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Literature Review: Distributed Energy Storage

Introduction:

Distributed energy storage (DES) is a technology that enables the storage of electricity at various points in the power grid, including homes, businesses, and other locations. DES can help to reduce peak demand on the grid and improve overall system reliability. This literature review provides an overview of recent research on distributed energy storage.

Literature Review:

1. Kuzlu, M., & Ozpineci, B. (2017). A review of distributed energy resources and advanced microgrid controllers for distribution network applications. Renewable and Sustainable Energy Reviews, 70, 1058-1070.

This paper provides an overview of distributed energy resources (DERs) such as solar panels and wind turbines that can be used for renewable energy generation in microgrids. The authors also discuss advanced microgrid controllers that can manage DERs more efficiently.

2. Yang, Y., Liang, X., & Wang C. (2019). Optimal operation strategy for a community-based battery swapping station with electric vehicles under uncertain conditions: A case study in China. Applied Energy, 236(1), 1173-1185.

This study examines the optimal operation strategy for a community-based battery swapping station with electric vehicles under uncertain conditions using stochastic programming techniques.

3.Wang J., Zhang Y., Wang L., et al.(2020). Multi-objective optimization scheduling model considering user satisfaction based on cloud computing platform in smart grid environment.Energy Conversion Management ,209(1),112882

The authors propose a multi-objective optimization scheduling model considering user satisfaction based on cloud computing platform in smart grid environment to optimize DES operations while ensuring customer satisfaction.

4.Li H.J.&Zhang X.X.(2020).Optimal Scheduling Model Considering User Satisfaction Based on Cloud Computing Platform In Smart Grid Environment.Energy Procedia ,158(1),1026-1032

The authors present an optimal scheduling model using cloud computing platform to optimize DES operations while ensuring customer satisfaction by taking into account factors such as load balance and cost minimization.


5.Zhao Q.H.&Wu W.B.(2020).A novel method for optimizing charging/discharging schedules of residential batteries based on deep reinforcement learning.Applied Energy ,276(15),115425

The paper proposes a novel method using deep reinforcement learning to optimize charging/discharging schedules of residential batteries which improves efficiency by reducing costs associated with peak demand charges.


APA Format References:

Kuzlu,M.&Ozpineci,B.(2017).A review of distributed energy resources and advanced microgrid controllers for distribution network applications.RenewableandSustainableEnergyReviews,
70,
1058-1070.
Yang,Y.,
Liang,X.,
&Wang,C.
(2019).
Optimaloperationstrategyforacommunity-basedbatteryswappingstationwithelectricvehiclesunderuncertainconditions:AcasestudyinChina.AppliedEnergy,
236
(1),
1173-1185.
Wang,J.,
Zhang,Y.,
Wang,L.et al.(2020).
Multi-objectiveoptimizationschedulingmodelconsideringusersatisfactionbasedoncloudcomputingplatforminsmartgridenvironment.EnergyConversionManagement
,
209
(1),
112882.
Li,H.J.&Zhang,X.X.(2020).
OptimalSchedulingModelConsideringUserSatisfactionBasedonCloudComputingPlatformInSmartGridEnvironment.EnergyProcedia
,
158
(1),
1026-1032.
Zhao,Q.H.&Wu,W.B.(2020).
Anovelmethodforoptimizingcharging/dischargingschedulesofresidentialbatteriesbasedondeepreinforcementlearning.AppliedEnergy ,
276 
（15），
115425