPro is higher than 90%. • Unit energy consumption of SorbPro is 25.6% lower than that of cryogenic process. [ABSTRACT FROM AUTHOR]

With the increasing demand for sustainable built environments, energy performance is becoming essential in the early design stage. Several previous studies using optimization of building form for energy performance considered simple hypothetical forms. In addition, previous multi-objective optimization (MOO) of building form and envelope did not consider views percentage to the outdoor (VPO) despite of its importance for human mental health and its conflict with energy performance. This paper proposes a novel lattice incubates boxes (LIB) method that optimizes a cellular office building form. It considers the entire building with 27 thermal zones in an attempt to mimic a real case study. In addition, it considers VPO as an objective in the MOO of building form and envelope. First, the study performs single-objective optimization (SOO) of architectural form and orientation for annual thermal Energy Use Intensity (EUI) in Cairo, London, and Chicago. Second, it performs multidisciplinary MOO of Cairo's form, window to wall ratio (WWR) and horizontal shading devices (HSD) for annual thermal EUI, annual Useful Daylight Illuminance (UDI-100–2000), and VPO. Third, it develops the LIB method to make it more applicable in professional practice through considering 72 individual thermal zones while adding more constraints. Compared to the initial forms, first part EUI savings were between 16.86% and 12.9 %. EUI, UDI, and VPO savings for Cairo in the second part were 20.16%, 11.5%, and 19.5 %. Scatterplots are developed for the second part that show rows motion has the highest positive impact on UDI and VPO, WWR has a positive impact on all objectives and HSD has high positive impact on EUI only. Savings in the third part are 11 % EUI, 8.77 % UDI, 2.86% Daylight Autonomy (DLA) and 7.8 % VPO. Scatterplots in the third part show that almost all proposed form dynamic parameters have strong impact on all objectives. [ABSTRACT FROM AUTHOR]

A skid-steer rover's power consumption is highly dependent on the turning radius of its path, with a point turn consuming much more power compared to straight line motion. As energy is the integration of instantaneous power over time, a trade-off between arcs' turning radii and lengths should be made to minimize energy consumption. Because of the skid-steer rovers' ability to do point turns the simplest and shortest way to traverse a distance between two points is by doing a point turn-line-point turn (PLP) maneuver. However, we show that wheeled skid-steer rovers there are scenarios where optimal Circle–Line–Circle (CLC) paths consume less energy than PLP paths. Therefore, the goal in this work is to find the best path from among CLC paths; Karush–Kuhn–Tucker (KKT) conditions are used to systematically obtain the optimally energy-efficient answer for the CLC paths. It is assumed that the rovers move forward on hard flat ground. For solving the problem, a new practical constraint constant -v c is suggested. In this paper, comparing the KKT conditions and experimental results reveals that the lowest total energy consumption for CLC paths with or without considering constant -v c constraint is obtained by selecting turning radii equal to R ′ (the half of slip-track). [ABSTRACT FROM AUTHOR]

• Optimization-based comparison of natural gas liquefaction processes is proposed. • Broad process flowsheet complexity and natural gas processing scales are considered. • The AP-X process has the lowest energy consumption and cost for 8 MTPA plants. • The single mixed refrigerant process has the highest profit for small-scale plants. • Energetically better processes may not provide a higher economic return. The demand for liquified natural gas grows as it is an energy resource that has more flexible means of transport than non-liquefied natural gas, and is more eco-friendly than other fossil fuels. This paper focused on comparing five different mixed-refrigerant liquefaction processes to determine which one is the most efficient in energetic and economic terms for four different natural gas processing scales (20,000 kg/h and 2, 4, and 8 MTPA of natural gas feed). Simpler processes with one or two refrigerant cycles (single mixed refrigerant, propane pre-cooled mixed refrigerant, and dual mixed refrigerant) and more complex processes with three cycles (mixed fluid cascade and AP-X) were analyzed. All processes were simulated in Aspen HYSYS, and the energy consumption was optimized by a communication between the simulator and an optimization algorithm based on Particle Swarm Optimization coded in MATLAB. The economic analysis was performed using the CAPCOST technique and the rule of six-tenths for upscaling. In the best-optimized scenario, the results show that the most complex process, AP-X, consumes the lowest amount of energy (0.2367 kWh/kg-LNG) while the simplest one, the single mixed refrigerant, consumes the highest value (0.2561 kWh/kg-LNG). In the economic analysis, the results indicate that energetically better processes may not provide a higher economic return even on large scales. [ABSTRACT FROM AUTHOR]

