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Conventional energy resource problems made power system planners apply more renewable energy sources (RESs) in the power system. RESs have a significant impact on fulfilling the world's energy demand, protecting the environment at least in the exploration phase, and providing energy reliability as well as security for the new generation of power grids. In this regard, wind power generation is considered a promising solution. However, by increasing the share of wind power generation units in the power sector, some challenges, such as voltage problems, transient stability, increasing system losses, and line congestions can occur. Consequently, an alternative solution is required to accelerate wind power units' connections to the grid by preventing the problems caused by the penetration of wind power. In this regard, Flexible Alternating Current Transmission Systems (FACTS) devices can play an important role while they can improve power grid dynamic performance. This paper presents a review of the optimization techniques applied to accelerate the penetration of wind energy by exploiting FACTS devices. The topic identifies the objectives and optimization formulations, as well as schemes and models available in the published literature. This survey gives noteworthy insights to the researchers to cover available solutions in these regards. The literature in the field is summarized in different aspects, such as FACTS technology, objective functions, constraints, optimization methods, planning or operation purposes, and load flow types. At each aspect, a comparison is derived to recognize the most important issues. The result of the review is the finding of future work and contributions which can be done in the field of wind energy integration acceleration using FACTS devices and the output of the paper can be used as a guide for researchers to find a new path for research in this area. [ABSTRACT FROM AUTHOR]

This paper presents a framework for the efficient design and evaluation of a standalone hybrid renewable energy system (HRES) to meet the energy requirements of a rural community in the north-eastern region of Nigeria. The proposed microgrid system incorporates solar photovoltaic, wind turbines, biomass gasifier, fuel cell, and Battery storage. The sizing of each component is determined through the utilization of real local meteorological data and the load demand over a year, employing the Levy flight-salp swarm algorithms (LF-SSA). The optimization objective is to minimize the annualized system cost (ASC) of the HRES, while also considering the reliability constraint of Loss of power supply probability (LPSP). The comparative outcomes demonstrate that the LF-SSA surpasses other examined algorithms, namely the salp swarm, genetic algorithm, and HOMER software by achieving substantial cost savings of $29033, $50796, $191771 respectively in relation to the proposed HRES. The result further shows that, the LF-SSA provides the lowest LCOE of $0.933162/kWh, in contrast to SSA at $0.947737/kWh, GA at $0.958660/kWh, and HOMER at $1.075351/kWh. Additionally, the results indicate that the implemented Energy Management System (EMS) has successfully facilitated the establishment of an environmentally friendly and cost-effective energy system. • An efficient energy storage system is essential for managing intermittent energy supply. • The fuel cell can generate DC power by combining hydrogen with oxygen. • The amount of power generated by a PV panel depends on temperature and solar radiation intensity. • Deterministic rule-based controllers operate based on pre-defined rules. • Energy Management System coordinate the flow of power between different components. [ABSTRACT FROM AUTHOR]

Energy shortage has always been a problem that the world needs to face. The combined cooling, heating, and power (CCHP) system, as a multi-level energy utilization system that can provide cooling, heating, and electric energy simultaneously, is considered to have good development prospects in alleviating energy problems. In addition, because of the rapid development of electric vehicles (EVs), using EVs as power supply devices has become a hot topic of research. In this paper, EVs are combined with the CCHP system as new power supply equipment, and the influence of the season on the user's cooling, heating, and power demand is considered. Aiming at the minimum economic cost, the system is optimized by using the PSO algorithm in two operating modes: following electricity load (FEL) and following thermal load (FTL). The final results show that the participation of EVs can reduce costs in both operating modes, especially in FTL mode, which can reduce costs by 4.58%, 13.61%, 12.74%, and 3.57% in spring, summer, autumn, and winter, respectively. In addition, the FEL mode is more economical in spring and winter, and the FTL mode is more economical in summer and winter. In addition, the C O 2 emissions in FEL mode are always less than in FTL mode. [ABSTRACT FROM AUTHOR]

Economic, technological, and environmental causes are moving energy toward smart distribution networks. The change from fossil fuels to renewable energy sources is extremely environmentally friendly in both the building and transport sectors. Microgrids could be efficiently used in remote areas utilizing renewable energy resources for supplying different energy demands namely power, heat and cold. This paper presents a mixed-integer linear programming (MILP) model for optimizing planning and sizing decisions in microgrids connected to main grid. Planning decisions the amount of generation of each distributed generation (DG) technology and the amount of power transmission to other nodes. Also, sizing of different DGs including photovoltaic and combined heat and power facilities are performed by the proposed model. According to the results, the model is able to optimize sizing and planning decisions in grid-connected microgrids. [ABSTRACT FROM AUTHOR]

This paper examines the issue of strategic planning of fuel supplies in combined heat and power systems. This is a major challenge in energy modeling because heating-degree day calculation methods only address short-term horizons and are not suitable for the long-term planning of fuel supplies. In this work, a comprehensive method is proposed for strategic fuel supply planning of independent heat producers. The method considers changes in the market dynamics of residential and commercial properties, the annual rate of customer acquisition by the network operator, customer disconnections, as well as the thermal modernization of buildings for estimating the long-term thermal energy demand of an urban area. Moreover, the method develops a mathematical model to minimize production costs, taking into account the technical constraints of the system. The proposed strategic planning tool, in addition to information on the quantities of fuel consumed for heat and electricity production, also provides valuable management information on the operational costs of the CHP system and its environmental impact. The application of the method is illustrated with the analysis of a large-scale combined heat and power plant supplying heat and electricity to a city with over 500,000 inhabitants. The results indicate that depending on the changes in the primary and secondary heat markets, the demand for energy carriers may range from 107.37 TWh to 119.87 TWh. [ABSTRACT FROM AUTHOR]

