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 s...
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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.
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 l...
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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.
We present a schedulingalgorithm 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...
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We present a schedulingalgorithm 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.
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 ...
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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.
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 rel...
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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 strategie...
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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.
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 quicke...
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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 optimalalgorithm 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 schedulingalgorithms 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.
The goal of task schedulingalgorithm 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 num...
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ISBN:
(纸本)0780378652
The goal of task schedulingalgorithm 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.
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