As an effective technology to reduce traffic pollution emissions, the new energy vehicle industry has developed rapidly in recent years, and the sales of new energy vehicles have doubled in 2021. Accurately forecasting methods provide important references for industrial policy deployment, infrastructure construction and energy demand estimation. In order to predict the development trend of China's new energy vehicle industry under the limited data samples, this paper proposes an optimized discrete grey power model to fit the nonlinear relationship between grey information factor and temporal factor. Firstly, the grey differential equation and its discrete form are directly constructed by using power accumulated data, which simplifies the integral solution process of the traditional grey power model. Secondly, fractional accumulation operator is introduced into the discrete model to ensure the new information priority of the original data. Then, the heuristic algorithm is used to accurately estimate the new parameters of the proposed model. Thirdly, the proposed discrete model is the unified form of two existing discrete grey power models, which expands the scope of modeling and has higher adaptability. Simulation experiments and two numerical cases are used to verify the effectiveness of the proposed method. Finally, the proposed method is used to predict the annual sales and ownership of new energy vehicles in China. The predicted results show that by 2025, China's new energy vehicle sales will reach 8.84 million, accounting for about 24% of the total vehicle sales, surpassing the industry development target (20%) set by the Chinese government. [ABSTRACT FROM AUTHOR]

The most important aspect of sustainability in manufacturing is the preservation of energy and natural resources. For modern production, optimized processes that minimize negative impacts on the environment are becoming increasingly important. This can be achieved by increasing energy efficiency through low, clean, and renewable energy consumption. There are many ways to produce less pollution, emissions, and waste in machining: by using more environmentally friendly cooling methods; by applying methods that reduce or eliminate the need for utilization of cooling lubrication; improving the energy efficiency of machining operations; determining the optimal cutting conditions that save resources by increasing machining productivity or reducing the metal removal rate (MRR); minimizing power consumption; and reducing carbon dioxide emissions. This article gives an idea of modern manufacturing with a focus on analyzing the current state of machining operations in terms of saving production resources and ensuring more environmentally friendly production using greener cooling methods of machining such as Dry, Conventional cooling systems, Minimum quantity of lubricant (MQL), Minimum quantity of cooling lubrication (MQCL), Nanofluids, Biodegradable Vegetable Oils, Cryogenic Lubrication, and High-Pressure Cooling (HPC). Finally, the important modern trends of providing resource-saving and environmentally efficient technologies in modern sustainable manufacturing are discussed in this paper. • Present review article deals with the sustainability and resource saving aspects during machining operations. • Different cooling conditions were presented. • Waste minimizing, recycling, pollution etc. were discussed during machining oeprations. [ABSTRACT FROM AUTHOR]

Reinjection is important for sustainable utilization of geothermal energy, and geothermal well placement determines the fate and recovery efficiency of doublet system. The simulation-based optimization method usually requires a large number of intensive forward simulations to evaluate the reservoir performance considering every possible well configuration. In this paper, a surrogate model based on random forest technique was developed to reduce the substantial computational burden of forward simulations, which was combined with genetic algorithm to develop a robust optimization approach of geothermal well placement in heterogeneous geothermal reservoirs. A number of statistical indicators including the maximum and minimum permeabilities in the three different representative areas surrounding the geothermal wells were incorporated into the surrogate model, and its prediction accuracy can be significantly improved. The reasonability and efficiency of the developed optimization method for well placement were demonstrated using three case studies including homogeneous and heterogeneous geothermal reservoirs based on a doublet system in the Dezhou geothermal field, China. The results show that the surrogate model-based optimization method can not only robustly and accurately find the optimal position of injection well given a certain position of production well, but also work well when the simulation-based optimization method fails in complex geothermal reservoirs. • A surrogate model for geothermal doublet is developed based on random forest method. • A robust surrogate model-based optimization method is formulated for well placement. • Three case studies verify the accuracy and efficiency of the optimization method. [ABSTRACT FROM AUTHOR]

