Dynamic optimal allocation of energy storage systems integrated within photovoltaic based on a dual timescale dynamics model

Energy storage systems(ESSs)operate as independent market participants and collaborate with photovoltaic(PV)generation units to enhance the flexible power supply capabilities of PV units.However,the dynamic variations in the profitability of ESSs in the electricity market are yet to be fully understood.This study introduces a dual-timescale dynamics model that integrates a spot market clearing(SMC)model into a system dynamics(SD)model to investigate the profit-aware capacity growth of ESSs and compares the profitability of independent energy storage systems(IESSs)with that of an ESS integrated within a PV(PV-ESS).Furthermore,this study aims to ascertain the optimal allocation of the PV-ESS.First,SD and SMC models were set up.Second,the SMC model simulated on an hourly timescale was incorporated into the SD model as a subsystem,a dual-timescale model was constructed.Finally,a development simulation and profitability analysis was conducted from 2022 to 2040 to reveal the dynamic optimal range of PV-ESS allocation.Additionally,negative electricity prices were considered during clearing processes.The simulation results revealed differences in profitability and capacity growth between IESS and PV-ESS,helping grid investors and policymakers to determine the boundaries of ESSs and dynamic optimal allocation of PV-ESSs.

River width and depth as key factors of diurnal activity energy expenditure allocation for wintering Spot-billed Ducks in the Xin'an River Basin

Rivers are important habitats for wintering waterbirds.However,they are easily influenced by natural and human activities.An important approach for waterbirds to adapt to habitats is adjusting the activity time and energy expenditure allocation of diurnal behavior.The compensatory foraging hypothesis predicts that increased energy expenditure leads to longer foraging time,which in turn increases food intake and helps maintain a constant energy balance.However,it is unclear whether human-disturbed habitats result in increased energy expenditure related to safety or foraging.In this study,the scan sample method was used to observe the diurnal behavior of the wintering Spot-billed Duck(Anas poecilorhyncha) in two rivers in the Xin’an River Basin from October 2021 to March 2022.The allocation of time and energy expenditure for activity in both normal and disturbed environments was calculated.The results showed that foraging accounted for the highest percentage of time and energy expenditure.Additionally,foraging decreased in the disturbed environment than that in the normal environment.Resting behavior showed the opposite trend,while other behaviors were similar in both environments.The total diurnal energy expenditure of ducks in the disturbed environment was greater than that in the normal environment,with decreased foraging and resting time percentage and increased behaviors related to immediate safety(swimming and alert) and comfort.These results oppose the compensatory foraging hypothesis in favor of increased security.The optimal diurnal energy expenditure model included river width and water depth,which had a positive relationship;an increase in either of these two factors resulted in an increase in energy expenditure.This study provides a better understanding of energy allocation strategies underlying the superficial time allocation of wintering waterbirds according to environmental conditions.Exploring these changes can help understand the maximum fitness of wintering waterbirds in response to nature and human influences.

Gini Coefficient-based Task Allocation for Multi-robot Systems With Limited Energy Resources

Nowadays, robots generally have a variety of capabilities, which often form a coalition replacing human to work in dangerous environment, such as rescue, exploration, etc. In these operating conditions, the energy supply of robots usually cannot be guaranteed. If the energy resources of some robots are consumed too fast, the number of the future tasks of the coalition will be affected. This paper will develop a novel task allocation method based on Gini coefficient to make full use of limited energy resources of multi-robot system to maximize the number of tasks. At the same time, considering resources consumption,we incorporate the market-based allocation mechanism into our Gini coefficient-based method and propose a hybrid method,which can flexibly optimize the task completion number and the resource consumption according to the application contexts.Experiments show that the multi-robot system with limited energy resources can accomplish more tasks by the proposed Gini coefficient-based method, and the hybrid method can be dynamically adaptive to changes of the work environment and realize the dual optimization goals.

Energy Efficient Power Allocation for Relay-Aided D2D Communications in 5G Networks

As one of the key technologies for the fifth generation(5G) wireless networks,device-to-device(D2D) communications allow user equipment(UE) in close proximity to communicate with each other directly.Forwarded by a relay,the relay-aided D2D(RA-D2D) communications can not only be applied to communications in much longer distance but also achieve a high quality of service(Qo S) .In this paper,we first propose a two-layer system model allowing RA-D2 D links to underlay traditional cellular uplinks.Then we maximize the energy efficiency of the RA-D2 D link while satisfying the minimum data-rate of the cellular link.The optimal transmit power at both D2 D transmitter and D2 D relay sides is obtained by transforming the nonlinear fractional programming into a nonlinear parameter programming.Simulation results show that our proposed power allocation method is more energy efficient than the existing works,and the proposed RA-D2 D scheme outperformed direct D2 D scheme when the distance between two D2 D users is longer.

