Capacity Allocation Scheme Research Articles

Generalized Energy Storage Allocation Strategies for Load Aggregator in Hierarchical Electricity Markets

The uncertainty of user-side resource response will affect the response quality and economic benefit of load aggregator (LA). Therefore, this paper regards the flexible user-side resources as a virtual energy storage (VES), and uses the traditional narrow sense energy storage (NSES) to alleviate the uncertainty of VES. In order to further enhance the competitive advantage of LA in electricity market transactions, the opertion mechanism of LA in day-ahead and real-time market is analyzed, respectively. Besides, truncated normal distribution is used to simulate the response accuracy of VES, and the response model of NSES is constructed at the same time. Then, the hierarchical market access index (HMAI) is introduced to quantify the risk of LA being eliminated in the market competition. Finally, combined with the priority response strategy of VES and HMAI, the capacity allocation model of NSES is established. As the capacity model is nonlinear, Monte Carlo simulation and adaptive particle swarm optimization algorithm are used to solve it. In order to verify the effectiveness of the model, the data from PJM market in the United States is used for testing. Simulation results show that the model established can provide the effective NSES capacity allocation strategy for LA to compensate the uncertainty of VES response, and the economic benefit of LA can be increased by 52.2% at its maximum. Through the reasonable NSES capacity allocation, LA is encouraged to improve its own resource level, thus forming a virtuous circle of market competition.

An efficient routing strategy for coupled spatial networks

The study of traffic dynamics on couple networks is important for the design and management of many real systems. In this paper, an efficient routing strategy on coupled spatial networks is proposed, considering both traffic characteristics and network topology information. With the routing strategy, the traffic capacity can be greatly improved in both scenarios of identical and heterogeneous node capacity allocation. Heterogeneous allocation strategy of node delivery capacity performs better than identical capacity allocation strategy. The study can help to improve the performance of real-world multi-modal traffic systems.

Optimal Configuration with Capacity Analysis of PV-Plus-BESS for Behind-the-Meter Application

As the cost of photovoltaic (PV) systems and battery energy storage systems (BESS) decreases, PV-plus-BESS applied to behind-the-meter (BTM) market has grown rapidly in recent years. With user time of use rates (TOU) for charging and discharging schedule, it can effectively reduce the electricity expense of users. This research uses the contract capacity of an actual industrial user of 7.5 MW as a research case, and simulates a PV/BESS techno-economic scheme through the HOMER Grid software. Under the condition that the electricity demand is met and the PV power generation is fully used, the aim is to find the most economical PV/BESS capacity allocation and optimal contract capacity scheme. According to the load demand and the electricity price, the analysis shows that the PV system capacity is 8.25 MWp, the BESS capacity is 1.25 MW/3.195 MWh, and the contract capacity can be reduced to 6 MW. The benefits for the economical solution are compared as follows: 20-year project benefit, levelized cost of energy (LCOE), the net present cost (NPC), the internal rate of return (IRR), the return on investment (ROI), discounted payback, total electricity savings, renewable fraction (RF), and the excess electricity fraction. Finally, the sensitivity analysis of the global horizontal irradiation, electricity price, key component cost, and real interest rate will be carried out with the most economical solution by analyzing the impacts and evaluating the economic evaluation indicators. The analysis method of this research can be applied to other utility users to program the economic benefit evaluation of PV/BESS, especially an example for Taiwan’s electricity prices at low levels in the world.

