scholarly journals Optimal capacity allocation of multiple energy storage considering microgrid cost

2018 ◽  
Vol 1074 ◽  
pp. 012126
Author(s):  
Yuan Tian ◽  
Xiangyu Li ◽  
Yongqiang Zhu ◽  
Ruihua Xia
Keyword(s):  
Energy Storage ◽  
Capacity Allocation ◽  
Optimal Capacity

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

2020 ◽  
Vol 2020 ◽  
pp. 1-11 ◽  
Author(s):  
Leijiao Ge ◽  
Shuai Zhang ◽  
Xingzhen Bai ◽  
Jun Yan ◽  
Changli Shi ◽  
...  
Keyword(s):  
Energy Storage ◽  
Distribution System ◽  
Storage System ◽  
Pso Algorithm ◽  
Distribution Networks ◽  
Energy Storage System ◽  
Capacity Allocation ◽  
Allocation Model ◽  
Optimal Capacity ◽  
Node Distribution

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.

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

Energies ◽  
2021 ◽  
Vol 14 (6) ◽  
pp. 1611
Author(s):  
Tao Xu ◽  
He Meng ◽  
Jie Zhu ◽  
Wei Wei ◽  
He Zhao ◽  
...  
Keyword(s):  
Energy Storage ◽  
Life Cycle Cost ◽  
Large Scale ◽  
Reactive Power ◽  
Supply And Demand ◽  
Distribution Networks ◽  
Capacity Allocation ◽  
Mixed Integer ◽  
Optimal Capacity ◽  
Network Losses

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.

scholarly journals Optimising the balance of acute and intermediate care capacity for the complex discharge pathway: computer modelling study during COVID-19 recovery in England

2021 ◽  
Author(s):  
Zehra Onen Dumlu ◽  
Alison L Harper ◽  
Paul G Forte ◽  
Anna L Powell ◽  
Martin Pitt ◽  
...  
Keyword(s):  
Computer Modelling ◽  
Care Pathway ◽  
Capacity Allocation ◽  
Community Services ◽  
System Level ◽  
Computer Simulation Model ◽  
Intermediate Care ◽  
Care Services ◽  
Optimal Capacity ◽  
Care Capacity

Objectives: While there has been significant research on the pressures facing acute hospitals during the COVID-19 pandemic, there has been less interest in downstream community services which have also been challenged in meeting demand. This study aimed to estimate the theoretical cost-optimal capacity requirement for 'step down' intermediate care services within a major healthcare system in England, at a time when considerable uncertainty remained regarding vaccination uptake and the easing of societal restrictions. Methods: Demand for intermediate care was projected using an epidemiological model (for COVID-19 demand) and regressing upon public mobility (for non-COVID-19 demand). These were inputted to a computer simulation model of patient flow from acute discharge readiness to bedded and home-based Discharge to Assess (D2A) intermediate care services. Cost-optimal capacity was defined as that which yielded the lowest total cost of intermediate care provision and corresponding acute discharge delays. Results: Increased intermediate care capacity is likely to bring about lower system-level costs, with the additional D2A investment more than offset by substantial reductions in costly acute discharge delays (leading also to improved patient outcome and experience). Results suggest that completely eliminating acute 'bed blocking' is unlikely economical (requiring large amounts of downstream capacity), and that health systems should instead target an appropriate tolerance based upon the specific characteristics of the pathway. Conclusions: Computer modelling can be a valuable asset for determining optimal capacity allocation along the complex care pathway. With results supporting a Business Case for increased downstream capacity, this study demonstrates how modelling can be applied in practice and provides a blueprint for use alongside the freely-available model code.

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