Optimal Capacity Allocation Research Articles

Optimal Capacity Allocation for Supercapacitor Energy Storage System in Power Grid Primary Frequency Regulation

“Rapid response” features enable supercapacitor energy storage systems (SCESSs), used as auxiliary equipment for primary frequency regulation (PFR), to meet the stability and reliability requirements of power grids. In this application, capacity allocation for SCESSs is studied through theoretical derivation and simulation experiments. Firstly, the application scenarios of SCESSs in PFR are analyzed on the basis of weak grid frequency characteristics. Secondly, in two typical operating modes, namely virtual inertia control and droop control, an integrated operating mode is presented with consideration of the start-stop timing and the output depth of SCESSs, and the relationship between the operating modes and rated powers is derived. Then, to satisfy simulation needs of PFR, an analysis model for SCESSs is built, and a common process of SCESS capacity allocation is summarized. Finally, the optimal rated power, virtual inertia coefficient and droop coefficient of an SCESS is calculated with reference events and frequency control requirements of a weak grid as the benchmark. The results show that aforementioned theory is correct and reasonable, and that the proposed method can achieve optimal frequency regulation effects with a small capacity.

Flood Storage Allocation Rules for Parallel Reservoirs

AbstractOptimal operating policies have been derived for reservoirs in series and in parallel for various purposes, but little formal analysis has been done for flood operation of parallel reservoirs. For flood management, parallel reservoir releases should be managed together to reduce downstream peak flow and minimize flood damage. The optimal allocation of total available flood storage capacity among parallel reservoirs should have each reservoir’s flood storage allocation providing the same incremental reduction to downstream flood flows. This approach is developed mathematically and applied for deterministic and probabilistic inflows. The applicability and effectiveness of these derived flood storage allocation rules are demonstrated by the case study of Oroville Reservoir and New Bullards Bar Reservoir in California’s Sacramento River Basin with a single historical 1997 flood and an uncertain storm.

Computing approximate Nash equilibria in general network revenue management games

Computing optimal capacity allocations in network revenue management is computationally hard. The problem of computing exact Nash equilibria in non-zero-sum games is computationally hard, too. We present a fast heuristic that, in case it cannot converge to an exact Nash equilibrium, computes an approximation to it in general network revenue management problems under competition. We also investigate the question whether it is worth taking competition into account when making (network) capacity allocation decisions. Computational results show that the payoffs in the approximate equilibria are very close to those in exact ones. Taking competition into account never leads to a lower revenue than ignoring competition, no matter what the competitor does. Since we apply linear continuous models, computation time is very short.

A HIGH ROBUSTNESS AND LOW COST CASCADING FAILURE MODEL BASED ON NODE IMPORTANCE IN COMPLEX NETWORKS

In this paper, we investigate the trade-off problem between the high robustness against cascading failures and the low costs of network constructions in complex networks. Since the important nodes with highly connected components usually play a key role in the network structure and network dynamics, we propose an optimal capacity allocation model based on node importance. The novel model will increase the capacities of those important nodes but reduce the network construction cost with the appropriate capacity allocation parameter. Moreover, we also discover that our matching model can enhance the robustness against cascading failures on the IEEE 300 network.

Optimal Capacity Allocation of Large-Scale Wind-PV-Battery Units

An optimal capacity allocation of large-scale wind-photovoltaic- (PV-) battery units was proposed. First, an output power model was established according to meteorological conditions. Then, a wind-PV-battery unit was connected to the power grid as a power-generation unit with a rated capacity under a fixed coordinated operation strategy. Second, the utilization rate of renewable energy sources and maximum wind-PV complementation was considered and the objective function of full life cycle-net present cost (NPC) was calculated through hybrid iteration/adaptive hybrid genetic algorithm (HIAGA). The optimal capacity ratio among wind generator, PV array, and battery device also was calculated simultaneously. A simulation was conducted based on the wind-PV-battery unit in Zhangbei, China. Results showed that a wind-PV-battery unit could effectively minimize the NPC of power-generation units under a stable grid-connected operation. Finally, the sensitivity analysis of the wind-PV-battery unit demonstrated that the optimization result was closely related to potential wind-solar resources and government support. Regions with rich wind resources and a reasonable government energy policy could improve the economic efficiency of their power-generation units.