Optimal Model of Desalination Planning Under Uncertainties in a Water Supply System

A requirement for developing desalination efforts in coastal regions suffering water scarcity is proposed to address the increased water demand and limited traditional water supply. The determination of a plant capacity and water allocation scheme in a multiple-source water supply system, as the first problem in planning desalination under streamflow and water demand uncertainties, remains a challenge. To address this gap, an interval-parameter two-stage stochastic programming model is developed in this study. The first-stage problem is to determine a proper desalination plant capacity, and the second is the development of a water allocation scheme under the uncertainties of natural streamflows, water demands, benefits and economic losses. The objective function is to maximize the net benefit of the system, and the cost function of desalination, including capital and operational costs, implying environmental impact, is linearized within a range of plant capacities to solve the model. The proposed approach is applied to an urban area of Weihai in China to illustrate the validity of the model. The results suggest a capacity of 46 × 103 m3/d in 2030 and 55 × 103 m3/d in 2040. Sensitivity analyses of the parameters indicate that a decrease in the unit price of electricity leads to an increase in the utilization level of desalinated seawater. A complementary relationship was observed between reclaimed water and desalinated seawater, in that a decrease in the use rate of reclaimed water from 0.38 to 0.18 led to a 15% increase in desalinated plant capacity.

Adaptive Robust Recourse-Based Bidding Strategy and Capacity Allocation of PV-WT-BES Owning Prosumers Under Uncertainties

This article presents an adaptive robust co-optimization for capacity allocation and bidding strategy of a prosumer equipped with photovoltaic system (PV), wind turbine (WT), and battery energy storage (BES). The uncertainties of load and PV/WT productions are modeled through controllable user-defined polyhedral uncertainty sets. The proposed co-optimization determines the optimal capacity of PV-WT-BES, while maximizing prosumer's benefit by 1) optimal self-scheduling of PV-WT-BES, and 2) effective interactions with grid through optimal buying/selling bids under uncertainties. In previous min-max-min robust models, it was not possible to characterize bidding strategy binary variables as recourse decisions which was due to the use of duality theory in solving the inner max-min problem (duality theory is weak and nontractable in the presence of binary variables). In this study, block coordinate descent (BCD) method is used to solve the inner max-min problem by means of Taylor series instead of transforming it into a single-level max problem by duality theory. As a result, prosumer's bidding status (indicated by binary variables) can be successfully modeled as recourse decisions, which make the obtained solutions more realistic and robust. Linearization of the dualized inner problem is also avoided as Lagrange multipliers are eliminated. A post-event analysis is developed to avoid over/under conservative solutions and to determine the optimal robust settings of the model. A comprehensive case study is conducted for an industrial prosumer. To illustrate the effectiveness of the proposed BCD robust model, its long-term performance is compared with conventional dual-based models in the literature. Results show 10% long-term cost reductions when using the proposed model under uncertainties.

Optimal Capacity Allocation of Energy Storage in Distribution Networks Considering Active/Reactive Coordination

Energy storage system (ESS) has been advocated as one of the key elements for the future energy system by the fast power regulation and energy transfer capabilities. In particular, for distribution networks with high penetration of renewables, ESS plays an important role in bridging the gap between the supply and demand, maximizing the benefits of renewables and providing various types of ancillary services to cope the intermittences and fluctuations, consequently improving the resilience, reliability and flexibility. To solve the voltage fluctuations caused by the high permeability of renewables in distribution networks, an optimal capacity allocation strategy of ESS is proposed in this paper. Taking the life cycle cost, arbitrage income and the benefit of reducing network losses into consideration, a bilevel optimization model of ESS capacity allocation is established, the coordination between active/reactive power of associate power conversion system is considered, and the large scale nonlinear programming problem is solved using genetic algorithm, simulated annealing and mixed integer second-order cone programming method. The feasibility and effectiveness of the proposed algorithm have been verified.

