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optimal scheduling of independently operated, locally controlled energy storage systems as dispatchable assets in a competitive electricity market

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IET GENERATION TRANSMISSION & DISTRIBUTION 2017年第6期11卷 1360-1369页

作者： Khani, Hadi Zadeh, Mohammad R. Dadash Varma, Rajiv K. Western Univ Dept Elect & Comp Engn 1151 Richmond St London ON N6A 5B9 Canada ETAP Dept Res & Dev 17 Goodyear Irvine CA 92618 USA

Independently operated energy storage can exploit arbitrage opportunities available due to inter-temporal variation of electricity prices. Storage can be utilised as a dispatchable or non-dispatchable asset. In this study, a new optimal scheduling algorithm is proposed to enable independently operated, locally controlled storage to accept dispatch instructions issued by independent system operators (ISOs). Storage in this case is referred to as dispatchable storage. In addition, a new index is proposed to measure the storage dispatchability. While the operation of locally controlled storage is optimally scheduled at the owner's end, using the proposed algorithm, storage is fully dispatchable at the ISO's end. Dispatchable storage units have great potential to enhance the flexibility of electric grids and are key elements envisioned to enable smart grid realisation. The proposed algorithm outperforms previous algorithms in which storage is either locally controlled at the owner's end and cannot optimally accept ISO's instructions;or storage is centrally controlled by the grid operator to achieve some technical objectives. The efficacy and feasibility of the proposed algorithm are validated using real-world data. It is demonstrated that the proposed algorithm can enable the storage to accept all ISO's instructions. Revenue values of dispatchable and non-dispatchable storage are computed and compared.

关键词： power markets energy storage power generation scheduling power generation economics power generation dispatch pricing smart power grids independently operated locally controlled energy storage system dispatchable asset competitive electricity market electricity price intertemporal variation nondispatchable asset optimal scheduling algorithm independent system operator storage dispatchability dispatchable storage unit electric grid flexibility enhancement smart grid realisation

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Multiobjective scheduling Strategy With Genetic algorithm and Time-Enhanced A* Planning for Autonomous Parking Robotics in High-Density Unmanned Parking Lots

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IEEE-ASME TRANSACTIONS ON MECHATRONICS 2021年第3期26卷 1547-1557页

作者： Chen, Guang Hou, Jing Dong, Jinhu Li, Zhijun Gu, Shangding Zhang, Bo Yu, Junwei Knoll, Alois Tongji Univ Dept Automot Engn Shanghai 200092 Peoples R China Univ Sci & Technol China Dept Automat Hefei 230026 Peoples R China Wuhan Univ Technol Wuhan 430070 Peoples R China Shanghai Westwell Informat & Technol Co Ltd Autonomous Driving Grp Shanghai 200050 Peoples R China Tech Univ Munich D-85748 Munich Germany

With the process of urbanization, the problem of insufficient parking spaces has become prominent. Adopting a high-density parking lot with parking robots can greatly improve the land utilization rate of the parking lot. This article tackles the multiple parking robots scheduling problem of high-density layout parking lots, including task execution sequence decision, robot allocation, and cooperative path planning. First, we mathematically describe the parking robot scheduling problem. Existing approximation algorithms are often far from the optimal solution. This article proposes an improved genetic algorithm and a time-enhanced A* path planning algorithm for high-density parking lots. The improved genetic algorithm can efficiently search task execution sequence and robot allocation and converge to the optimal solution even in large-scale complex scenarios. Meanwhile, the time-enhanced A* algorithm takes a new dimension "the time" into consideration, together with the distance, and security factors, to solve the multi-parking-robot path planning problem. Simulation experiments show that our algorithm can improve scheduling performance in many aspects such as task execution time, driving distance, and security in large-scale high-density parking lots. This article provides an efficient and convenient scheduling solution for the implementation of the high-density unmanned parking lot.