Grinding plays an important role in mining, construction, metallurgy, chemical, coal and other basic industries. In terms of beneficiation, grinding is the most energy consuming operation. So, reasonable grinding conditions according to the properties of ores is the key to obtain good grinding results and reduce energy consumption and resource waste. In this paper, Tongkeng and Gaofeng polymetallic complex ores are taken as research objects, and the effects of grinding law based on single factor condition test and the grinding parameters optimization based on response surface method were studied for two kinds of ores. The results show that grinding time is a significant factor affecting the particle size composition. The suitable grinding concentration of Tongkeng ore and Gaofeng ore is 70% and 75%, respectively. The effect of mill filling ratio on Gaofeng ore is not obvious. The rotational rate has little effect on the grinding technical efficiency. The regression model equations obtained by response surface method are extremely significant, and the relative errors of prediction are all within 1%, indicating high reliability of fitting equations. The order of influencing factors of the two ores is as follows: grinding time > filling ratio > grinding concentration. For Tongkeng ore, the optimized grinding conditions are grinding time 5.4 min, grinding concentration 67% and filling ratio 35%. For Gaofeng ore, the optimized grinding conditions are grinding time 3.8 min, grinding concentration 73% and filling ratio 34%. [ABSTRACT FROM AUTHOR]

The aim of this paper is to analyze through simulation how the energy efficiency of a single electrolysis stack at various loads is affected by shunt currents and to determine the energy-optimal load distribution between lines in a multiline electrolysis system under various magnitudes of shunt current losses. A dynamic energy and mass balance model of an industrial 3 MW , 16 bar alkaline water electrolyzer (AWE) process was developed using MATLAB. The optimization goal is to determine the power supply for each AWE line so that it can meet any hydrogen demand while minimizing the global specific energy consumption (SEC). The Particle Swarm Optimization (PSO) algorithm is used to minimize the objective function. According to the results of the single stack investigation, shunt current reduction could significantly improve the energy efficiency of partial-load operation. In addition, the optimization study revealed that whenever two or more lines are required to run in order to satisfy the hydrogen demand, the global SEC is minimized when the lines operate at equal loads. [ABSTRACT FROM AUTHOR]

Using renewable energy sources instead of fossil fuels is one of the best solutions to overcome greenhouse gas (GHG) emissions. However, in designing clean power generation microgrids, the economic aspects of using renewable energy technologies should be considered. Furthermore, due to the unpredictable nature of renewable energy sources, the reliability of renewable energy microgrids should also be evaluated. Optimized hybrid microgrids based on wind and solar energy can provide cost-effective power generation systems with high reliability. These microgrids can meet the power demands of the consuming units, especially in remote areas. Various techniques have been used to optimize the size of power generation systems based on renewable energy to improve efficiency, maintain reliability, improve the power grid's resilience, and reduce system costs. Each of these techniques has shown its advantages and disadvantages in optimizing the size of hybrid renewable energy systems. To increase the share of renewable energies in electricity supply in the future and develop these new technologies further, this paper reviews the latest and most efficient techniques used to optimize green microgrids from an economical and reliable perspective to achieve a clean, economical, and highly reliable microgrid. [ABSTRACT FROM AUTHOR]

Summary: The integration of demand side management (DSM) with smart grid (SG) can facilitate residents' transfer into smart homes and sustainable cities by reducing the carbon emission. This manuscript reviews the recent works related to the application of DSM in SG through discussing the techniques and algorithms and their associated challenges for effective implementation. This paper also critically discusses the operation mode of DSM, the profile of energy production, storage and consumption, and finally the benefit obtained by the DSM implementation. Previous literature suggested that DSM practice reduced peak‐to‐average ratio, energy cost and carbon emission by approximately 10% to 65%, 5% to 50%, and 14%, respectively. The implementation of DSM in SG deals with a number of challenges such as security and privacy, tariff regulation, energy transmission, distribution, and effective utilization of energy resources. A number of international organizations have taken various measures and solutions to guarantee the security and privacy of the DSM in SG discussed. So far, a number of algorithms have been used as optimization approach to solve the DSM optimization problems; however hybrid algorithms have showed better performance than single algorithms due to their faster convergence speed. At the end, the paper presents the research gaps and future research directions. [ABSTRACT FROM AUTHOR]

In this paper, we propose a novel resource management scheme that jointly allocates the transmit power and computational resources in a centralized radio access network architecture. The network comprises a set of computing nodes to which the requested tasks of different users are offloaded. The optimization problem minimizes the energy consumption of task offloading while takes the end-to-end-latency, i.e., the transmission, execution, and propagation latencies of each task, into account. We aim to allocate the transmit power and computational resources such that the maximum acceptable latency of each task is satisfied. Since the optimization problem is non-convex, we divide it into two sub-problems, one for transmit power allocation and another for task placement and computational resource allocation. Transmit power is allocated via the convex-concave procedure. In addition, a heuristic algorithm is proposed to jointly manage computational resources and task placement. We also propose a feasibility analysis that finds a feasible subset of tasks. Furthermore, a disjoint method that separately allocates the transmit power and the computational resources is proposed as the baseline of comparison. A lower bound on the optimal solution of the optimization problem is also derived based on exhaustive search over task placement decisions and utilizing Karush–Kuhn–Tucker conditions. Simulation results show that the joint method outperforms the disjoint method in terms of acceptance ratio. Simulations also show that the optimality gap of the joint method is less than 5%. [ABSTRACT FROM AUTHOR]

Energy efficiency is a critical issue in the management of data centers, which form the backbone of cloud computing. Virtual resource management has a significant impact on improving the energy efficiency of data centers. Despite the progress in this area, virtual resource management has been considered mainly at two separate levels: application assignment and virtual machine placement. It has not been well-investigated in a unified framework for both levels, limiting further improvement in the energy efficiency of data centers. To address this issue, this paper proposes the virtual resource management problem for energy efficiency as a constrained optimization problem. Then, this paper simplifies the problem through profile-guided task classification and problem decomposition for complexity reduction and improved energy efficiency. After that, a three-phase framework and algorithms are presented for profiling and profile updating, task classification and application assignment, and successive virtual machine placement. Experimental studies show energy savings of 8–12% by the three-phase framework compared to the existing technique. [ABSTRACT FROM AUTHOR]