Temporal-spatial allocation of bottleneck capacity for managing morning commute with carpool

Carpooling as one of demand management measures is effective in reducing highway congestion. Recent studies have shown that an appropriate spatial allocation of bottleneck capacity between carpool lane and general-purpose lane can lead to peak-spread of the morning commuters' departure time and reduce the system's total trip cost. What is not clear however is whether temporal allocation of bottleneck capacity can also be effective and if so, what the impact would be, and furthermore what the combined effects of temporal-spatial allocation of bottleneck capacity would be. This paper investigates the impacts of a temporal allocation of bottleneck capacity, when carpool lane is available only within a reserved time window, and a joint temporal-spatial capacity allocation, on morning commute patterns. User equilibrium commute patterns are derived for both the temporal-only and the joint temporal-spatial capacity allocation schemes, along a highway corridor with two driving modes: solo driving and carpooling. The extra costs associated with carpooling are considered alongside of travel time and schedule delay costs. We identify three different cases representing the relative barriers and attractions of carpooling to commuters, and we show that the optimal capacity allocations are sensitive to the accurate estimation of the commuters' extra carpool cost. To assist in evaluating the difference between a non-optimal and the optimal temporal-spatial allocation schemes, we derive analytically the upper bounds on the efficiency loss and present numerical illustrations on how the upper bounds vary with the different operational and behavioral variables.

Improving robustness of complex networks by a new capacity allocation strategy

The robustness of infrastructure networks has attracted great attention in recent years. Scholars have studied the robustness of complex networks against cascading failures from different aspects. In this paper, a new capacity allocation strategy is proposed to reduce cascading failures and improve network robustness without changing the network structure. Compared with the typical strategy proposed in Motter–Lai (ML) model, the new strategy can reduce the scale of cascading failure. The new strategy applied in scale-free network is more efficient. In addition, to reasonably evaluate the two strategies, we introduce contribution rate of unit capacity to network robustness as evaluation index. Results show that our new strategy works well, and it is more advantageous in the rational utilization of capacity in scale-free networks. Furthermore, we were surprised to find that the efficient utilization of capacity costs declined as costs rose above a certain threshold, which indicates that it is not wise to restrain cascading failures by increasing capacity costs indefinitely.

Economic Capacity Allocation of Grid-connected Microgrid Based on Improved Hybrid Genetic Algorithm

For the wind and solar diesel storage grid-connected microgrid, in order to meet the minimum investment cost of the microgrid at the initial stage and have certain economic benefits. The objective function of this paper is the annual average economic cost, the number of each micro-power source is used as the optimization variable, the constraints of micro-grid operation are considered, the capacity optimization configuration model of grid-connected micro-grid is established, and then the improved genetic algorithm can be used to solve this problem. Finally, the annual average economic cost, the utilization rate of new energy and the redundancy rate are used as evaluation indicators. By comparing the configuration results of genetic algorithm, wolf pack algorithm and mixed wolf pack genetic algorithm, the capacity allocation scheme of microgrid under economic indicators is obtained.

Planning Optimization of Integrated Energy System Considering Economy and Integrated Energy Efficiency

The integrated energy system is the concrete realization of the energy Internet in the region by coupling various energies and carrying out multi-dimensional coordinated management and distribution of various energies such as electricity, gas, cold and heat. An Integrated energy system planning method is proposed. Firstly, a heterogeneous energy network model of a typical integrated energy system is developed. Aiming at minimizing the total operating cost and maximizing the integrated energy efficiency of the integrated energy system, NSGA-II algorithm is used to realize optimization in MATLAB program. Through the TOPSIS method, multiple sets of solutions are compared and analyzed, thus the optimal planning scheme of the system is obtained quickly and accurately. Through optimization calculation, the optimal capacity allocation and operation strategy of major equipment can be obtained. On the premise of meeting users’ various energy demands, the system investment and energy consumption cost can be reduced to the maximum extent, and the efficient utilization and sustainable development of various energy sources can be promoted.

