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Academic Journal of Energy, 2020, 1(3); doi: 10.38007/RE.2020.010305.

Regional Distributed Energy System Planning Based on Integration Theory

Author(s)

Tredenick Julius

Corresponding Author:
Tredenick Julius
Affiliation(s)

Warsaw Univ Technol, Inst Elect Syst, Nowowiejska 15-19, PL-00665 Warsaw, Poland

Abstract

In many countries and regions, distributed energy systems have become a mature energy-saving technology. The system has the characteristics of being close to users, making full use of energy, protecting the environment and providing reliable power. The purpose of this paper is to study regional distributed energy system planning based on integration theory. The regional energy planning should be carried out according to the following steps. First, predict the regional load, then determine the available resource conditions of the region, evaluate and analyze the supply side of the region, and select a reasonable energy allocation plan according to the analysis results. Based on the energy planning of the region, an optimal allocation model based on the maximum net benefit function is established. It can be seen from the analysis of the actual cases of residential quarters that the annual income is kept at the maximum when the load characteristics of cooling and heating users are known.

Keywords

Integration Theory, District Energy, Distributed Energy, System Planning

Cite This Paper

Tredenick Julius. Regional Distributed Energy System Planning Based on Integration Theory. Academic Journal of Energy (2020), Vol. 1, Issue 3: 34-41. https://doi.org/10.38007/RE.2020.010305.

References

[1] Nabavi S A ,  Motlagh N H ,  Zaidan M A , et al. Deep Learning in Energy Modeling: Application in Smart Buildings With Distributed Energy Generation. IEEE Access, 2020, PP(99):1-1.

[2] Wirth T V ,  Gislason L ,  Seidl R . Distributed energy systems on a neighborhood scale: Reviewing drivers of and barriers to social acceptance. Renewable & Sustainable Energy Reviews, 2018, 82(PT.3):2618-2628.

[3] Mavromatidis G ,  Orehounig K ,  Carmeliet J . Uncertainty and global sensitivity analysis for the optimal design of distributed energy systems. Applied Energy, 2018, 214(MAR.15):219-238.

[4] Horvath D ,  Szabo R Z . Evolution of photovoltaic business models: Overcoming the main barriers of distributed energy deployment. Renewable and Sustainable Energy Reviews, 2018, 90(JUL.):623-635.

[5] Kalantar-Neyestanaki M ,  Cherkaoui R . Coordinating Distributed Energy Resources and Utility-Scale Battery Energy Storage System for Power Flexibility Provision Under Uncertainty. IEEE Transactions on Sustainable Energy, 2020, PP(99):1-1.

[6] Olivella-Rosell P ,  Bullich-Massague E ,  Aragues-Penalba M , et al. Optimization problem for meeting distribution system operator requests in local flexibility markets with distributed energy resources. Applied Energy, 2018, 210(jan.15):881-895.

[7] Ferraz R ,  Ferraz R ,  Rueda-Medina A C , et al. Genetic optimisation-based distributed energy resource allocation and recloser-fuse coordination. IET Generation Transmission & Distribution, 2020, 14(20):4501-4508.

[8] Nassif A B . A protection and grounding strategy for integrating inverter-based distributed energy resources in an isolated microgrid. CPSS Transactions on Power Electronics and Applications, 2020, 5(3):242-250.

[9] Gilani M A ,  Kazemi A ,  Ghasemi M . Distribution system resilience enhancement by microgrid formation considering distributed energy resources. Energy, 2020, 191(Jan.15):116442.1-116442.13.

[10] Lee S ,  Choi D H . Federated Reinforcement Learning for Energy Management of Multiple Smart Homes with Distributed Energy Resources. IEEE Transactions on Industrial Informatics, 2020, PP(99):1-1.

[11] Nizami M ,  Hossain M J ,  Fernandez E . Multiagent-Based Transactive Energy Management Systems for Residential Buildings With Distributed Energy Resources. IEEE Transactions on Industrial Informatics, 2020, 16(3):1836-1847.

[12] Fernandez I ,  Uribe-Perez N ,  Eizmendi I , et al. Characterization of non-intentional emissions from distributed energy resources up to 500 kHz: A case study in Spain. International Journal of Electrical Power & Energy Systems, 2019, 105(FEB.):549-563.

[13] Mishra R ,  Banerjee U ,  Sekhar T , et al. Development and implementation of control of stand-alone PMSG-based distributed energy system with variation in input and output parameters. Electric Power Applications, IET, 2019, 13(10):1497-1506.

[14] Nazaripouya H ,  Pota H R ,  Chu C C , et al. Real-Time Model-Free Coordination of Active and Reactive Powers of Distributed Energy Resources to Improve Voltage Regulation in Distribution Systems. IEEE Transactions on Sustainable Energy, 2019, PP(99):1-1.

[15] Seyedi Y ,  Karimi H ,  Grijalva S . Irregularity Detection in Output Power of Distributed Energy Resources Using PMU Data Analytics in Smart Grids. IEEE transactions on industrial informatics, 2019, 15(4):2222-2232.

[16] Alvarado D ,  Moreira A ,  Moreno R , et al. Transmission Network Investment With Distributed Energy Resources and Distributionally Robust Security. IEEE Transactions on Power Systems, 2019, 34(6):5157-5168.

[17] Kaplan P O ,  Witt J W . What is the role of distributed energy resources under scenarios of greenhouse gas reductions? A specific focus on combined heat and power systems in the industrial and commercial sectors. Applied Energy, 2019, 235(FEB.1):83-94.

[18] Ahl A ,  Yarime M ,  Tanaka K , et al. Review of blockchain-based distributed energy: Implications for institutional development. Renewable and Sustainable Energy Reviews, 2019, 107(JUN.):200-211.

[19] Dobbe R ,  Sondermeijer O ,  Fridovich-Keil D , et al. Toward Distributed Energy Services: Decentralizing Optimal Power Flow With Machine Learning. IEEE Transactions on Smart Grid, 2019, PP(99):1-1.