• Combined cycle capable of offering three switchable energy supply modes was studied. • The set of the evaporator ensures the effective switching of different cycle modes. • Heat source in low temperature grade helps to desorb the work or basic solution. • Analysis was conducted from both thermodynamic and economic perspectives. • All cycle modes can maintain high comprehensive performance. The emergence of energy-related issues and the growing need for year-round energy supply has created a pressing demand for efficient cogeneration combined cycles. To this end, a new combined cycle using ammonia-water as the working fluid is proposed and studied from both thermodynamic and economic standpoints in this paper. The proposed cycle can effectively operate in power generation, power/refrigeration cogeneration and power/heating cogeneration modes. Parametric analysis and performance optimization of the proposed cycle are carried out, and the genetic algorithm method is employed for the optimization. Under the same initial conditions, the proposed cycle exhibits comprehensive power recovery efficiencies that are 58.11 %, 23.85 %, and 7 % relatively higher than those of the compared cogeneration cycles and Kalina cycle reported in the literature, respectively. The optimization results show that the maximum comprehensive power recovery efficiencies for the power/refrigeration, power/heating and power modes with heat source/cooling water temperature of 250℃/30℃, are 0.2271, 0.2658, and 0.1789, respectively. The associated power/refrigeration, power/heating, and power outputs are 221.1 kW/417.8 kW, 248.5 kW/650.9 kW, and 292.1 kW, respectively. Under these conditions, the overall annual unit cost of comprehensive power and the payback period of the cycle are 0.03947 $/kWh and 3.898 years. Results of the comprehensive analysis indicate that the proposed cycle can achieve satisfactory comprehensive performance for each mode, while also sufficiently recovering sensible heat source. [ABSTRACT FROM AUTHOR]

Edge infrastructures, including edge computing servers, are increasingly powered by renewable energy and smart grid combined. Two-way energy trading allows the surplus or shortfall of renewable energy to be traded between a server and the smart grid, but is non-trivial due to randomly varying computation demands and renewables. This paper proposes a new online policy, namely, distributed online resource allocation and load management (DORL), which enables such an edge server and its serving devices to minimize their energy cost and energy consumption, respectively, in a fully distributed manner. The key idea is that we employ the stochastic dual-subgradient method to interpret the battery of the server as a virtual queue. Based on the virtual queue and task queues, the CPU frequencies of the devices and the edge server, the offloading transmit rates of the devices (to the server) and the energy trading decisions of the server (with the smart grid) are decoupled over time and among devices, and optimized on an ongoing basis. Furthermore, we prove that the DORL yields a feasible and asymptotically optimal solution with a cost-backlog tradeoff of $[\eta, 1/\eta]$. Simulations show that the DORL reduces the system cost by nearly 50%, as compared to existing benchmarks. [ABSTRACT FROM AUTHOR]

This paper investigates the cyber resiliency of future power systems with high penetration of distributed energy resources using advanced distributed and (or) hierarchical control architectures. Specifically, we simulate cyberattacks on three prototypical use cases, and we identify attack scenarios that are the most damaging to the overall system performance. We show that these attacks can have a significant impact on grid operation. Results provide additional insights into the robustness of the system to the most common cyberattacks. • This paper delves into the realm of cyber-resiliency, examining its implications across three distinct use cases encompassing distributed, distributed-hierarchical, and hierarchical control architectures. • Through exhaustive numerical simulations, we discern and highlight the precise signals within the system that exhibit heightened susceptibility to cybersecurity attacks. • The results obtained from our analysis offer valuable insights into the robustness of these systems, shedding light on their ability to withstand potential cybersecurity threats. [ABSTRACT FROM AUTHOR]

Microgrids are one of the best solutions to present energy crisis, where the expansions of distributed networks are not feasible. This is achieved with penetration of renewable energy sources along with suitable selection of Energy Storage Systems (ESS).The load demand can be supported with microgrid in the grid connected mode or autonomous mode by suitable sizing of DGs and BESS. Sizing of suitable ESS in terms of its capacity or discharge helps in shaving the peak demand and also maximizes the benefits related to their use. Various advantages to end-users can be provided by microgrid with ESS, like improvement in energy efficiency, minimizing overall energy consumption through distributed generations (DGs) and increasing reliability of power supply to the customers. All these advantages can be achieved by obtaining the optimal microgrid operating cost with ESS. One of the challenging tasks in Microgrids is obtaining optimal energy utilization of DGs to meet the load demand. This can be achieved through proper energy management in the microgrid by obtaining optimal power allocations of DGs and ESS. This paper presents an algorithm for microgrid operating cost minimization for 24 hrs time horizon with optimal power allocation of DGs considering randomness in DGs, with and without BESS. Accelerated particle swarm optimization (APSO), Jaya optimization (JAYA) technique and Linear programming based interior point algorithm (LP-IP) have been applied to minimize the operating cost of microgrid with optimal Energy management while selecting proper size of BESS. Simulation results obtained by PSO, APSO, JAYA and LP-IP solver based algorithms are compared for BESS size and microgrid operating cost for 24 hrs. Results of LP-IP solver based optimization algorithm are proved to be the most effective of all other algorithms in terms of cost reduction. Optimal sizing of BESS with energy management of DGs and BESS reduces the operating cost of microgrid meeting the load criteria. [ABSTRACT FROM AUTHOR]

Terahertz (THz) frequency band has become one of the popular researches for the short-range broadband, high-rate data communication, reliability, and strong anti-interference ability, which can realize better performance for dense vehicular network scenario. In this paper, we introduce a cache based dense vehicular networks with THz frequency link and focus on the resource allocation optimization problem of subband and power allocation by maximizing energy efficiency (EE). In order to reduce the computational complexity, the original optimization problem is formulated as a mean-field game (MFG) problem, where each cache access point (cAP) can solve its local EE optimization problem by Dinkelbach-style method and Lagrangian dual method. Different access cells interact with MFG through mean subband power. Then a global MFG based EE optimization algorithm is proposed to associate the local strategy of each cell, which guarantee the globality of the optimized solutions. The numerical results suggest the effectiveness of the proposed scheme. [ABSTRACT FROM AUTHOR]