Two sources of task prioritization: The interplay of effector-based and task\xa0order-based capacity allocation in the PRP paradigm

When processing of two tasks overlaps, performance is known to suffer. In the well-established psychological refractory period (PRP) paradigm, tasks are triggered by two stimuli with a short temporal delay (stimulus onset asynchrony; SOA), thereby allowing control of the degree of task overlap. A decrease of the SOA reliably yields longer RTs of the task associated with the second stimulus (Task 2) while performance in the other task (Task 1) remains largely unaffected. This Task 2-specific SOA effect is usually interpreted in terms of central capacity limitations. Particularly, it has been assumed that response selection in Task 2 is delayed due to the allocation of less capacity until this process has been completed in Task 1. Recently, another important factor determining task prioritization has been proposed—namely, the particular effector systems associated with tasks. Here, we study both sources of task prioritization simultaneously by systematically combining three different effector systems (pairwise combinations of oculomotor, vocal, and manual responses) in the PRP paradigm. Specifically, we asked whether task order-based task prioritization (SOA effect) is modulated as a function of Task 2 effector system. The results indicate a modulation of SOA effects when the same (oculomotor) Task 1 is combined with a vocal versus a manual Task 2. This is incompatible with the assumption that SOA effects are solely determined by Task 1 response selection duration. Instead, they support the view that dual-task processing bottlenecks are resolved by establishing a capacity allocation scheme fed by multiple input factors, including attentional weights associated with particular effector systems.

Optimal Capacity Allocation of Energy Storage System considering Uncertainty of Load and Wind Generation

Energy storage systems (ESSs) are promising solutions for the mitigation of power fluctuations and the management of load demands in distribution networks (DNs). However, the uncertainty of load demands and wind generations (WGs) may have a significant impact on the capacity allocation of ESSs. To solve the problem, a novel optimal ESS capacity allocation scheme for ESSs is proposed to reduce the influence of uncertainty of both WG and load demands. First, an optimal capacity allocation model is established to minimize the ESS investment costs and the network power loss under constraints of DN and ESS operating points and power balance. Then, the proposed method reduces the uncertainty of load through a comprehensive demand response system based on time-of-use (TOU) and incentives. To predict the output of WGs, we combined particle swarm optimization (PSO) and backpropagation neural network to create a prediction model of the wind power. An improved simulated annealing PSO algorithm (ISAPSO) is used to solve the optimization problem. Numerical studies are carried out in a modified IEEE 33-node distribution system. Simulation results demonstrate that the proposed model can provide the optimal capacity allocation and investment cost of ESSs with minimal power losses.

The impact of ex-post information sharing on a two-echelon supply chain with horizontal competition and capacity constraint

We consider a two-echelon supply chain consisting of one capacity-constrained supplier and two Cournot-competitive retailers. The incumbent retailer has private demand information and takes strategic information sharing to maximize its expected profit. The supplier carries out capacity allocation strategy when the capacity cannot satisfy both retailers’ total order quantity. We model three scenarios of information sharing: (1) no member is informed; (2) only the entrant retailer is informed; and (3) both the supplier and entrant retailer are informed. We characterize the conditions under which information sharing may benefit or hurt the supply chain and different members and meaningful information sharing is achieved via “cheap talk”. Compared with no information sharing, when only the entrant retailer is informed, the incumbent retailer always has incentive to deflate its demand information to make the entrant retailer order less. Both retailers cannot achieve meaningful information sharing. When the supplier and entrant retailer both are informed, the incumbent retailer faces a trade-off between the desire to receive more products and the fear of intense competition and high wholesale price. The meaningful information sharing is achieved and all members are benefitted. The numerical examples verify theoretical results.

