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Analyses of Distributed Generation and Storage Effect on the Electricity Consumption Curve in the Smart Grid Context

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  • Simona-Vasilica Oprea

    (Department of Economic Informatics and Cybernetics, Bucharest Academy of Economic Studies, Romana Square 6, Bucharest 010374, Romania)

  • Adela Bâra

    (Department of Economic Informatics and Cybernetics, Bucharest Academy of Economic Studies, Romana Square 6, Bucharest 010374, Romania)

  • Adina Ileana Uță

    (Department of Economic Informatics and Cybernetics, Bucharest Academy of Economic Studies, Romana Square 6, Bucharest 010374, Romania)

  • Alexandru Pîrjan

    (Department of Informatics, Statistics and Mathematics, Romanian-American University, Expoziției 1B, Bucharest 012101, Romania)

  • George Căruțașu

    (Department of Informatics, Statistics and Mathematics, Romanian-American University, Expoziției 1B, Bucharest 012101, Romania)

Abstract

The householders’ electricity consumption is about 20–30% of the total consumption that is a significant space for demand response. Mainly, the householders are becoming more and more active and interested in diminishing their expenses related to the electricity consumption, considering different rates of the advanced tariffs. Therefore, in the smart grid context, especially for prosumers with energy sources and storage devices (SD), the electricity consumption optimization becomes attractive since they obtain significant benefits. On the other hand, the electricity suppliers design appropriate tariffs in order to reduce the consumption peaks and avoid the occurrence of new peaks. Based on the effect of these tariffs on consumers’ behavior, the stress on generators decreases and the electricity suppliers improve the demand forecast and adjust their strategies on the market. In addition, the grid operators are interested in the minimization of the consumption peak that leads to loss reduction and avoidance of congestions that would ensure at least the delay of the onerous investment in grid capacities. In this paper, we will run several scenarios for electricity consumption optimization in the context of smart grid that includes: sensors, actuators, smart meters, advanced tariff schemes, smart appliances and electricity home control applications. Our goal is to analyze the effect of the Renewable Energy Systems (RES) distributed generation (such as photovoltaic panels—PV) and storage on the consumption curve. The results show that consumption optimization with RES distributed generation and SD brings sustainable development of the power systems and significant benefits from the consumption peak and savings point of view.

Suggested Citation

  • Simona-Vasilica Oprea & Adela Bâra & Adina Ileana Uță & Alexandru Pîrjan & George Căruțașu, 2018. "Analyses of Distributed Generation and Storage Effect on the Electricity Consumption Curve in the Smart Grid Context," Sustainability, MDPI, vol. 10(7), pages 1-25, July.
    • Handle: RePEc:gam:jsusta:v:10:y:2018:i:7:p:2264-:d:155577
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    References listed on IDEAS

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    1. Ion LUNGU & Adela BÂRA & George CĂRUTASU & Alexandru PÎRJAN, & Simona-Vasilica OPREA, 2016. "Prediction Intelligent System In The Field Of Renewable Energies Through Neural Networks," ECONOMIC COMPUTATION AND ECONOMIC CYBERNETICS STUDIES AND RESEARCH, Faculty of Economic Cybernetics, Statistics and Informatics, vol. 50(1), pages 85-102.
    2. Yongli Wang & Yujing Huang & Yudong Wang & Fang Li & Yuanyuan Zhang & Chunzheng Tian, 2018. "Operation Optimization in a Smart Micro-Grid in the Presence of Distributed Generation and Demand Response," Sustainability, MDPI, vol. 10(3), pages 1-25, March.
    3. Chang Ye & Shihong Miao & Qi Lei & Yaowang Li, 2016. "Dynamic Energy Management of Hybrid Energy Storage Systems with a Hierarchical Structure," Energies, MDPI, vol. 9(6), pages 1-15, May.
    4. Panagiotis Michalitsakos & Lucian Mihet-Popa & George Xydis, 2017. "A Hybrid RES Distributed Generation System for Autonomous Islands: A DER-CAM and Storage-Based Economic and Optimal Dispatch Analysis," Sustainability, MDPI, vol. 9(11), pages 1-16, November.
    5. Omowunmi Mary Longe & Khmaies Ouahada & Suvendi Rimer & Ashot N. Harutyunyan & Hendrik C. Ferreira, 2017. "Distributed Demand Side Management with Battery Storage for Smart Home Energy Scheduling," Sustainability, MDPI, vol. 9(1), pages 1-13, January.
    6. Shirazi, Elham & Jadid, Shahram, 2017. "Cost reduction and peak shaving through domestic load shifting and DERs," Energy, Elsevier, vol. 124(C), pages 146-159.
    7. Uddin, Moslem & Romlie, Mohd Fakhizan & Abdullah, Mohd Faris & Abd Halim, Syahirah & Abu Bakar, Ab Halim & Chia Kwang, Tan, 2018. "A review on peak load shaving strategies," Renewable and Sustainable Energy Reviews, Elsevier, vol. 82(P3), pages 3323-3332.
    8. Ampimah, Benjamin Chris & Sun, Mei & Han, Dun & Wang, Xueyin, 2018. "Optimizing sheddable and shiftable residential electricity consumption by incentivized peak and off-peak credit function approach," Applied Energy, Elsevier, vol. 210(C), pages 1299-1309.
    9. Zheng, Menglian & Meinrenken, Christoph J. & Lackner, Klaus S., 2015. "Smart households: Dispatch strategies and economic analysis of distributed energy storage for residential peak shaving," Applied Energy, Elsevier, vol. 147(C), pages 246-257.
    10. Hun-Chul Seo, 2018. "Development of Reclosing Method in a Distribution System with Distributed Generation and Battery Energy Storage System," Energies, MDPI, vol. 11(6), pages 1-18, May.
    11. Alexandru Pîrjan & Simona-Vasilica Oprea & George Căruțașu & Dana-Mihaela Petroșanu & Adela Bâra & Cristina Coculescu, 2017. "Devising Hourly Forecasting Solutions Regarding Electricity Consumption in the Case of Commercial Center Type Consumers," Energies, MDPI, vol. 10(11), pages 1-36, October.
    12. Reihani, Ehsan & Motalleb, Mahdi & Ghorbani, Reza & Saad Saoud, Lyes, 2016. "Load peak shaving and power smoothing of a distribution grid with high renewable energy penetration," Renewable Energy, Elsevier, vol. 86(C), pages 1372-1379.
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    2. Byuk-Keun Jo & Seungmin Jung & Gilsoo Jang, 2019. "Feasibility Analysis of Behind-the-Meter Energy Storage System According to Public Policy on an Electricity Charge Discount Program," Sustainability, MDPI, vol. 11(1), pages 1-17, January.
    3. Leocadio Hontoria & Catalina Rus-Casas & Juan Domingo Aguilar & Jesús C. Hernandez, 2019. "An Improved Method for Obtaining Solar Irradiation Data at Temporal High-Resolution," Sustainability, MDPI, vol. 11(19), pages 1-15, September.
    4. Alain Aoun & Hussein Ibrahim & Mazen Ghandour & Adrian Ilinca, 2019. "Supply Side Management vs. Demand Side Management of a Residential Microgrid Equipped with an Electric Vehicle in a Dual Tariff Scheme," Energies, MDPI, vol. 12(22), pages 1-21, November.

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