Remarkable resource management and energy efficiency improvements can be achieved in greenhouses using innovative technological advancements and modern agricultural methods. Deployment of Internet of Things (IoT) and optimization algorithms in greenhouse farming is highly desirable for real-time monitoring and controlling various parameters with optimal solutions. However, IoT based greenhouses require more energy as compared to traditional farming. This paper proposes an optimal greenhouse water supplement mechanism with efficient energy consumption based on IoT and optimization techniques. The first contribution of this study is to gather the actual water and soil moisture levels from the greenhouse and tank using IoT devices. Secondly, the formulation and deployment of an objective function to compute the optimal water and soil moisture levels for greenhouse and tank based on user-desired settings, the system constraints and actual sensing values. We applied a rule-based expert system to activate water pumps with the required flow rate and operational duration to achieve efficient energy consumption. To prove the effectiveness of the proposed concept, embedded IoT devices and objective function for optimization are deployed as well as, a number of experiments are conducted to provide the optimal water and soil moisture levels in a real greenhouse and water tank environment. [ABSTRACT FROM AUTHOR]

Based on the Petri net (PN) models of the flexible manufacturing systems (FMSs), this paper focuses on solving the scheduling problem of minimizing the total energy consumption of FMSs. In the view of different energy consumption rates of resources under different working statuses, two energy consumption functions are considered. The dynamic programming (DP) models of the scheduling problems based on PNs are established, where a reachable marking of a PN model, start processing time vector, route vector, and the transition sequence leading to the reachable marking from the initial one, is regarded as a state, and the Bellman equation is based on transition firing. For small-size scheduling problems, their optimal solutions can be obtained by the presented DP algorithm. However, it is difficult to solve larger-scale ones since the number of explored states increases exponentially with the problem size, which makes DP computationally infeasible. To obtain an optimal or suboptimal schedule in an acceptable time, modified DP (MDP) algorithm is proposed, in which fewer states are explored. In MDP, two ways are introduced through which only the most promising states are explored. One is keeping only one transition sequence for each marking through an evaluation function. Another is selecting the most promising states in each stage for further exploration through a heuristic function. To guarantee that the generated states are safe, a deadlock controller is applied in the recursion procedure of MDP. Experimental results on manufacturing systems and comparisons with existing works are provided to show the effectiveness of MDP. Note to Practitioners—This paper is motivated by the need of optimizing the energy consumption of manufacturing systems, considering the increasing energy cost and environmental concerns. Existing studies on energy optimization rarely focus on flexible manufacturing systems (FMSs) which exhibit a high degree of resource sharing and route flexibility and can be highly adaptable to various production plans and goals. Scheduling becomes more challenging when facing deadlock-prone FMSs. This paper provides a method to solve this complex scheduling problem efficiently. In this paper, dynamic programming (DP) is formulated for it, and the optimal energy consumption schedules are found. Considering the computational burden of implementing DP in the scheduling of large-size FMSs, a novel scheduling method named modified DP (MDP) is proposed. Experimental tests on an FMS and a stamping system suggest that MDP can successfully find feasible solutions. Besides, it can be applied to other FMS scheduling problems and industrial cases, once their processing time of operations and energy consumption of resources per unit time are known. [ABSTRACT FROM AUTHOR]

Terahertz (THz) band has attracted considerable interest recently due to its superior high frequency and large available bandwidth. THz could act a vital part in the sixth generation (6G) mobile communication networks. In this paper, we introduce the downlink non-orthogonal multiple access (NOMA) technology into THz band small cell networks, where the total performance is optimized considering the two key enabling technologies. In order to decrease the energy consumption triggered by increasing of wireless services, we pay great attention to energy efficiency (EE) optimization and resource allocation in the THz-NOMA downlink systems by solving the subchannel assignment and power optimization. We first exploit a channel model for the THz-NOMA downlink system by using the key features of THz-NOMA networks. Then we utilize Dinkelbach-style algorithm to solve the resource allocation problem and decompose it into two subproblems. A subchannel assignment algorithm and a power optimization based on alternative direction method of multipliers (ADMM) algorithm are developed to get the solution. Finally, to embody the strengths of THz-NOMA performance, we compare our proposed schemes against the conventional schemes. Simulation results yield substantially higher EE and further prove the availability of our proposed schemes. [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]

Cloud providers offer computing services based on user demands using the Infrastructure as a Service (IaaS) service model. In a cloud data center, it is possible that multiple Virtual Machines (VMs) run on a Physical Machine (PM) using virtualization technology. Virtual Machine Placement (VMP) problem is the mapping of virtual machines across multiple physical ones. This process plays a vital role in defining energy consumption and resource usage efficiency in the cloud data center infrastructure. However, providing an efficient solution is not trivial due to difficulties such as machine heterogeneity, multi-dimensional resources, and large scale cloud data centers. In this paper, we propose an efficient heuristic algorithm that focuses on power consumption and resource wastage optimization to solve the aforementioned problem. The proposed algorithm, called MinPR, minimizes the total power consumption by reducing the number of active physical machines and prioritizing the power-efficient ones. Also, it reduces resource wastage by maximizing and balancing resource utilization among physical machines. To achieve these goals, we propose a new Resource Usage Factor model that manages virtual machine placement on physical machines using reward and penalty mechanisms. Simulations based on cloud user-customized VMs and Amazon EC2 Instances workloads illustrate that the proposed algorithm outperforms existing approaches. In particular, the proposed algorithm reduces total energy consumption by up to 15% for cloud user-customized VMs and by up to 10% for Amazon EC2 Instances. [ABSTRACT FROM AUTHOR]

In this paper, we design a computing, communication and caching scheme for edge caching-based video streaming in order to improve the network performance. Firstly, we optimize the system's energy efficiency and delay with the aid of network function virtualization. Then, a dynamic edge caching decision is developed, and based on Lyapunov optimization, an alternating resource optimization algorithm is proposed for allocating the optimal subcarrier and power resources, video caching and computing resources. Our numerical results show that the proposed scheme outperforms both the traditional caching scheme as well as the least frequently used (LFU)-40% regime, and strikes a compelling tradeoff between the energy efficiency and delay. [ABSTRACT FROM AUTHOR]

