Online Optimization in Power Systems With High Penetration of Renewable Generation:Advances and Prospects

Traditionally,offline optimization of power systems is acceptable due to the largely predictable loads and reliable generation.The increasing penetration of fluctuating renewable generation and internet-of-things devices allowing for fine-grained controllability of loads have led to the diminishing applicability of offline optimization in the power systems domain,and have redirected attention to online optimization methods.However,online optimization is a broad topic that can be applied in and motivated by different settings,operated on different time scales,and built on different theoretical foundations.This paper reviews the various types of online optimization techniques used in the power systems domain and aims to make clear the distinction between the most common techniques used.In particular,we introduce and compare four distinct techniques used covering the breadth of online optimization techniques used in the power systems domain,i.e.,optimization-guided dynamic control,feedback optimization for single-period problems,Lyapunov-based optimization,and online convex optimization techniques for multi-period problems.Lastly,we recommend some potential future directions for online optimization in the power systems domain.

Online Optimization of Physical-Layer Secure Computation Offloading in Dynamic Environments

Mobile edge computing can provide powerful computation services around the end-users.However,given the broadcast nature of wireless transmissions,offloading the computation tasks via the uplink channels would raise serious security concerns.This paper proposes an online approach to jointly optimize local processing,transmit power,and task offloading decisions without the a-priori knowledge of the dynamic environments.The proposed approach can guarantee the secure offloading and asymptotically minimize the time-average energy consumption of devices while maintaining the stability of the ergodic secrecy queues and task queues.By exploiting the Lyapunov optimization,the local processing,transmit power,and task offloading variables can be decoupled between time slots.The subproblems on local processing and computation offloading can be solved separately.Convex optimization and graph matching can be used to solve the computation offloading subproblem.Simulations show that the performances of the proposed approach are superior to other popular approaches.

A Trusted Multi-Task Distribution Mechanism for Internet of Vehicles Based on Smart Contract

In this paper,a trusted multi-task distribution mechanism for Internet of Vehicles based on smart contract is proposed to improve the security and efficiency for the task distribution in Internet of Vehicles.Firstly,a three-tier trusted multi-task distribution framework is presented based on smart contract.The smart contract will be triggered by the task request.As the important part of the smart contract,the task distribution algorithm is stored on the blockchain and run automatically.In the process of the task distribution,the cost of the task distribution and the system stability play a critical role.Therefore,the task distribution problem is formulated to minimize the cost of the task distribution whilst maintaining the stability of the system based on Lyapunov theorem.Unfortunately,this problem is a mixed integer nonlinear programming problem with NP-hard characteristics.To tackle this,the optimization problem is decomposed into two sub problems of computing resource allocation and task distribution decision,and an effective task distribution algorithm is proposed.Simulation results show that the proposed algorithm can effectively improves system performance.

Intelligent Task Offloading and Collaborative Computation over D2D Communication

In this paper,the problem of computation offloading in the edge server is studied in a mobile edge computation(MEC)-enabled cell networks that consists of a base station(BS)integrating edge servers,several terminal devices and collaborators.In the considered networks,we develop an intelligent task offloading and collaborative computation scheme to achieve the optimal computation offloading.First,a distance-based collaborator screening method is proposed to get collaborators within the distance threshold and with high power.Second,based on the Lyapunov stochastic optimization theory,the system stability problem is transformed into a queue stability issue,and the optimal computation offloading is obtained by solving these three sub-problems:task allocation control,task execution control and queue update,respectively.Moreover,rigorous experimental simulation shows that our proposed computation offloading algorithm can achieve the joint optimization among the system efficiency,energy consumption and time delay compared to the mobility-aware and migration-enabled approach,Full BS and Full local.

Delay-Based Cross-Layer QoS Scheme for Video Streaming in Wireless Ad Hoc Networks

With correlating with human perception, quality of experience(Qo E) is also an important measurement in evaluation of video quality in addition to quality of service(Qo S). A cross-layer scheme based on Lyapunov optimization framework for H.264/AVC video streaming over wireless Ad hoc networks is proposed, with increasing both Qo E and Qo S performances. Different from existing works, this scheme routes and schedules video packets according to the statuses of the frame buffers at the destination nodes to reduce buffer underflows and to increase video playout continuity. The waiting time of head-ofline packets of data queues are considered in routing and scheduling to reduce the average end-to-end delay of video sessions. Different types of packets are allocated with different priorities according to their generated rates under H.264/AVC. To reduce the computational complexity, a distributed media access control policy and a power control algorithm cooperating with the media access policy are proposed. Simulation results show that, compared with existing schemes, this scheme can improve both the Qo S and Qo E performances. The average peak signal-to-noise ratio(PSNR) of the received video streams is also increased.