Improving Energy Performance of Water Allocation Networks Through Appropriate Stream Merging

Water-using operations in the process industry have demands for water usually both on water quality and temperature, and the existing mathematical models of heat exchange networks cannot guarantee the energy performance of a water network optimal. In this paper, the effects of non-isothermal merging on energy performance of water allocation networks are analyzed, which include utility consumption, total heat exchange load, and number of heat exchange matches. Three principles are proposed to express the effects of non-isothermal merging on energy performance of water allocation networks. A rule of non-isothermal merging without increasing utility consumption is deduced. And an approach to improve energy performance of water allocation network is presented. A case study is given to demonstrate the method.

Power control and channel allocation optimization game algorithm with low energy consumption for wireless sensor network

In a wireless sensor network （WSN）, the energy of nodes is limited and cannot be charged. Hence, it is necessary to reduce energy consumption. Both the transmission power of nodes and the interference among nodes influence energy consumption. In this paper, we design a power control and channel allocation game model with low energy consumption （PCCAGM）. This model contains transmission power, node interference, and residual energy. Besides, the interaction between power and channel is considered. The Nash equilibrium has been proved to exist. Based on this model, a power control and channel allocation optimization algorithm with low energy consumption （PCCAA） is proposed. Theoretical analysis shows that PCCAA can converge to the Pareto Optimal. Simulation results demonstrate that this algorithm can reduce transmission power and interference effectively. Therefore, this algorithm can reduce energy consumption and prolong the network lifetime.

Energy Efficient Constrained Shortest Path First-Based Joint Resource Allocation and Route Selection for Multi-Hop CRNs

Cognitive radio networks(CRNs) are expected to improve spectrum utilization efficiently by allowing secondary users(SUs) to opportunistically access the licensed spectrum of primary users(PUs).In CRNs,source and destination SUs may achieve information interaction in an ad hoc manner.In the case that no direct transmission link between the SU transmission pairs is available,multi-hop relay SUs can be applied to forward information for the source and destination SUs,resulting in multi-hop CRNs.In this paper,we consider a multi-hop CRN consisting of multiple PUs,SU transmission pairs and relay SUs.Stressing the importance of transmission hops and the tradeoff between data rate and power consumption,we propose an energy efficient constrained shortest path first(CSPF)-based joint resource allocation and route selection algorithm,which consists of two sub-algorithms,i.e.,CSPF-based route selection sub-algorithm and energy efficient resource allocation sub-algorithm.More specifically,we first apply CSPF-based route selection sub-algorithm to obtain the shortest candidate routes(SCRs) between the SU pair under the transmission constraints.Then,an energy efficient resource allocation problem of the SCRs is formulated and solved by applying iterative algorithm and Lagrange dual method.Simu-lation results demonstrate the effectiveness of the proposed algorithm.

Energy-Efficient Resource Allocation for Heterogeneous Network with Grouping D2D

In this paper,a new communication model is built named grouping D2D(GD2D).Different from the traditional D2D coordination,we proposed GD2D communication in licensed and unlicensed spectrum simultaneously.We formulate a resource allocation problem,which aims at maximizing the energy efficiency(EE)of the system while guaranteeing the quality-of-service(Qos)of users.To efficiently solve this problem,the non-convex optimization problem is first transformed into a convex optimization problem.By transforming the fractional-form problem into an equivalent subtractive-form problem,an iterative power allocation algorithm is proposed to maximize the system EE.Moreover,the optimal closedform power allocation expressions are derived by the Lagrangian approach.Simulation results show that our algorithm achieves higher EE performance than the traditional D2D communication scheme.

Collaborative Multiple Access And Energy-Efficient Resource Allocation in Distributed Maritime Wireless Networks

With the rapid increasing of maritime activities, maritime wireless networks(MWNs) with high reliability, high energy efficiency, and low delay are required. However, the centralized networking with fixed resource scheduling is not suitable for MWNs due to the special environment. In this paper,we introduce the collaborative relay communication in distributed MWNs to improve the link reliability, and propose an orthogonal time-frequency resource block reservation based multiple access(RRMA) scheme for both one-hop direct link and two-hop collaborative relay link to reduce the interference. To further improve the network performance, we formulate an energy efficiency(EE) maximization resource allocation problem and solve it by an iterative algorithm based on the Dinkelbach method. Finally, numerical results are provided to investigate the proposed RRMA scheme and resource allocation algorithm, showing that the low outage probability and transmission delay can be attained by the proposed RRMA scheme. Moreover,the proposed resource allocation algorithm is capable of achieving high EE in distributed MWNs.