This paper proposes a dynamic input–output model, in order to present the interaction mechanism between energy supply and demand, and provide optimal design for energy supply–consumption system. Combined with mixed integer programming, the model is able to provide optimal technology development route for the energy supply–consumption system. In this model, the generational change and replacement of energy supply facilities are considered. Based on the model, a case study of China is employed. Three scenarios are designed to compare the different outcome of the model. The optimal objectives of Scenario 1 are Pareto Optimality of minimum total energy consumption and minimum total CO 2 emission. The optimal objective of Scenario 2 is minimum CO 2 emission. The optimal objective of Scenario 3 is minimum energy consumption. Scenario 1 leads to a balancing design of multiple objectives, and the results indicate that energy conservation and CO 2 emission reduction targets can be achieved simultaneously. Compared with the optimal design with single objective of Scenarios 2 and 3, the balancing design with multiple objectives performs better in avoiding over-capacity problems during the modeling period. With well-designed development route, energy intensity can be reduced by about 58%–60%, and CO 2 emission intensity can be reduced by about 34%–36% from 2012 to 2040. The optimal objectives of the model also influence the development of energy demand structure. The modeling results of transport sector are presented as an example in this paper. To achieve the targets of energy conservation and CO 2 emission reduction, both the development routes for energy supply and demand should be well-designed. [ABSTRACT FROM AUTHOR]

Energy Harvesting (EH) technology enables Device-to-Device (D2D) User Equipments (DUEs) to harvest energy from ambient energy, making contributions to green communications and breaking the single battery-powered and the regional limitations of device deployment. The joint problem of energy harvesting and resource allocation in EH-based D2D Communication Networks (EH-DCNs) is a challenge issue. In this paper, we investigate the joint problem of resource allocation and EH time slot allocation of DUEs in EH-DCNs, where the DUEs harvest energy and multiplex Cellular User Equipments (CUEs) uplink resources. A channel assignment, power allocation and EH time slot allocation problem in EH-DCNs is formulated. The goal is to maximize energy- and spectral-efficiency with α-fairness while guaranteeing the EH constraints of DUEs and the quality of service of CUEs. The formulated problem is a non-convex mixed-integer multi-objective optimization problem. In order to solve the formulated problem, the multi-objective optimization problem is transformed into a single-objective optimization problem based on the weight sum method. We propose a joint iterative algorithm based on Lagrangian dual decomposition for α > 0 and α = 0, respectively. Numerical results illustrate that the proposed algorithm achieves higher energy efficiency and spectral efficiency for different network parameter settings. [ABSTRACT FROM AUTHOR]

The microgrid vision has come to incorporate various communication technologies, which facilitate residential users to adopt different scheduling schemes in order to manage energy usage with reduced carbon emission. Through this study, we have introduced a novel method for residential load control with energy resources integrated. To this end, an input and optimization algorithm has been employed to control and schedule residential charges for cost savings, consumer inconvenience, and peak-to-average rate savings (PAR) purposes, including real-time electricity costs, energy demand, user expectations, and renewable energy parameters. This paper also provides a Maximum Power Point Tracking (MPPT) technique used to obtain full power from a hybrid power system during the variation of environmental conditions in both photovoltaic stations and batteries. An IEEE 14 bus program was considered to determine the efficiency of the proposed algorithm. This research also aims at developing the role model to determine the behaviors, as a result of a shift in the opening Protocol to the disconnect establishing the power generation island, of distributed energy resources on 14-node IEEE networks. The micro-grid is a simple case for the study of energy flow and smart grid efficiency variables and has dispersed tools. The findings show that the energy management system load collection using the suggested approach improves performance and decreases losses in contrast to previous approaches. [ABSTRACT FROM AUTHOR]

Distributed satellite cluster network is a novel structure in spatial information network. This paper investigates the spectrum-power tradeoff algorithm for distributed satellite cluster (DSC), which enables the master satellite (MS) to offload data traffic to the slave satellite (SS). The SS provides power resource to the master satellite users (MSUs) with bandwidth as compensation. The purpose of the proposed algorithm is to maximize the energy efficiency (EE) of the SS while ensuring the QoS of the DSC. Considering data rate and bandwidth constraints of the MS, the SS jointly allocates bandwidth and power resources based on the served MSU set, meanwhile, data rate and power constraints of the SS should be also satisfied. We first prove that compensating bandwidth of a MSU can only be used by one slave satellite user. Then objective function is simplified to the equivalent subtractive form that can be solved by convex optimization method. Besides, we discuss the MSU selection conditions and employ them to the proposed spectrum-power tradeoff algorithm. The numerical results validate the performance improvement of the proposed algorithm and demonstrate the impact of power, bandwidth and data rate on the EE. [ABSTRACT FROM AUTHOR]

Vehicular Ad-hoc Networks (VANETs) have unique capabilities and are associated with Vehicle-to-Vehicle (V2V) and Vehicle-to-Infrastructure (V2I) communications to reduce traffic dynamics and provide support to the providers and the drivers to rely on security and communication alerts. Our proposed method is only supported by V2V and V2I communications and neighbourhood localization records, which are summarized in an adaptive adjustment of the forwarded information related to the messages(data communication). We optimize these message information, and only the relevant information is transmitted to the relevant vehicles, and all other unnecessary information is terminated. Because, irrelevant information causes many problems, like unnecessary resource utilization and energy consumption. In this case, the driver will be promptly reminded to take appropriate measures. A swarm intelligence algorithm such as the Artificial Bee Colony (ABC) is utilized to optimize the communication mechanism. In this paper, we present a high-level framework for ABC based on the fuzzy logic for the VANETs. We prove the validity and efficiency of the proposed method. Our proposed method achieves higher optimization accuracy in information broadcasting with minimum delay and with the lowest energy consumption. [ABSTRACT FROM AUTHOR]

The optimization of train trajectory and timetable offers two effective methods of energy-efficient operations. Most traditional research optimizes them separately and thus the global optimality cannot be achieved. Previous work has mainly concentrated on minimizing the mechanical energy consumption of trains, not the electrical energy. This paper aims to develop an integrated model to optimize the integrated timetable, which includes both the timetable and the train trajectory achieve the minimum electrical energy consumption. The AC power supply system model is first proposed to calculate the parameters of the AC power supply network. Then, a model is developed to calculate various energy consumptions using power flow calculation. Furthermore, an integrated timetable optimization model for minimum total energy consumption of AC railway system is proposed and the optimal integrated timetable is obtained by the proposed hybrid GA-PSO algorithm. Finally, 5 real-world case studies based on the Lanzhou–Xinjiang high-speed railway are presented. The results show that the proposed integrated model can achieve a reduction in total energy consumption for the entire line up to 14.3% compared with the previous optimization model. The hybrid GA-PSO algorithm achieves the best results compared with the results achieved by the GA and PSO algorithms applied along. [ABSTRACT FROM AUTHOR]