Adaptive Application Offloading Decision and Transmission Scheduling for Mobile Cloud Computing

Offloading application to cloud can augment mobile devices' computation capabilities for the emerging resource-hungry mobile application, however it can also consume both much time and energy for mobile device offloading application remotely to cloud. In this paper, we develop a newly adaptive application offloading decision-transmission scheduling scheme which can solve above problem efficiently. Specifically, we first propose an adaptive application offloading model which allows multiple target clouds coexisting. Second, based on Lyapunov optimization theory, a low complexity adaptive offloading decision-transmission scheduling scheme has been proposed. And the performance analysis is also given. Finally, simulation results show that,compared with that all applications are executed locally, mobile device can save 68.557% average execution time and 67.095% average energy consumption under situations.

Mobile-agent-based energy-efficient scheduling with dynamic channel acquisition in mobile cloud computing

Mobile cloud computing（MCC） combines mobile Internet and cloud computing to improve the performance of mobile applications. However, MCC faces the problem of energy efficiency because of randomly varying channels. A scheduling algorithm is proposed by introducing the Lyapunov optimization, which can dynamically choose users to transmit data based on queue backlog and channel statistics. The Lyapunov analysis shows that the proposed scheduling algorithm can make a tradeoff between queue backlog and energy consumption in the channel-aware mobile cloud computing system. The simulation results verify the effectiveness of the proposed algorithm.

Day-ahead P2P Energy Sharing Strategy Among Energy Hubs Considering Flexibility of Energy Storage and Loads

Multi-energy systems are one of the key technologies to tackle energy crisis and environmental pollution.An energy hub(EH)is a minimum multi-energy system.Interconnection of multiple EHs through energy routers(ERs)can realize mutual energy assistance.This paper proposes a peer-to-peer(P2P)energy sharing strategy between EHs including ERs in an interconnected system,which is divided into two levels.In the lower level,a method of determining the charging/discharging constraints of energy storage devices is proposed.Based on the Lyapunov optimization method,virtual queues are used to model the energy storage devices and flexible loads in the system.The objective is to minimize the overall operating cost of the interconnected system.In the upper level,a non-cooperative game model is introduced to minimize the cost of purchasing power from other EHs for each EH.A best response-based method is adapted to find the Nash equilibrium.The simulation outcomes demonstrate that application of the proposed strategy can reduce operating costs of an interconnected system and each EH.On basis of a real-world dataset of interconnected EHs,both analytical and numerical results show the effectiveness of the proposed strategy.

A Stochastic Optimization Approach for Dynamic Adaptive Streaming over NDN

Nowadays,video streaming counts for the major part of network traffic over the Internet.However,on account of the host-to-host mechanism of the traditional IP network,video distribution over IP-based Internet encounters bottlenecks.Fortunately,a new proposed future Internet architecture,named data networking(NDN)can improve the performance of video distribution by its features such as in-network storage,multi-path forwarding,etc.In this paper,we design an adaptive bitrate algorithm based on Lyapunov optimization theory over NDN to optimize the long-term quality-of-experience(QoE)of video distribution while ensuring the stability of the whole system.When the network condition is abundant and stable,the problem can be simplified by approximating to a fixed-slot queuing model,but the theoretical performance will degrade when the network status is poor and fluctuate fiercely.Therefore,we divide the problem into two models of fixed time slot and non-fixed time slot and design two Lyapunov optimization algorithms to adapt different network scenarios.The proposed algorithms do not require prior knowledge of the network bandwidth and are capable of running online with the client’s available information.Simulation and realistic experiment results demonstrate that our algorithms perform better than others in NDN.

Real-time Energy Management of Grid-connected Microgrid with Flexible and Delay-tolerant Loads

For optimal operation of microgrids,energy management is indispensable to reduce the operation cost and the emission of conventional units.The goals can be impeded by several factors including uncertainties of market price,renewable generation,and loads.Real-time energy management system(EMS)can effectively address uncertainties due to the online information of market price,renewable generation,and loads.However,some issues arise in real-time EMS as batterylimited energy levels.In this paper,Lyapunov optimization is used to minimize the operation cost of the microgrid and the emission of conventional units.Therefore,the problem is multiobjective and a Pareto front is derived to compromise between the operation cost and the emission.With a modified IEEE 33-bus distribution system,general algebraic modeling system(GAMS)is utilized for implementing the proposed EMS on two case studies to verify its applicability.

An online electricity cost budgeting algorithm for maximizing green energy usage across data centers

With the sky-rocketing development of Internet services, the power usage in data centers has been signifi- cantly increasing. This ever increasing energy consumption leads to negative environmental impact such as global warming. To reduce their carbon footprints, large Internet service operators begin to utilize green energy. Since green energy is currently more expensive than the traditional brown one, it is important for the operators to maximize the green en- ergy usage subject to their desired long-term （e.g., a month） cost budget constraint. In this paper, we propose an online algorithm GreenBudget based on the Lyapunov optimization framework. We prove that our algorithm is able to achieve a delicate tradeoff between the green energy usage and the en- forcement of the cost budget constraint, and a control parameter V is the knob to arbitrarily tune such a tradeoff. We evaluate GreenBudget utilizing real-life traces of user requests, cooling efficiency, electricity price and green energy avail- ability. Experimental results demonstrate that under the same cost budget constraint, GreenBudget can increase the green energy usage by 11.55% compared with the state-of-the-art work, without incurring any performance violation of user requests.