Optimal Storage Allocation Research Articles

Economic Allocation for Energy Storage System Considering Wind Power Distribution

Energy storage systems play a significant role in both distributed power systems and utility power systems. Among the many benefits of an energy storage system, the improvement of power system cost and voltage profile can be the salient specifications of storage systems. Studies show that improper size and placement of energy storage units leads to undesired power system cost as well as the risk of voltage stability, especially in the case of high renewable energy penetration. To solve the problem, a hybrid multi-objective particle swarm optimization (HMOPSO) approach is proposed in the paper to minimize the power system cost and improve the system voltage profiles by searching sitting and sizing of storage units under consideration of uncertainties in wind power production. Furthermore, the probability cost analysis is first put forward in this paper. The proposed HMOPSO combines multi-objective particle swarm optimization (MOPSO) algorithm with elitist nondominated sorting genetic algorithm (NSGA-II) and probabilistic load flow technique. It also incorporates a five-point estimation method (5PEM) for discretizing wind power distribution. The IEEE 30-bus system is adopted to perform case studies. The simulation results for each case clearly demonstrate the necessity for optimal storage allocation, and the effectiveness of the proposed method.

Optimal allocation of energy storage in a co-optimized electricity market: Benefits assessment and deriving indicators for economic storage ventures

This paper presents a framework for optimally allocating storage technologies in a power system. This decision support tool helps in quantitatively answering the questions on “where to and how much to install” considering the profits from arbitrage opportunities in a co-optimized electricity market. The developed framework is illustrated on a modified IEEE (Institute of Electrical and Electronics Engineers) 24 bus RTS (Reliability Test System), and the framework finds the optimal allocation solution and the revenues storage earns at each of these locations. Bulk energy storage, CAES (compressed air energy storage) is used as the representative storage technology, and the benefits of optimally allocated storage integration onto the grid are compared with transmission expansion solution. The paper also discusses about system-level indicators to identify candidate locations for economical storage ventures, which are derived based on the optimal storage allocation solution; and applies the market price based storage venture indicators on MISO (Mid-continental Independent System Operator) and PJM (Pennsylvania-New Jersey-Maryland Interconnection) electricity markets.

Distributed Storage Allocations

We examine the problem of allocating a given total storage budget in a distributed storage system for maximum reliability. A source has a single data object that is to be coded and stored over a set of storage nodes; it is allowed to store any amount of coded data in each node, as long as the total amount of storage used does not exceed the given budget. A data collector subsequently attempts to recover the original data object by accessing only the data stored in a random subset of the nodes. By using an appropriate code, successful recovery can be achieved whenever the total amount of data accessed is at least the size of the original data object. The goal is to find an optimal storage allocation that maximizes the probability of successful recovery. This optimization problem is challenging in general because of its combinatorial nature, despite its simple formulation. We study several variations of the problem, assuming different allocation models and access models. The optimal allocation and the optimal symmetric allocation (in which all nonempty nodes store the same amount of data) are determined for a variety of cases. Our results indicate that the optimal allocations often have nonintuitive structure and are difficult to specify. We also show that depending on the circumstances, coding may or may not be beneficial for reliable storage.

梯级水库防洪库容分配策略研究

针对梯级水库防洪任务和边界条件发生的变化，建立了一种确定梯级水库防洪库容分配的数学模型，耦合水库电力调度优化、防洪效益优化等多种目标，把梯级水库预留的防洪库容最小作为约束条件，采用逐次优化算法求解出使梯级水库发电效益最大的非劣解集。以清江干流梯级水库为例，确定水布垭和隔河岩水库防洪库容的最优分配策略。在水布垭预留5亿m3和隔河岩预留不小于1.5亿m3防洪库容的前提下，若发生100年一遇的洪水时，隔河岩水库的最高调洪水位不会超过200 m，可以满足防洪安全。该研究为梯级水库防洪库容分配提供了一种新的探讨思路。 Due to the changes of flood control task and boundary condition of cascade reservoirs, a mathematical model was established to allocate cascade reservoir flood prevention storage. Coupled hydropower generation with flood control operation objections, progressive optimality algorithm was used to obtain Pareto non-inferior solutions of maximum hydropower generation under the minimum flood prevention storage constrains for cascade reservoirs. The Qingjiang basin was selected as case study, the optimal storage allocation strategy for the Shuibuya and Geheyan reservoirs was given. If the Shuibuya reservoir keeps 500 million m3 and Geheyan reservoir remains not less than 150 million m3 flood prevention storages, the maximum regulation water level of the Geheyan reservoir will not over 200mwhen 100-year flood occurs, which can assurance flood control safety. The proposed method might provide a new approach for cascade reservoir storage allocation.