Recently, the concept of network virtualization and network slicing attracted significant attention from both industry and academia as a key component of the evolving 5G architecture to allow the efficient entrance of vertical industries and tackle increased aggregate traffic by flexible network re-configurability. However, the potential price to be paid for facilitating network slicing in a multi-tenant virtual network is the underutilization of the scarce wireless network resources due to the different tenant requirements and the inherent dynamics of the traffic. A potential way to avoid such sacrifice of radio resources is to allow efficient inter-tenant resource sharing. To this end, this paper proposes a novel optimization framework for flexible inter-tenant resource sharing embedded with transmission power control to aggressively improve network capacity, the utilization of wireless access resources, user data rate as well as energy efficiency. More specifically, we define two novel resource sharing mechanisms called tight coupling and loose coupling, respectively, via mixed integer linear programming formulations. Furthermore, two resource and power joint allocation algorithms are designed to solve the optimization problem in polynomial time. Based on 3GPP network parameterization, a rigorous analysis via a wide set of numerical investigations reveal that significant gains in network throughput, individual user rate, and energy efficiency can be achieved compared with current baseline network slicing methods and constant power resource sharing algorithms. [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]

Smart grids provide great opportunities for design and implementation of sustainable and efficient electricity supply chains that are more resilient to disruptions. The problem with electricity supply chain network design (ESCND) using smart grid has not been broadly explored by researchers. This paper aims to approach this problem using a multiobjective robust optimization method. The three objectives are economic (profit maximization), environmental (greenhouse gas emissions minimization), and resilience (network resilience maximization). Efficiency maximization—power loss minimization—has also been taken into account using its corresponding cost element in the economic objective function. The proposed approach accounts for different unique smart grid components such as demand side management programs, microgrid structure, two-way distribution lines, and supplier–consumer nodes, while incorporating different interrelated decisions including facility location, capacity expansion, load allocation, and pricing. To solve the resultant model, a hybrid multiobjective robust optimization technique is proposed based on cutting plane and AUGMECON2 algorithms. The proposed model and solution approach are then applied to an actual case study and the produced results are thoroughly analyzed. We find that while economic and environmental objectives can be strictly conflicting, the implementation of smart grids can result in concurrent boost in both environmental performance and network resilience. [ABSTRACT FROM AUTHOR]

Traditionally viewed as mere energy consumers, buildings have adapted, capitalizing on smart grid technologies and distributed energy resources to efficiently use and trade energy, as evident in net-zero energy buildings (NZEBs). In this paper, we examine the opportunities presented by applying net-zero to building communities (clusters). This paper makes two main contributions: one, it presents a framework for generating Pareto optimal operational strategies for building clusters; two, it examines the energy tradeoffs resulting from adaptive decisions in response to dynamic operation conditions. Using a building cluster simulator, the proposed approach is shown to adaptively and significantly reduce total energy cost. [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]

Unmanned aerial vehicles (UAVs) have been recently considered as means to provide enhanced coverage or relaying services to mobile users (MUs) in wireless systems with limited or no infrastructure. In this paper, a UAV-based mobile cloud computing system is studied in which a moving UAV is endowed with computing capabilities to offer computation offloading opportunities to MUs with limited local processing capabilities. The system aims at minimizing the total mobile energy consumption while satisfying quality of service requirements of the offloaded mobile application. Offloading is enabled by uplink and downlink communications between the mobile devices and the UAV, which take place by means of frequency division duplex via orthogonal or nonorthogonal multiple access schemes. The problem of jointly optimizing the bit allocation for uplink and downlink communications as well as for computing at the UAV, along with the cloudlet's trajectory under latency and UAV's energy budget constraints is formulated and addressed by leveraging successive convex approximation strategies. Numerical results demonstrate the significant energy savings that can be accrued by means of the proposed joint optimization of bit allocation and cloudlet's trajectory as compared to local mobile execution as well as to partial optimization approaches that design only the bit allocation or the cloudlet's trajectory. [ABSTRACT FROM AUTHOR]

In this paper, we focus on the low-power scheduling problem with multiple threshold and/or supply voltage technologies in high-level synthesis. We propose a unified scheduling approach which is applicable to various optimization problems, including: 1) dynamic power and resource usage co-optimization; 2) leakage power optimization; and 3) dynamic power and leakage power co-optimization. To deal with different objectives with high flexibility, three problems are divided into two common subproblems including delay assignment and resource density variance minimization, then a vertex potential-based mobility allocation model is proposed to solve two subproblems simultaneously. Experimental results show that, for dynamic power and resource co-optimization, our scheduling approach produces optimum solutions for all six benchmarks with 15 groups of data; for leakage power optimization it also greatly excels the latest existing work, by 20% leakage power reduction and 52 times speedup. Besides, for dynamic and leakage power co-optimization, the Pareto solutions are studied. [ABSTRACT FROM PUBLISHER]

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]

High energy efficiency for low operating cost of desalination can be obtained from operational optimizations as well as component-wise innovations. This paper is concerned with an energy-efficient operation for reverse osmosis (RO) desalination process involving a joint control of multiple modules. Energy resource considered for desalination is of two types: intermittent solar energy and steady grid energy. With solar energy, the energy efficiency is measured by total permeate production obtained while the solar energy is available. With grid energy, the energy efficiency is measured by the power consumed to get unit permeate production rate. Achieving maximum energy efficiency is equivalent to solving constrained optimization. For the constrained optimization, obligatory constraints are given by the permissible ranges of trans-membrane pressure on RO membrane and total dissolved solids of permeate, and optional constraint is given by the permissible range of permeate production rate. Constraints as well as the objective function are modeled by the empirical second order equations in terms of control variables of multiple modules. Jointly optimal values of the control variables are found by the sequential quadratic programming. Experimental results obtained by the jointly optimal control demonstrate superior performances as compared to those acquired by marginally optimal control schemes. [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]