Energy-Efficient Power Allocation for IoT Devices in CR-NOMA Networks

Non-orthogonal multiple access is a promising technique to meet the harsh requirements for the internet of things devices in cognitive radio networks.To improve the energy efficiency(EE)of the unlicensed secondary users(SU),a power allocation(PA)algorithm with polynomial complexity is investigated.We first establish the feasible range of power consumption ratio using Karush-Kuhn-Tucker optimality conditions to support each SU’s minimum quality of service and the effectiveness of successive interference cancellation.Then,we formulate the EE optimization problem considering the total transmit power requirements which leads to a non-convex fractional programming problem.To efficiently solve the problem,we divide it into an inner-layer and outer-layer optimization sub-problems.The inner-layer optimization which is formulated to maximize the sub-carrier PA coefficients can be transformed into the difference of convex programming by using the first-order Taylor expansion.Based on the solution of the inner-layer optimization sub-problem,the concave-convex fractional programming problem of the outer-layer optimization sub-problem may be converted into the Lagrangian relaxation model employing the Dinkelbach algorithm.Simulation results demonstrate that the proposed algorithm has a faster convergence speed than the simulated annealing algorithm,while the average system EE loss is only less than 2%.

Three-Level Optimal Scheduling and Power Allocation Strategy for Power System ContainingWind-Storage Combined Unit

To mitigate the impact of wind power volatility on power system scheduling,this paper adopts the wind-storage combined unit to improve the dispatchability of wind energy.And a three-level optimal scheduling and power allocation strategy is proposed for the system containing the wind-storage combined unit.The strategy takes smoothing power output as themain objectives.The first level is the wind-storage joint scheduling,and the second and third levels carry out the unit combination optimization of thermal power and the power allocation of wind power cluster(WPC),respectively,according to the scheduling power of WPC and ESS obtained from the first level.This can ensure the stability,economy and environmental friendliness of the whole power system.Based on the roles of peak shaving-valley filling and fluctuation smoothing of the energy storage system(ESS),this paper decides the charging and discharging intervals of ESS,so that the energy storage and wind power output can be further coordinated.Considering the prediction error and the output uncertainty of wind power,the planned scheduling output of wind farms(WFs)is first optimized on a long timescale,and then the rolling correction optimization of the scheduling output of WFs is carried out on a short timescale.Finally,the effectiveness of the proposed optimal scheduling and power allocation strategy is verified through case analysis.

Energy Efficient Resource Allocation Approach for Renewable Energy Powered Heterogeneous Cellular Networks

In this paper,maximizing energy efficiency(EE)through radio resource allocation for renewable energy powered heterogeneous cellular networks(HetNet)with energy sharing,is investigated.Our goal is to maximize the network EE,conquer the instability of renewable energy sources and guarantee the fairness of users during allocating resources.We define the objective function as a sum weighted EE of all links in the HetNet.We formulate the resource allocation problem in terms of subcarrier assignment,power allocation and energy sharing,as a mixed combinatorial and non-convex optimization problem.We propose an energy efficient resource allocation scheme,including a centralized resource allocation algorithm for iterative subcarrier allocation and power allocation in which the power allocation problem is solved by analytically solving the Karush-Kuhn-Tucker(KKT)conditions of the problem and a water-filling problem thereafter and a low-complexity distributed resource allocation algorithm based on reinforcement learning(RL).Our numerical results show that both centralized and distributed algorithms converge with a few times of iterations.The numerical results also show that our proposed centralized and distributed resource allocation algorithms outperform the existing reference algorithms in terms of the network EE.

An Energy Sharing EH-DAS Model and its Power Allocation Among RAUs

For the energy sharing problem of distributed antenna system(DAS)with energy harvesting(EH),a distributed antenna system model capable of sharing collected energy among the components in system is proposed.Compared with the existing model in literatures,the proposed model connects with smart grid through a unified interface and facilitates energy management and scheduling.Based on the proposed model,three kinds of energy sharing methods including the partial energy sharing method,the complete energy sharing method and the self-sustaining energy sharing method are analyzed.Under various energy sharing methods,the corresponding optimization problems of power allocation among the remote antenna units(RAUs)are described,formed and solved.As a result,the corresponding power allocation algorithm to each method has been concluded.Simulation results show that the proposed model is more efficient in terms of the channel capacity and energy efficiency,compared to the existing model.

Energy Efficient Relay Positioning and Power Allocation for One-Way N-Relay Channel

Two end-users which have symmetric traffic requirements in terms of data rate are considered. They exchange information in Rayleigh flat-fading channels and multiple serial half-duplex relay nodes are employed to extend the communication coverage and assist the bidirectional communication between them using the analog network coding( ANC) protocol. With the objective of minimizing the sum transmit energy at the required data rate c,the optimal relay positioning and power allocation problem is firstly investigated and then the sub-optimal solutions for a two-relay channel are proposed,due to no close-form optimal solution. Furthermore,a sub-optimal scheme of relay positioning and power allocation,called equal-distance equal-transmit-power( EDEP) for an arbitrary Nrelay channel,N > 1 is proposed. Simulation results demonstrate a consistence with our proposed scheme.

