Optimal siting and sizing of Energy Storage System for power systems with large-scale wind power integration

Zhao, Haoran; Wu, Qiuwei; Huang, Shaojun; Guo, Qinglai; Sun, Hongbin; Xue, Yusheng

doi:10.1109/ptc.2015.7232615

Cited by 21 publications

(13 citation statements)

References 12 publications

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“…Specifying Equation (23) in the model, with the inclusion of the direction of the power flow, this becomes Equation (15). Finally, the maximum and minimum voltage variations for bus i are defined in Equation (24):…”

Section: Voltage Drop Equationsmentioning

confidence: 99%

“…An alternative current (AC) OPF that minimizes operating cost is used in [22]. In [23] the objective function is composed of ESS charging and discharging costs, weighting cost losses and the additional cost of adding more generation. In [24], the objective function comprises the squared norm of voltage deviations of all the network buses over a given period of time, the total amount of network losses over a given period of time and the total energy cost related to the power flow with the external grid.…”

Section: Introductionmentioning

confidence: 99%

“…Heuristic methods have been also applied, where a genetic algorithm (GA) is applied using chance constrained programming (CCP) [2], tabu-search (TS) [12], particle swarm optimization (PSO) [17], and big bang-big crunch (BB-BC) in [16]. Probabilistic optimal power flow (POPF) models are used in [7,[21][22][23][24]. In [19] the authors converted the stochastic problem to a convex optimization one.…”

Section: Introductionmentioning

confidence: 99%

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Optimal Placement of Energy Storage and Wind Power under Uncertainty

Quevedo

Contreras

2016

Energies

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Due to the rapid growth in the amount of wind energy connected to distribution grids, they are exposed to higher network constraints, which poses additional challenges to system operation. Based on regulation, the system operator has the right to curtail wind energy in order to avoid any violation of system constraints. Energy storage systems (ESS) are considered to be a viable solution to solve this problem. The aim of this paper is to provide the best locations of both ESS and wind power by optimizing distribution system costs taking into account network constraints and the uncertainty associated to the nature of wind, load and price. To do that, we use a mixed integer linear programming (MILP) approach consisting of loss reduction, voltage improvement and minimization of generation costs. An alternative current (AC) linear optimal power flow (OPF), which employs binary variables to define the location of the generation, is implemented. The proposed stochastic MILP approach has been applied to the IEEE 69-bus distribution network and the results show the performance of the model under different values of installed capacities of ESS and wind power.

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Section: Voltage Drop Equationsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Optimal Placement of Energy Storage and Wind Power under Uncertainty

Quevedo

Contreras

2016

Energies

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“…However, MPPT could be more advantageous to be used for a single or a remote turbine or a small group of solar panel technique, since it does not require any further resources like BESS or an additional maintenance cost. Nevertheless, for an aggregated number of wind turbines or a large number of solar panels, BESS could be the recommended option since it is a unique choice for many problems that are related to integrating RES [19]- [20] as it is mentioned earlier. Additionally, the inertia constant (H) is controllable when using BESS emulation control techniques whereas it is variable and depend mostly on the operational limitation that it is obtained from VSWT.…”

Section: B Inertia Emulating Techniques Using Bessmentioning

confidence: 99%

Investigation on grid-scale BESS providing inertial response support

Alhejaj

González-Longatt

2016

2016 IEEE International Conference on Power System Technology (POWERCON)

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“…To consider uncertainties in the model, a stochastic optimization approach has been considered. Two different problems have been formulated respectively for the siting and sizing of ESS in distribution networks coupled with a wind farm in [11]. However, the authors considered a linearized DC-optimal power flow (DC-OPF), and the wind power forecast is assumed perfect.…”

Section: Introductionmentioning

confidence: 99%

Optimal Energy Storage System Positioning and Sizing with Robust Optimization

2020

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Energy storage systems can improve the uncertainty and variability related to renewable energy sources such as wind and solar create in power systems. Aside from applications such as frequency regulation, time-based arbitrage, or the provision of the reserve, where the placement of storage devices is not particularly significant, distributed storage could also be used to improve congestions in the distribution networks. In such cases, the optimal placement of this distributed storage is vital for making a cost-effective investment. Furthermore, the now reached massive spread of distributed renewable energy resources in distribution systems, intrinsically uncertain and non-programmable, together with the new trends in the electric demand, often unpredictable, require a paradigm change in grid planning for properly lead with the uncertainty sources and the distribution system operators (DSO) should learn to support such change. This paper considers the DSO perspective by proposing a methodology for energy storage placement in the distribution networks in which robust optimization accommodates system uncertainty. The proposed method calls for the use of a multi-period convex AC-optimal power flow (AC-OPF), ensuring a reliable planning solution. Wind, photovoltaic (PV), and load uncertainties are modeled as symmetric and bounded variables with the flexibility to modulate the robustness of the model. A case study based on real distribution network information allows the illustration and discussion of the properties of the model. An important observation is that the method enables the system operator to integrate energy storage devices by fine-tuning the level of robustness it willing to consider, and that is incremental with the level of protection. However, the algorithm grows more complex as the system robustness increases and, thus, it requires higher computational effort.

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Optimal siting and sizing of Energy Storage System for power systems with large-scale wind power integration

Cited by 21 publications

References 12 publications

Optimal Placement of Energy Storage and Wind Power under Uncertainty

Optimal Placement of Energy Storage and Wind Power under Uncertainty

Investigation on grid-scale BESS providing inertial response support

Optimal Energy Storage System Positioning and Sizing with Robust Optimization

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