Abstract: A mobile grid incorporates mobile devices into Grid systems. But mobile devices at present have severe limitations in terms of processing, memory capabilities and energy. Minimizing the energy usage in mobile devices poses significant challenges in mobile grids. This paper presents energy constrained resource allocation optimization for mobile grids. The goal of the paper is not only to reduce energy consumption, but also to improve the application utility in a mobile grid environment with a limited energy charge, ensuring battery lifetime and the deadlines of the grid applications. The application utility not only depends on its allocated resources including computation and communication resources, but also on the consumed energy, this leads to a coupled utility model, where the utilities are functions of allocated resources and consumed energy. Energy constrained resources allocation optimization is formulated as a utility optimization problem, which can be decomposed into two subproblems, the interaction between the two sub-problems is controlled through the use of a pricing variable. The paper proposes a price-based distributed energy constrained resources allocation optimization algorithm. In the simulation, the performance evaluation of our energy constrained resources allocation optimization algorithm is conducted. [Copyright &y& Elsevier]

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]

Increasing attention has recently been drawn to energy consumption in manufacturing plants. Facing the challenges from reducing emissions coupled with rising raw material prices and energy costs, manufacturers are trying to balance the energy usage strategy among the total energy consumption, economy, and environment, which can be self-conflicting at times. In this paper, energy systems in manufacturing environments are reviewed, and the current status of onsite energy system and renewable energy usage are discussed. Single objective and multicriteria optimization approaches are effectively formulated for making the best use of energy delivered to the production processes. Energy supply operation suggestions based on the optimization results are obtained. Finally, an example from an automotive assembly manufacturer is described to demonstrate the energy usage in the current manufacturing plants and how the optimization approaches can be applied to satisfy the energy management objectives. According to the optimization results, in an energy oriented operation, it takes 35% more in monetary cost; while in an economy oriented operation, it takes 17% more in megawatt hour energy supply and tends to rely more on the inexpensive renewable energy. [ABSTRACT FROM AUTHOR]

Increasing attention has recently been drawn to energy consumption in manufacturing plants. Facing the challenges from reducing emissions coupled with rising raw material prices and energy costs, manufacturers are trying to balance the energy usage strategy among the total energy consumption, economy, and environment, which can be self-conflicting at times. In this paper, energy systems in manufacturing environments are reviewed, and the current status of onsite energy system and renewable energy usage are discussed. Single objective and multicriteria optimization approaches are effectively formulated for making the best use of energy delivered to the production processes. Energy supply operation suggestions based on the optimization results are obtained. Finally, an example from an automotive assembly manufacturer is described to demonstrate the energy usage in the current manufacturing plants and how the optimization approaches can be applied to satisfy the energy management objectives. According to the optimization results, in an energy oriented operation, it takes 35% more in monetary cost; while in an economy oriented operation, it takes 17% more in megawatt hour energy supply and tends to rely more on the inexpensive renewable energy. [ABSTRACT FROM AUTHOR]

In this paper, a power management strategy (PMS) has been developed for the control of energy storage in a system subjected to loads of random duration. The PMS minimises the costs associated with the energy consumption of specific systems powered by a primary energy source and equipped with energy storage, under the assumption that the statistical distribution of load durations is known. By including the variability of the load in the cost function, it was possible to define the optimality criteria for the power flow of the storage. Numerical calculations have been performed obtaining the control strategies associated with the global minimum in energy costs, for a wide range of initial conditions of the system. The results of the calculations have been tested on a MATLAB/Simulink model of a rubber tyre gantry (RTG) crane equipped with a flywheel energy storage system (FESS) and subjected to a test cycle, which corresponds to the real operation of a crane in the Port of Felixstowe. The results of the model show increased energy savings and reduced peak power demand with respect to existing control strategies, indicating considerable potential savings for port operators in terms of energy and maintenance costs. [ABSTRACT FROM AUTHOR]

In this paper, a robust bi-level decision-making framework is presented for distributed generation (DG) owning retailers to supply the electricity to price-sensitive customers. Uncertainties about client demand and wholesale prices are the main difficulties faced by the electricity retailer. Clients can adjust their consumption according to the retailer's selling price. A higher selling price increases retailers' profit but decreases client consumption. Hence, the retailer faces a tradeoff between the price and sales. In the proposed model, the optimal selling price and the retailer's energy-supply strategy are modeled in the lower sub-problem. According to the proposed selling price, the optimal energy consumption of price-sensitive clients is determined in the upper sub-problem. To evaluate the financial risk arising from uncertain prices, the Information Gap Decision Theory (IGDT) approach is addressed in the lower sub-problem. Additionally, the risk-based optimization problem is formulated for risk-averse and risk-taker retailers via the robustness and opportunity functions, respectively. The robustness of the optimal solution against price variations is evaluated such that the associated profit will be more than the electricity retailer's acceptable threshold. The efficiency and performance of the decision-making framework are analyzed via a case study, and the numerical results are discussed. [ABSTRACT FROM AUTHOR]

The need for improved utilization of existing system assets and energy sources, as well as the smooth incorporation of new technologies (such as electric vehicles) into the grid, has prompted the participation of small power consumers and generators in the energy markets. A problem of such scale however cannot be managed in a centralized manner in its full detail. This paper examines the idea of a decentralized approach in clearing the energy market. A general framework for the problem decomposition and its distributed solution is presented and analyzed. A key point of interest in this work is the fundamental question of how far decomposition may be pursued for a given system, while still achieving reasonable convergence properties. The corresponding optimization problem is formulated and solved through a parallel implementation of the alternating direction method of multipliers (ADMM). A thorough investigation of its convergence properties is conducted, and through its coordination with an additional proximal based decomposition method, we improve its scalability characteristics. [ABSTRACT FROM AUTHOR]