Enhanced PSO Based Energy-Efficient Resource Allocation and CQI Based MCS Selection in LTE-A Heterogeneous System

In order to maximize system energy efficiency(EE) under user quality of service(Qo S) restraints in Long Term Evolution-Advanced(LTE-A) networks,a constrained joint resource optimization allocation scheme is presented,which is NP-hard. Hence,we divide it into three sub-problems to reduce computation complexity,i.e.,the resource block(RB) allocation,the power distribution,and the modulation and coding scheme(MCS) assignment for user codewords. Then an enhanced heuristic approach GAPSO is proposed and is adopted in the RB and power allocation respectively to reduce computational complexity further on. Moreover,a novel MCS allocation scheme is put forward,which could make a good balance between the system reliability and availability under different channel conditions. Simulation results show that the proposed GAPSO could achieve better performance in convergence speed and global optimum searching,and that the joint resource allocation scheme could improve energy efficiency effectively under user Qo S requirements.

Energy efficient resource allocation for D2D multicast communications

The resource allocation for device-to-device(D2D)multicast communications is investigated.To achieve fair energy efficiency(EE)among different multicast groups,the max-min fairness criterion is used as the optimization criterion and the EE of D2D multicast groups are taken as the optimization objective function.The aim is to maximize the minimum EE for different D2D multicast groups under the constraints of the maximum transmit power and minimum transmit rate,which is modeled as a non-convex and mixed-integer fractional programming problem.Here,suboptimal resource allocation algorithms are proposed to solve this problem.First,channel assignment scheme is performed to assign channel to D2D multicast groups.Second,for a given channel assignment,iterative power allocation schemes with and without loss of cellular users’rate are completed,respectively.Simulation results corroborate the convergence performance of the proposed algorithms.In addition,compared with the traditional throughput maximization algorithm,the proposed algorithms can improve the energy efficiency of the system and the fairness achieved among different multicast groups.

Multi-objective planning model for simultaneous reconfiguration of power distribution network and allocation of renewable energy resources and capacitors with considering uncertainties

This research develops a comprehensive method to solve a combinatorial problem consisting of distribution system reconfiguration, capacitor allocation, and renewable energy resources sizing and siting simultaneously and to improve power system's accountability and system performance parameters. Due to finding solution which is closer to realistic characteristics, load forecasting, market price errors and the uncertainties related to the variable output power of wind based DG units are put in consideration. This work employs NSGA-II accompanied by the fuzzy set theory to solve the aforementioned multi-objective problem. The proposed scheme finally leads to a solution with a minimum voltage deviation, a maximum voltage stability, lower amount of pollutant and lower cost. The cost includes the installation costs of new equipment, reconfiguration costs, power loss cost, reliability cost, cost of energy purchased from power market, upgrade costs of lines and operation and maintenance costs of DGs. Therefore, the proposed methodology improves power quality, reliability and security in lower costs besides its preserve, with the operational indices of power distribution networks in acceptable level. To validate the proposed methodology's usefulness, it was applied on the IEEE 33-bus distribution system then the outcomes were compared with initial configuration.

Energy Efficient Resource Allocation in Timesharing Multiuser Systems with Hybrid Energy Harvesting Transmitter

An energy effi cient resource allocation scheme in timesharing multiuser system with a hybrid energy harvesting transmitter is studied in this paper. Specially, the operation energy of system is supplied by constant energy and energy harvesting, which harvests energy from external environment. Our goal is to maximize the energy effi ciency of timesharing multiuser systems by considering jointly allocation of transmission time and power control in an off-line manner. The original nonconvex objective function is transformed into convex optimization problem via the fractional programming approach. Then, we solve the convex problem by Lagrange dual decomposition method. Simulation results show that the proposed energy efficient resource allocation scheme has a better performance than the scheme which decomposes optimization problem into two parts(power allocation, time allocation) to solve iteratively.

Energy Harvesting in Cognitive Networks with Cooperative Beamforming:Power Allocation and Stability Analysis

Energy harvesting (EH) is a promising technology to improve both energy efficiency and spectral efficiency in cognitive radio (CR) networks. However, due to the randomness of the harvested energy and the interference constraint at the primary users (PUs), the limited transmission power of secondary users (SUs) may reduce the service rate of SUs. To solve this problem, this paper investigates a cooperative transmission method where a zero-forcing beamforming method is used in the EH based secondary network. Considering the transmission power constraint and energy causality, we derive the closed-form solution of the optimal transmission power for the secondary source and relays, which achieves the maximal stable throughput of the secondary network. Numerical results show the impact of different system parameters to the maximal stable throughput. In addition, compared with the traditional decode-and-forward (DF) scheme, the cooperative beamforming method achieves higher stable throughput under an high quality source-to-relay channel.