Distribution energy storage investment prioritization with a real coded multi-objective Genetic Algorithm

Celli, Gianni; Pilo, Fabrizio; Pisano, Giuditta; Soma, Gian Giuseppe

doi:10.1016/j.epsr.2018.06.008

Cited by 21 publications

(18 citation statements)

References 23 publications

(35 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…where c P and c E are the specific costs of the ESS adopted technology, reliant respectively on the power rating P rated n and the nominal capacity E rated n of the n-th ESS located in the network (here c P = 200 €/kW and c E = 400 €/kWh, according to the market cost of lithium-ion technology [15]).…”

Section: Storage Investment Cost C Capex_ess Nmentioning

confidence: 99%

“…Equations 14and (15) impose the limits to the active and reactive power curtailment associated with RES and CHP generators. In Equation 14, P min RESc/CHPc n represents the lower bound of the active power curtailment of RES and CHP generators.…”

Section: Constraints For Res and Controllable Generatormentioning

confidence: 99%

“…In the transmission storage part, a sensitivity analysis is performed using complex-valued neural networks (CVNN) and time domain power flow (TDPF) to obtain the optimal ESS location(s). In [15] a multi-criteria approach where a genetic algorithm (NSGA-II) has been used to identify the optimal place, size, and scheduling of energy storage in the distribution network. The authors created a full multi-objective (MO) optimization procedure able to identify the Pareto set of design options with fixed network topology for a given medium voltage (MV) network.…”

Section: Introductionmentioning

confidence: 99%

“…The authors created a full multi-objective (MO) optimization procedure able to identify the Pareto set of design options with fixed network topology for a given medium voltage (MV) network. In addition to that, the same authors of [15] have proposed a multi-criteria analysis approach selecting the best planning alternative for energy storage integration in the distribution system [16]. However, heuristic techniques often required a high computational burden and are not guaranteed to converge in global optima [17].…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Optimal Energy Storage System Positioning and Sizing with Robust Optimization

2020

Self Cite

View full text Add to dashboard Cite

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.

show abstract

Section: Storage Investment Cost C Capex_ess Nmentioning

confidence: 99%

Section: Constraints For Res and Controllable Generatormentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Optimal Energy Storage System Positioning and Sizing with Robust Optimization

2020

Self Cite

View full text Add to dashboard Cite

show abstract

“…This type of fault is named high-impedance fault. Therefore, the priority in the distribution networks is the loads' reliability and the reduction of interruption duration which can increases the consumers' satisfaction [23].…”

Section: The Pickup Current Boundsmentioning

confidence: 99%

A New Approach for the Setting of Directional Overcurrent Relays by Incorporating Cascading Outages

2020

View full text Add to dashboard Cite

Directional overcurrent relays (DOCRs) are the essential protective devices in distribution networks which are usually set without considering any contingencies. However, the current challenge in power systems is the existence of uncertainty and its unfavorable consequences. It sometimes appears that some elements simultaneously fail which makes other parts to be overloaded to the extent that it leads to cascading outages. Therefore, DOCRs may have mal-operation which ends in unwanted trips when there is no fault, or they may not operate in the case the fault is located within their reach point. In such cases, the coordination setting will need complex programming with many related non-linear inequality constraints. In this paper, a novel hybrid method is proposed based on multi-objective optimization including new objective functions by using the genetic algorithm (GA). Also, the cascading outages are considered in the presented method based on network data analysis. This approach is performed on distribution part of the IEEE 14-bus meshed system, and a real industrial radial feeder named TOSEE, located in Iran. The simulations have been implemented in MATLAB and PowerFactory-DIgSILENT software packages in different models, and the results are evaluated.

show abstract

Energy trilemma in active distribution network design: Balancing affordability, sustainability and security in optimization-based decision-making

Sansavini

2021

Applied Energy

View full text Add to dashboard Cite

Distribution energy storage investment prioritization with a real coded multi-objective Genetic Algorithm

Cited by 21 publications

References 23 publications

Optimal Energy Storage System Positioning and Sizing with Robust Optimization

Optimal Energy Storage System Positioning and Sizing with Robust Optimization

A New Approach for the Setting of Directional Overcurrent Relays by Incorporating Cascading Outages

Energy trilemma in active distribution network design: Balancing affordability, sustainability and security in optimization-based decision-making

Contact Info

Product

Resources

About