Pricing and capacity allocation strategies: Implications for manufacturers with product sharing

AbstractEmerging sharing modes, like the consumer‐to‐consumer (C2C) sharing of Uber and the business‐to‐consumer (B2C) sharing of GoFun, have considerably affected the retailing markets of traditional manufacturers, who are motivated to consider product sharing when making pricing and capacity decisions, particularly electric car manufacturers with limited capacity. In this paper, we examine the equilibrium pricing for a capacity‐constrained manufacturer under various sharing modes and further analyze the impact of capacity constraint on the manufacturer's sharing mode selection as well as equilibrium outcomes. We find that manufacturers with low‐cost products prefer B2C sharing while those with high‐cost products prefer C2C sharing except when the sharing price is moderate. However, limited capacity motivates manufacturers to enter into the B2C sharing under a relatively low sharing price, and raise the total usage level by sharing high‐cost products. We also show that the equilibrium capacity allocated to the sharing market with low‐cost products first increases and then decreases. Finally, we find that sharing low‐cost products with a high limited capacity leads to a lower retail price under B2C sharing, which creates a win‐win situation for both the manufacturer and consumers. However, sharing high‐cost products with a low limited capacity creates a win‐lose situation for them.

Delivering capacity allocation strategy for traffic dynamics on scale-free networks

The traffic dynamics of complex networks are largely determined by the node’s resource distribution. In this paper, based on the shortest path routing strategy, a node delivering capacity distribution mechanism is proposed into the traffic dynamics in Barabási and Albert (BA) scale-free networks; the efficiency of the mechanism on the network capacity measured by the critical point ([Formula: see text]) of phase transition from free flow to congestion is primarily explored. Based on the proposed strategy, the total delivering capacity is reallocated according to both degree and betweenness of each node, and an optimal value of parameter [Formula: see text] is found, leading to the maximum traffic capacity. The results of numerical experiments on scale-free networks suggest that the resource allocation strategy proposed here is capable of effectively enhancing the transmission capacity of networks. Furthermore, this study may provide novel insights into research on networked traffic systems.

Electric vehicle battery capacity allocation and recycling with downstream competition

We study the optimal channel choice and battery capacity allocation strategies of an electric vehicle (EV) manufacturer in the presence of battery recycling. The EV manufacturer also produces EV batteries. As an upstream manufacturer, this firm has the option to supply batteries to its competitor, a downstream EV manufacturer which is unable to produce batteries. We characterise the equilibrium decisions of the two EV manufacturers under different channel structures. One finding is that the optimal channel choice of each EV manufacturer depends on the procurement cost from an external battery supplier. If the procurement cost is moderate, the downstream manufacturer prefers to order from the upstream manufacturer and the upstream manufacturer will supply batteries to satisfy the order. In addition, we investigate the effects of key parameters on the equilibrium capacity allocation decisions and manufacturers’ profits using numerical experiments. We also show that to maximise social welfare, the upstream EV manufacturer should not supply batteries to its competitor if the procurement cost from the external supplier is low, which is contrary to the case of profit maximisation.

Research on DFIG-ES System to Enhance the Fast-Frequency Response Capability of Wind Farms

With the increasing penetration of wind power generation, the frequency regulation burden on conventional synchronous generators has become heavier, as the rotor speed of doubly-fed induction generator (DFIG) is decoupled with the system frequency. As the frequency regulation capability of wind farms is an urgent appeal, the inertia control of DFIG has been studied by many researchers and the energy storage (ES) system has been installed in wind farms to respond to frequency deviation with doubly-fed induction generators (DFIGs). In view of the high allocation and maintenance cost of the ES system, the capacity allocation scheme of the ES system—especially for fast-frequency response—is proposed in this paper. The capacity allocation principle was to make the wind farm possess the same potential inertial energy as that of synchronous generators set with equal rated power. After the capacity of the ES system was defined, the coordinated control strategy of the DFIG-ES system with consideration of wind speed was proposed in order to improve the frequency nadir during fast-frequency response. The overall power reference of the DFIG-ES system was calculated on the basis of the frequency response characteristic of synchronous generators. In particular, once the power reference of DFIG was determined, a novel virtual inertia control method of DFIG was put forward to release rotational kinetic energy and produce power surge by means of continuously modifying the proportional coefficient of maximum power point tracking (MPPT) control. During the deceleration period, the power reference smoothly decreased with the rotor speed until it reached the MPPT curve, wherein the rotor speed could rapidly recover by virtue of wind power so that the secondary frequency drop could be avoided. Afterwards, a fuzzy logic controller (FLC) was designed to distribute output power between the DFIG and ES system according to the rotor speed of DFIG and S o C of ES; thus the scheme enabled the DFIG-ES system to respond to frequency deviation in most cases while preventing the secondary frequency drop and prolonging the service life of the DFIG-ES system. Finally, the test results, which were based on the simulation system on MATLAB/Simulink software, verified the effectiveness of the proposed control strategy by comparison with other control methods and verified the rationality of the designed fuzzy logic controller and proposed capacity allocation scheme of the ES system.