Optimal Sizing of Battery Energy Storage System in Distribution Network

Economical value including reducing investment in generation side as well as transmission and distribution (T&D) side of the building capacity and decreasing the power expenses for important users brought by BESS are studied.Economic benefits obtained by BESS through decreasing loss of power grid and making use of time of use (TOU) price to reduce electricity tariff are analysed.Considering the primarily capital cost and operation and maintenance (O&M) cost,the capacity optimal allocation model for different types of BESS is developed under optimal conomy condition and genetic algorithm is used to solve it.

The value of energy storage in optimal non-firm wind capacity connection to power systems

Wind is a variable and uncontrollable source of power with a low capacity factor. Using energy storage facilities with a non-firm connection strategy is the key to maximum integration of distant wind farms into a transmission-constrained power system. In this paper, we explore the application of energy storage in optimal allocation of wind capacity to a power system from distant wind sites. Energy storage decreases transmission connection requirements, smoothes the wind farm output and decreases the wind energy curtailments in a non-firm wind capacity allocation strategy. Specifically, we examine the use of compressed air energy storage (CAES) technology to supplement wind farms and downsize the transmission connection requirements. Benders decomposition approach is applied to decompose this computationally challenging and large-scale mixed-integer linear programming (MILP) into smaller problems. The simulation results show that using energy storage systems can decrease the variation of wind farms output as well as the total cost, including investment and operation costs, and increase the wind energy penetration into the power system.

A Theoretical Analysis of Carbon Emission Trading System in China and the Perfection of its Rules and Regulations

The Carbon emission trading mechanism is an important tool to tackle climate change, promote low-carbon economic development, and facilitate ecological civilization construction. The Carbon emission trading system is set up based on the overall amount control. It controls the greenhouse gas emissions through the marketing mechanism, and reduces the cost of carbon emission control. Through theoretical analyses of the property rights of carbon emission right and the legal relationship of carbon emissions trading ,the theoretical basis for the carbon emissions trading mechanism is established. Currently, the measures to improve the carbon emissions trading mechanisms in China mainly consist of perfecting the trading platform, improving the marketing regulation system, promoting legislations of the total amount control of carbon emissions and the initial allocation of carbon emissions right so as to realize the optimal environmental capacity allocation of carbon emissions.

Mathematical Model for Revenue Management with Overbooking and Costly Price Adjustment for Hotel Industries

Revenue management (RM) has been widely used to model products characterized as perishable. Classical RM model assumed that price is the sole factor in the model. Thus price adjustment becomes a crucial and costly factor in business. In this paper, an optimal pricing model is developed based on minimization of soft customer cost, one kind of price adjustment cost and is solved by Lagrange multiplier method. It is formed by expected discounted revenue/bid price integrating quantity-based RM and pricing-based RM. Quantity-based RM consists of two capacity models, namely, booking limit and overbooking. Booking limit, built by assuming uncertain customer arrival, decides the optimal capacity allocation for two market segments. Overbooking determines the level of accepted order exceeding capacity to anticipate probability of cancellation. Furthermore, pricing-based RM models occupancy/demand rate influenced by internal and competitor price changes. In this paper, a mathematical model based on game theoretic approach is developed for two conditions of deterministic and stochastic demand. Based on the equilibrium point, the best strategy for both hotels can be determined.

Genetic Algorithm Based Optimal Capacity Allocation for an Independent Wind/PV/Diesel/Battery Power Generation System

The design of a Wind/PV/Diesel/Battery hybrid power generation system is complex because of the randomness of renewable energy resources, load demand uncertainty and the non-linear characteristics. So the design problem for hybrid power generation system cannot be solved easily by classical optimization methods. In this paper, a Genetic Algorithm (GA) is employed to optimize a wind/PV/Diesel/Battery hybrid system. Using the meteorological data with pollution emissions as constraint condition, the target of optimization is the lowest running cost and the high reliability of system. The results showed that the proposed algorithm could have decision variables as the type and size of wind turbines, the type and the capacity of photovoltaic cells as well as the capacity of the batteries.