Optimal buffer storage allocation in balanced reliable production lines

This work deals with the buffer allocation problem in balanced production lines. The workstations of the lines consist of single machines which are assumed to be perfectly reliable, i.e., they don't break down. The processing times follow the exponential or Erlang- k ( k=2,3,4) distribution. The paper presents two basic design rules that were extracted for the optimal buffer allocation in these types of lines using enumeration. We have developed a search technique that gives the optimal buffer allocation very fast. This is a modified Hooke–Jeeves search algorithm that exploits the two design rules to give an initial buffer allocation close to the optimal one. The rules are exploited via the use of the proposed Allocation Routine. The latter was found to give the optimal solution in almost all cases examined in this study.

Optimal random storage allocation for an AS/RS in an FMS

A flexible manufacturing system (FMS) comprises, automated machine tools, automated material handling, and an automated storage and retrieval system (AS/RS) as essential components. Optimal performance of each element of this system would improve the productivity and efficiency of the total FMS. In this context, this paper attempts to link the operation of the AS/RS with the production schedule and suggests two different classes of algorithms, namely, heuristics and genetic algorithms to improve the performance of the AS/RS operation by allocating the materials with minimum movement of the shuttle.

Optimal loop storage allocation for argument-fetching dataflow machines

In this paper, we consider the optimal loop scheduling and minimum storage allocation problems based on the argument-fetching dataflow architecture model. Under the argument-fetching model, the result generated by a node is stored in a unique location which is addressable by its successors. The main contribution of this paper includes: for loops containing no loop-carried dependences, we prove that the problem of allocating minimum storage required to support rate-optimal loop scheduling can be solved in polynomial time. The polynomial time algorithm is based on the fact that the constraint matrix in the formulation is totally unimodular. Since the instruction processing unit of an argument-fetching dataflow architecture is very much like a conventional processor architecture without a program counter, the solution of the optimal loop storage allocation problem for the former will also be useful for the latter.

The Effect of the Coefficient of Variation of Operation Times on the Allocation of Storage Space in Production Line Systems

Abstract This paper studies the effect of the coefficient of variation of operation times on the optimal allocation of storage space in production line systems. The operation times at each station are modelled by a two-stage Coxian distribution. This work extends the results of our previous study of the storage allocation problem with exponentially distributed operation times. Interpreting Stage 1 of the two-stage Coxian distribution as the normal service for an item at a station and Stage 2 as down time at the station, our model can also be used to study the effect of breakdowns on the allocation of storage space in production line systems. The results show that the “bowl effect” whereby the center stations should be given preferential treatment becomes more pronounced with higher variability in the operation times. Another general conclusion is that the overall optimal storage allocation commonly follows a “storage bowl phenomenon” whereby the allocation of buffer storage space fits an inverted bowl pat...

Operation Policy Analysis: Sam Rayburn Reservoir

A combined optimization-simulation procedure was developed and applied to identify the optimal conservation pool storage allocation problem is formulated and solved as a constrained nonlinear programming (NLP) problem with multiple objectives. The Box-Complex algorithm is coupled with an existing generalized reservoir simulation model to seek a solution to the NLP problem. The solutions are refined by an iterative simulation process that allows input from planners and engineers who are involved in management of this system.

A New Approach to the Analysis of Linear Probing Schemes

article Free AccessA New Approach to the Analysis of Linear Probing Schemes Authors: Haim Mendelson Graduate School of Management, University of Rochester, Rochester, NY Graduate School of Management, University of Rochester, Rochester, NYView Profile , Uri Yechiali Department of Statistics, Tel-Aviv University, Ramat-Aviv, Tel-Aviv, Israel Department of Statistics, Tel-Aviv University, Ramat-Aviv, Tel-Aviv, IsraelView Profile Authors Info & Claims Journal of the ACMVolume 27Issue 3July 1980 pp 474–483https://doi.org/10.1145/322203.322209Published:01 July 1980Publication History 8citation370DownloadsMetricsTotal Citations8Total Downloads370Last 12 Months13Last 6 weeks0 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteeReaderPDF

Optimal storage allocation for serial files

A computer system uses several serial files. The files reside on a direct-access storage device in which storage space is limited. Records are added to the files either by jobs in batch processing mode, or by on-line transactions. Each transaction (or job) generates a demand vector which designates the space required in each file for record addition. Whenever one file runs out of space, the system must be reorganized. This paper considers several criteria for best allocating storage space to the files.

Some Problems of Optimization of Complex Computer Systems

A number of specific and complicated optimization problems arise in the designing of modern computer systems. These problems by their nature are the problems of automatic control. The function of the control unit in such systems is carried out by the program which may be as part of software or hardware. This paper deals with two optimal problems. The first one is the problem of optimal processor time allocation in time-sharing systems and the second is the optimal storage allocation problem. The main interest is concentrated on the latter problem.