MBO-Based Bandwidth Allocation and Traffic Coloring Optimization in PON

Abstract Passive optical network (PON)-supported orthogonal frequency-division multiplexing is viewed as a capable answer designed for subsequent generation optical networks in terms of traffic coloring when integrated with monarch butterfly optimization (MBO). Traffic coloring optimization (TCO) or MBO algorithmic rule solves the matter of controlling the traffic, began by end-user transceivers with multiple parameters through rescheduling transmission capacity and bandwidth allocation strategy of the optical network, which are done by means of its operator functions. The TCO method with granularity adjustment components outcome signifies an improvement in quality of service as it reduces jitter and increases throughput in PON by 350 µs and 12.5 %, respectively. It even improves the blocking probability and fairness index.

Carbon reduction potential based on life cycle assessment of China’s aluminium industry-a perspective at the province level

Abstract The aluminium industry, with its traditionally high energy consumption, high emissions and high pollution, is facing increasing pressure to reduce carbon emissions in China. Implementing effective policies and measures to promote carbon emissions reduction is of great importance. This study deploys the life cycle assessment (LCA) method to explore carbon emissions and CO2 sources from aluminium manufacturing in China as well as a scenario analysis to evaluate the reduction potential and driving forces of carbon emissions in the industry. The evaluated scenarios include a new capacity allocation scheme, technological progress and energy structure optimization. The carbon emissions from the aluminium chain are mostly contributed by indirect emissions (more than 68%) from electricity generation. Significant regional discrepancies are observed in the total emissions and intensity, and a reasonable industry layout tends to transfer to the south and southwest areas from the perspective of the environmental pillar. Production output remains the dominant factors associated with total carbon emissions from manufacturing firms, such as the aluminium industry. Technological progress and the energy structure contribute to total reductions and carbon efficiency, although the former has a relatively limited scope for aluminium firms. This paper also shows that marginal carbon efficiency improvements are not very effective in lowering the total CO2 emissions under the context of high output growth.

Dynamic Capacity Allocation in Smart Gateway High Throughput Satellite Systems Using Matching Theory

In this paper, the performance of multibeam high throughput satellite systems, which employ the smart gateway diversity concept, is investigated and various capacity allocation schemes are examined. These satellite systems operate at high frequencies above 20 GHz. To study the system's capacity losses, the impact of the total atmospheric attenuation is incorporated and a novel flexible dynamic capacity allocation scheme is proposed based on the offered capacities of gateways and requested capacities of users’ beams. Specifically, the system's capacity losses, which constitute the system's performance metric, are determined as the summation of difference among the requested and offered capacities of paired user beams and gateways when this difference is positive. The proposed allocation scheme has low complexity, and its resource allocation algorithm is based on the fundamentals of matching theory, implemented in realistic scenarios where each gateway serves one or more users’ beams simultaneously. The proposed scheme has the same performance with the highly complex Exhaustive algorithm that estimates all possible matching combinations between gateways and users’ beams to find the minimum capacity losses. It is finally compared in terms of system's capacity losses with three other schemes, namely, the Fairness mechanism, a less complicated Fixed mechanism and a naive Random mechanism. The proposed scheme is better than the Fairness method in smaller probabilities and generally outperforms both the Fixed and Random methods.