Algorithm for optimal wind power plant capacity allocation in areas with limited transmission capacity

SUMMARY One of the basic requirements for making a high-level integration of wind power plants (WPPs) in power systems possible are much higher investments in transmission network development. Often used practice that charges WPP investors with full cost of transmission network upgrades (some of which are not directly linked to WPP connection) could result with large number of good locations being unused. This paper proposes a simulation framework for assessing the possibility of WPP integration into the existing transmission network without any need for additional transmission upgrades. Install capacity of each WPP is determined in a way that maximizes total production from all WPP and minimizes their construction costs while considering transmission network security constrains. Maximum WPP penetration level and optimal capacity allocation are analyzed from the static aspect in order to determine the capability of the transmission network to transfer WPP production while considering maximum line loading under different system operating conditions. WPP production is modeled as stochastic variable considering the correlation between WPP production at different locations. Primal–dual interior point method is employed to solve the optimization problem. Copyright © 2013 John Wiley & Sons, Ltd.

Optimal capacity allocation of standalone wind/solar/battery hybrid power system based on improved particle swarm optimisation algorithm

A standalone wind/solar/battery hybrid power system, making full use of the nature complementarity between wind and solar energy, has an extensive application prospect among various newly developed energy technologies. The capacity of the hybrid power system needs to be optimised in order to make a tradeoff between power reliability and cost. In this study, each part of the wind/solar/battery hybrid power system is analysed in detail and an objective function combining total owning cost and loss of power supply probability is built. To solve the problems with non-linearity, complexity and huge computation, an improved particle swarm optimisation (PSO) algorithm is developed, which integrates the taboo list to broaden the search range and introduces ‘restart’ and ‘disturbance’ operation to enhance the global searching capability. The simulation results indicate that the proposed algorithm is more stable and provides better results in solving the optimal allocation of the capacity of the standalone wind/solar/battery hybrid power system compared with the standard PSO algorithm.

A comparison of routing sets for robust network design

Designing a network able to route a set of non-simultaneous demand vectors is an important problem arising in telecommunications. In this paper, we compare the optimal capacity allocation costs for six routing sets: affine routing, volume routing and its two simplifications, the routing based on an unrestricted 2-cover of the uncertainty set, and the routing based on a cover delimited by a hyperplane.

Overbooking for parallel flights with transference

This paper considers a dynamic overbooking problem for parallel substitutable flights with different departure times. Those flights have multiple fare classes, and reservations are allowed to be canceled with costs. If the go-shows of a flight are larger than its capacity, then some of them may be transferred to a later flight if it has enough capacity. We separate the whole system into some subsystems according to their departure times. The analysis of each sub-system is divided into two parts. The first part derives the optimal booking rules under a given terminal function. The second part determines the terminal function of this subsystem according to a capacity allocation optimization scheme. The optimizing terminal function is shown to be well structured. The optimal overbooking control is conducted by setting overbooking limits on each flight in subsystem dynamically. The terminal function is inherited by the value function of the underlying continuous-time Markov decision process which takes it as the terminal reward. The upper bound on the overbooking limits of each flight is numerically computable, which confines the procedure of computing dynamic overbooking limits to finite ranges.

Resource capacity allocation to stochastic dynamic competitors: knapsack problem for perishable items and index-knapsack heuristic

In this paper we propose an approach for solving problems of optimal resource capacity allocation to a collection of stochastic dynamic competitors. In particular, we introduce the knapsack problem for perishable items, which concerns the optimal dynamic allocation of a limited knapsack to a collection of perishable or non-perishable items. We formulate the problem in the framework of Markov decision processes, we relax and decompose it, and we design a novel index-knapsack heuristic which generalizes the index rule and it is optimal in some specific instances. Such a heuristic bridges the gap between static/deterministic optimization and dynamic/stochastic optimization by stressing the connection between the classic knapsack problem and dynamic resource allocation. The performance of the proposed heuristic is evaluated in a systematic computational study, showing an exceptional near-optimality and a significant superiority over the index rule and over the benchmark earlier-deadline-first policy. Finally we extend our results to several related revenue management problems.