关键词： Task analysis Robots Path planning Automobiles scheduling Genetic algorithms scheduling algorithms Genetic algorithm high-density automatic parking multirobot systems optimal scheduling algorithm

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MULTIPROCESSOR scheduling OF PROCESSES WITH RELEASE TIMES, DEADLINES, PRECEDENCE, AND EXCLUSION RELATIONS

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IEEE TRANSACTIONS ON SOFTWARE ENGINEERING 1993年第2期19卷 139-154页

作者： XU, J Department of Computer Science York University North York ONT Canada

We present a scheduling algorithm that solves the problem of finding a feasible nonpreemptive schedule whenever one exists on M identical processors for a given set of processes such that each process starts executing after its release time and completes its computation before its deadline, and a given set of precedence relations and a given set of exclusion relations defined on ordered pairs of process segments are satisfied. This algorithm can be applied to the important problem of automated pre-run-time scheduling of processes with arbitrary precedence and exclusion relations on multiprocessors in hard-real-time systems.

关键词： AUTOMATED PRE-RUN-TIME SCHEDULER DEADLINES EXCLUSION HARD-REAL-TIME SYSTEMS MULTIPROCESSOR optimal scheduling algorithm PRECEDENCE

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Energy management platform for integrated battery-based energy storage - solar PV system: a case studyInspec keywordsOther keywords

IET ENERGY SYSTEMS INTEGRATION

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IET ENERGY SYSTEMS INTEGRATION 2020年第4期2卷 373-381页

作者： Suthar, Sachinkumar Pindoriya, Naran M. Indian Inst Technol Gandhinagar Elect Engn Palaj Gujarat India

This study develops an energy management platform for battery-based energy storage (BES) and solar photovoltaic (PV) generation connected at the low-voltage distribution network. The sewage treatment plant of Gujarat International Finance Tec-City located in the Gujarat state is considered as a testbed. The contribution of this case study is twofold: (i) Formulation and lab-scale implementation of an optimal scheduling algorithm to charge/discharge BES while minimising the net energy purchase cost. (ii) Development of a fuzzy logic-based speed control strategy for aeration blower to enhance the process efficiency. A stochastic optimisation problem was formulated as mixed-integer linear programming to achieve the charging and discharging schedules of BES. The simulation study on speed control of aeration blower was carried out to demonstrate the air injection control for optimal dissolved oxygen level in the aeration basin at the testbed. Simulation results of the case study and lab-scale hardware realisation (through real-time digital simulator interfaced with modular hardware) were presented to validate the demand- and supply-side energy management platform.

关键词： photovoltaic power systems energy management systems distribution networks fuzzy logic energy storage optimisation fuzzy control battery storage plants velocity control energy management platform integrated battery-based energy storage solar PV system low-voltage distribution network Gujarat International Finance Tec-City lab-scale implementation net energy purchase cost fuzzy logic-based speed control strategy aeration blower solar photovoltaic generation Gujarat state optimal scheduling algorithm charge-discharge BES stochastic optimisation problem mixed-integer linear programming speed control air injection control optimal dissolved oxygen level aeration basin real-time digital simulator modular hardware demand-side energy management platform supply-side energy management platform

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Feedback and Time are Essential for the optimal Control of Computing Systems

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IFAC-PapersOnLine 2015年第23期48卷 380-387页

作者： Kerrigan, Eric C. Imperial College London United Kingdom

The performance, reliability, cost, size and energy usage of computing systems can be improved by one or more orders of magnitude by the systematic use of modern control and optimization methods. Computing systems rely on the use of feedback algorithms to schedule tasks, data and resources, but the models that are used to design these algorithms are validated using open-loop metrics. By using closed-loop metrics instead, such as the gap metric developed in the control community, it should be possible to develop improved scheduling algorithms and computing systems that have not been over-engineered. Furthermore, scheduling problems are most naturally formulated as constraint satisfaction or mathematical optimization problems, but these are seldom implemented using state of the art numerical methods, nor do they explicitly take into account the fact that the scheduling problem itself takes time to solve. This paper makes the case that recent results in real-time model predictive control, where optimization problems are solved in order to control a process that evolves in time, are likely to form the basis of scheduling algorithms of the future. We therefore outline some of the research problems and opportunities that could arise by explicitly considering feedback and time when designing optimal scheduling algorithms for computing systems. © 2015, IFAC (International Federation of Automatic Control) Hosting by Elsevier Ltd. All rights reserved.