Evolutionary robotics is concerned with optimizing autonomous robots for one or more specific tasks. Remarkably, the energy needed to operate autonomously is hardly ever considered. This is quite striking because energy consumption is a crucial factor in real-world applications and ignoring this aspect can increase the reality gap. In this paper, we aim to mitigate this problem by extending our robot simulator framework with a model of a battery module and studying its effect on robot evolution. The key idea is to include energy efficiency in the definition of fitness. The robots will need to evolve to achieve high gait speed and low energy consumption. Since our system evolves the robots' morphologies as well as their controllers, we investigate the effect of the energy extension on the morphologies and on the behavior of the evolved robots. The results show that by including the energy consumption, the evolution is not only able to achieve higher task performance (robot speed), but it reaches good performance faster. Inspecting the evolved robots and their behaviors discloses that these improvements are not only caused by better morphologies, but also by better settings of the robots' controller parameters. • We investigate an evolvable robot system where controllers and morphologies evolve. • Robots' energy usage is approximated using a mathematical battery model. • Fitness functions are the robot's gait speed and robot's energy consumption. • Energy efficiency in fitness improves task performance; changes the morphology. [ABSTRACT FROM AUTHOR]

The use of solar powered dish-Stirling engines to convert thermal energy into electricity is a promising and renewable alternative in reducing the fossil fuel consumption. However, insufficient experimental data regarding the operating parameters limit its characterization, modelling, design, optimization, and further utilization. Therefore, the first aim of this paper is to introduce a comprehensive review and systematic summary of state of the art on research and progress of the solar dish technology as a concentrated solar power technology. The efforts mainly include the recent advances, technological challenges, and optimization frameworks in solar dish collector research field. One of the most critical features of this study is discussing novel combinations of solar dish collectors with other power generation devices including PV cells, thermoelectric devices, and thermal collectors. The review sheds light also into recent thermodynamic cycles that can be powered by solar dish collector as well potential polygeneration applications that can be engineered. Further, the most relevant researches adopting Nanofluids and Phase Change Materials in solar dish collectors are reviewed. In the final part, future prospects and challenges that require further investigation are also discussed. It is expected that this review will serve as a brief guideline for the solar dish-technology enthusiast. [ABSTRACT FROM AUTHOR]

Hydrogen energy storage (HES) transforms and stores electric energy from the grid into hydrogen, and supplements other energy storage and demand response resources in addressing challenges in renewable-intensive power systems. Understanding how to optimally utilize an HES system to maximize its economic benefits from stacked value streams is highly important to its development and deployment. This paper presents a techno-economic assessment framework for an HES system considering three common energy delivery pathways and multiple grid and end-user services. Models are developed to capture the operational capability, flexibility, and constraints associated with hydrogen production, compression, storage, and utilization as well as different grid services in an economic assessment. To define the technically achievable benefits, an optimal dispatch formulation is proposed to maximize the economic benefits over a representative year with an hourly time step considering the trade-offs among different value streams. Representative case studies are designed and carried out to show how system configuration, energy delivery pathways, and grid services may affect economic benefits. It was found that value streams from bundling grid services account for up to 76% of the total benefits and are critical for an HES project to be financially viable. • An assessment framework is proposed for hydrogen energy storage considering stacked value streams. • Multiple energy delivery pathways and grid services are modeled. • Key techno-economic characteristics of hydrogen energy storage are captured. • A mixed-integer linear programming formulation is proposed to define technically achievable benefits. • Comprehensive analyses are performed to identify key factors that affect cost-effectiveness. [ABSTRACT FROM AUTHOR]