Spatial Integration of Wind Farms

One of the common problems with the production of wind energy is the increased uncertainty surrounding the delivered output. Wind power producers adopt various strategies to reduce production risk, one of the most popular being the dispersion of wind farms across distant locations. The purpose of this paper is to examine the effectiveness of different methodologies for deriving optimal capacity allocation schemes. We collect a large dataset of wind-speed measurements for various locations across the Netherlands and use copula techniques to capture seasonal variations and cross-site dependencies in wind profiles. We exploit the information provided by statistical models to determine optimal resource allocation plans assuming a variety of operational and investment objectives. Experimental results designate the importance of accurately modelling site dependencies when deciding how to optimally allocate the overall wind capacity. Carefully chosen subgroups of sites can result in a more reliable energy supply as opposed to other allocation schemes that ignore spatial correlations.

Production capacity planning for multiple products under uncertain demand conditions

This study chiefly deliberates issues regarding capacity allocation for multiple products. When producing multiple products, a manufacturer needs to allocate a favorable production quantity to each product under conditions of uncertain demand since the excess or shortage of a product will in turn cause the loss of profit. The proposed model and the corresponding algorithm in this study are used to find out the optimal capacity allocation under the given probability density function of specific demands and to effectively allocate limited capacity to multiple products with an aim to maximize profit. Finally, the numerical example suggests that the marginal profit, the inventory holding cost, the shortage cost, the loss of excess production, and market demands should be considered in an effort to discover an optimal capacity allocation with regard to multiple products.

Hybrid Mutation Particle Swarm Optimisation method for Available Transfer Capability enhancement

A Hybrid Mutation Particle Swarm Optimisation (HMPSO) technique for improved estimation of Available Transfer Capability (ATC) as a decision criterion is proposed in this paper. First, this is achieved by comparing a typical application of the Particle Swarm Optimisation (PSO) technique with conventional Genetic Algorithm (GA) methods. Next, a multi-objective optimisation problem concerning optimal installation and capacity allocation of Flexible AC Transmission Systems (FACTSs) devices is presented and demonstrated.Modern heuristic techniques such as PSO have been demonstrated to be suitable approaches in solving non-linear power system problems. The outcome of this research further demonstrates that with better utilisation of FACTS devices, it is possible to improve transmission capabilities.The motivation of this research is a direct consequence of the deregulation of electricity industries and power markets worldwide. The current deregulated environment provides transmission systems operators (TSOs) with more options when procuring transmission services.The effectiveness of the proposed algorithm is demonstrated across a range of case studies, and the results are validated through analyses conducted on IEEE 30-bus and 57-bus test systems.

Reducing the throughput time of the diagnostic track involving CT scanning with computer simulation

IntroductionTo examine the use of computer simulation to reduce the time between the CT request and the consult in which the CT report is discussed (diagnostic track) while restricting idle time and overtime. MethodsAfter a pre implementation analysis in our case study hospital, by computer simulation three scenarios were evaluated on access time, overtime and idle time of the CT; after implementation these same aspects were evaluated again. Effects on throughput time were measured for outpatient short-term and urgent requests only. ConclusionThe pre implementation analysis showed an average CT access time of 9.8 operating days and an average diagnostic track of 14.5 operating days. Based on the outcomes of the simulation, management changed the capacity for the different patient groups to facilitate a diagnostic track of 10 operating days, with a CT access time of 7days. After the implementation of changes, the average diagnostic track duration was 12.6days with an average CT access time of 7.3days. The fraction of patients with a total throughput time within 10days increased from 29% to 44% while the utilization remained equal with 82%, the idle time increased by 11% and the overtime decreased by 82%.The fraction of patients that completed the diagnostic track within 10days improved with 52%. Computer simulation proved useful for studying the effects of proposed scenarios in radiology management. Besides the tangible effects, the simulation increased the awareness that optimizing capacity allocation can reduce access times.

Finding Optimal Allocation of Constrained Cloud Capacity Using Hyperbolic Voronoi Diagrams on the Sphere

Finding Optimal Allocation of Constrained Cloud Capacity Using Hyperbolic Voronoi Diagrams on the Sphere