关键词： Distributed computer systems Closed loop control systems Constraint satisfaction problems Feedback Interactive computer systems Model predictive control Numerical methods Optimization Real time systems scheduling scheduling algorithms Sensor networks Telecommunication networks Computing system Constraint Satisfaction Feedback algorithms Mathematical optimization problems Network programming optimal controls optimal scheduling algorithm Optimization problems

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A NOTE ON PREEMPTIVE scheduling OF PERIODIC, REAL-TIME TASKS

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INFORMATION PROCESSING LETTERS 1980年第3期11卷 115-118页

作者： LEUNG, JYT MERRILL, ML Department of Electrical Engineering and Computer Science Northwestern University Evanston IL 60201 U.S.A.

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Value stacking of a behind-the-meter utility-scale battery for demand response markets and demand charge management: real-world operation on the UC San Diego campus

Value stacking of a behind-the-meter utility-scale battery f...

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IEEE-Power-and-Energy-Society General Meeting (PESGM)

作者： Chen, Yi-An Greenough, Ryan Ferry, Mike Johnson, Kelsey Kleissl, Jan Univ Calif San Diego Ctr Energy Res La Jolla CA 92093 USA Nuvve Corp 2468 Hist Decatur Rd 200 San Diego CA 92106 USA

ISBN: (纸本)9781665405072

More and more microgrids implement renewable energy resources and integrate energy storage systems which brings opportunities for energy market participation. While many studies analyze energy market bidding strategies and optimal energy resource management, the complications of real-world operation are often not considered. This study offers an economic model including monthly demand charge management and daily demand response auction mechanism (DRAM) market participation for a real-world operation of risk-averse energy storage scheduling on the UC San Diego campus. Market revenue analysis indicates net DRAM revenue in July and August 2020 of $96,025. A case study provides insight into the market operation and validates the feasibility of the economic model and control algorithm.

关键词： Bidding Strategy Demand Charge Management Demand Response Market optimal scheduling algorithm Real-world Operation

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Optimization of Tasks in Cloud Computing Based on MAX-MIN, MIN-MIN and Priority 4

Optimization of Tasks in Cloud Computing Based on MAX-MIN, M...

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4th International Conference on Web Research (ICWR)

作者： Derakhshan, Majid Bateni, Zohreh Univ Sci & Culture Comp Engn Tehran Iran

ISBN: (纸本)9781538653647

Considering the growing use of cloud computing and the need for optimal use of resources in the cloud, and attention to users that pay for services they use based on their pay-as-you-go basis, There should be a quicker way for users to decrease the user's waiting time and task's waiting time. The main purpose of this paper is to provide an optimal algorithm using the advantages of the two traditional Min-Min and Max-Min algorithms. The other point that follow in this algorithm (TOMMP) is the priority of the tasks. There are a lot of scheduling algorithms in the world today, but the priority given to the tasks has been neglected and overlooked in most algorithms. In this algorithm, priority is firstly selected for tasks based on a prioritization algorithm, and then using the median number to decide which one of the Min-Min or Max-Min algorithms is to be used. It should be noted that according to the TOMMP algorithms, its waiting time is lower than comparisons of the compared algorithms and is shown to be better than the comparable algorithms.

关键词： TOMMP algorithm optimal scheduling algorithm scheduling algorithm cloud scheduling algorithm Min-Min algorithm Max-Min algorithm cloud computing

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A greedy algorithm for scheduling fork-join task graphs

A greedy algorithm for scheduling fork-join task graphs

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International Conference on Machine Learning and Cybernetics

作者： Zhang, JJ Ruan, YL Li, QH Yang, SD Huazhong Univ Sci & Technol Dept Comp Sci & Technol Wuhan 430074 Peoples R China

ISBN: (纸本)0780378652

The goal of task scheduling algorithm is to allocate the tasks of a parallel program to processors in order to minimize the completion time of the program. This is known as an NP-Complete problem. Although a large number of scheduling heuristics have been presented in the literature, most of them ignored to economize processors and minimize total completion time. In this paper, we present a greedy algorithm for scheduling fork-join task graph, which can generate better schedule results. The time complexity of the proposed algorithm is O(nu(2)), where nu is the number of tasks. Experimental comparisons with other algorithms show very favorable results.

关键词： optimal scheduling algorithm task duplication fork-join task graph

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