The European and national regulations in the decarbonisation path towards 2050 promote district heating in achieving the goals of efficiency, energy sustainability, use of renewables, and reduction of fossil fuel use. Improved management and optimisation, use of RES, and waste heat/cold sources decrease the overall demand for primary energy, a condition that is further supported by building renovations and new construction of under (almost) zero energy buildings, with a foreseeable decrease in the temperature of domestic heating systems. Models for the simulation of efficient thermal networks were implemented and described in this paper, together with results from a real case study in Italy, i.e., University Campus of Parma. Activities include the creation and validation of calculation codes and specific models in the Modelica language (Dymola software), aimed at investigating stationary regimes and dynamic behaviour as well. An indirect heat exchange substation was coupled with a resistive-capacitive model, which describes the building behaviour and the thermal exchanges by the use of thermos-physical parameters. To optimise indoor comfort conditions and minimise consumption, dynamic simulations were carried out for different operating sets: modulating the supply temperature in the plant depending on external conditions (Scenario 4) decreases the supplied thermal energy (−2.34%) and heat losses (−8.91%), even if a lower temperature level results in higher electricity consumption for pumping (+12.96%), the total energy consumption is reduced by 1.41%. A simulation of the entire heating season was performed for the optimised scenario, combining benefits from turning off the supply in the case of no thermal demand (Scenario 3) and from the modulation of the supply temperature (Scenario 4), resulting in lower energy consumption (the thermal energy supplied by the power plant −3.54%, pumping +7.76%), operating costs (−2.40), and emissions (−3.02%). The energy balance ex-ante and ex-post deep renovation in a single user was then assessed, showing how lowering the network operating temperature at 55 °C decreases the supplied thermal energy (−22.38%) and heat losses (−22.11%) with a slightly higher pumping consumption (+3.28%), while maintaining good comfort conditions. These promising results are useful for evaluating the application of low-temperature operations to the existing district heating networks, especially for large interventions of building renovation, and confirm their potential contribution to the energy efficiency targets. [ABSTRACT FROM AUTHOR]

In the area of energy-efficient (EE) resource allocation, only limited work has been done on consideration of both transmitter and receiver energy consumption. In this paper, we propose a novel EE resource-allocation scheme for orthogonal frequency-division multiple-access (OFDMA) networks, where both transmitter energy consumption and receiver energy consumption are considered. In addition, different quality-of-service (QoS) requirements, including minimum-rate guarantee service and best effort service, are taken into account. The time slot, subcarrier (frequency), and power-allocation policies are jointly considered to optimize system EE. With all these considerations, the EE resource-allocation problem is formulated as a mixed combinatorial and nonconvex optimization problem, which is extremely difficult to solve. To reduce the computational complexity, we tackle this problem in three steps. First, for a given power allocation, we obtain the time–frequency resource unit (RU) allocation policy via binary quantum-behaved particle swarm optimization (BQPSO) algorithm. Second, under the assumption of known RU allocation, we transform the original optimization problem into an equivalent concave optimization problem and obtain the optimal power-allocation policy through the Lagrange dual approach. Third, an iteration algorithm is developed to obtain the joint time–frequency power–resource-allocation strategy. We validate the convergence and effectiveness of the proposed scheme by extensive simulations. [ABSTRACT FROM PUBLISHER]

European countries are promoting the use of renewable energy sources in order to reduce their dependence on imported energy. Renewable sources of energy are a promising solution, but there are downsides too. The integration of renewable energy sources causes problems with the electric power systems, as their energy production is meteorologically dependent. In this paper a system is introduced that represents a solution to this problem, with the activation of users of the distribution part of the electricity network. The system connects the users of the distribution network that are willing to participate in the system environment based on market rules. The participation allows users to offer an adaptation of their consumption of electric energy or production in return for financial incentives. The system accepts or rejects the user's energy adaptation offers on the basis of market principles that are driven by the optimization method (genetic algorithms in the presented case). The system optimizes the total cost amount that is reflected in the integration of a larger portion of the renewable energy sources into the electric energy system. Testing the results of the system operation demonstrates that the developed solution has improved the integration of renewable energy sources and has enabled users to profit from their adaptation to energy consumption or production. [ABSTRACT FROM AUTHOR]

As the energy demand keeps growing with the world's increasing population, concerns have arisen about the carbon emissions that contribute to critical environmental issues. These environmental concerns drive many nations to commit themselves in pursuing sustainable energy resources. Different process integration techniques have been developed to achieve a viable energy planning sector. In reference to the literature review, the use of time-sliced based models in optimization problems is still sparse. Hence, this paper aims to develop a time-sliced based optimization model that can be integrated into energy integration models. With that, a multi-period pinch-based methodology (targeting, scheduling, and optimization) for electric power systems with the hybridization of renewables and fossil-based energy is proposed. The prospective framework is designed to meet the emissions limit, fulfil energy demand, and minimize the electricity bill. An illustrative case study in Malaysia incorporated with an actual billing system is used to demonstrate the effectiveness of the proposed framework. The results show that it is capable of reducing the total electricity bill by 35% and achieving a 71% reduction of emissions by using solar PV as the renewables. • Three-step multi-period pinch-based methodology for energy planning is presented. • The model incorporates an actual billing system. • Time-sliced based optimization model is developed in the proposed framework. • Optimal emission reduction ratio and storage size are identified as 0.71 and 236 kWh. • The total electricity bill can be further reduced by 35%. [ABSTRACT FROM AUTHOR]

Apart from numerous technical challenges, the transition towards a carbon-neutral energy supply is greatly hindered by limited economic feasibility of renewable energy sources. This results in their slow and bounded penetration in both commercial and residential sectors that are responsible for over 40% of final energy consumption. This paper aims to demonstrate that combined application of sophisticated planning methodologies at building-level and presents incentive mechanisms for renewables that can result in prosumers, featuring hybrid renewable energy systems (HRES), with economic performance comparable to that of conventional energy systems. The presented research enhances existing planning methodologies by integrating appliance-level demand side management into the decision process and investigates its effect on the planning problem. Moreover, the proposed methodology features an innovative and holistic approach that simultaneously assess electrical and thermal domain in both an isolated and grid-connected context. The analyzed hybrid system consists of solar photovoltaic, wind turbine and battery with thermal supply featuring solar thermal collector and a ground-source heat pump. Overall optimization problem is modeled as a mixed-integer linear program, while ranking of all feasible alternatives is made by the multicriteria decision-making algorithm against several technological, economic, and environmental criteria. A real-life scenario of energy system retrofit for a building in the United Kingdom was employed to demonstrate overall cost savings of 12% in the present market and regulation context. [ABSTRACT FROM AUTHOR]