Tolerance-Based Demand-Side Management for Load Shifting in Rural Areas of Southern Brazil

Henrique S. Eichkoff (), Daniel P. Bernardon, Julio A. Bitencourt, Vinícius J. Garcia, Daiana W. Silva, Lucas M. Chiara, Sebastian A. Butto, Solange M. K. Barbosa and Alejandre C. A. Pose
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Henrique S. Eichkoff: Center of Excellence in Energy and Power Systems, Federal University of Santa Maria, Avenida Roraima, 1000, Santa Maria 97105-340, Brazil
Daniel P. Bernardon: Center of Excellence in Energy and Power Systems, Federal University of Santa Maria, Avenida Roraima, 1000, Santa Maria 97105-340, Brazil
Julio A. Bitencourt: Center of Excellence in Energy and Power Systems, Federal University of Santa Maria, Avenida Roraima, 1000, Santa Maria 97105-340, Brazil
Vinícius J. Garcia: Center of Excellence in Energy and Power Systems, Federal University of Santa Maria, Avenida Roraima, 1000, Santa Maria 97105-340, Brazil
Daiana W. Silva: Companhia Paulista de Força e Luz Power Utility (CPFL Energia), Rodovia Engenheiro Miguel Noel Nascentes Burnier, 1755, Campinas 13088-900, Brazil
Lucas M. Chiara: Companhia Paulista de Força e Luz Power Utility (CPFL Energia), Rodovia Engenheiro Miguel Noel Nascentes Burnier, 1755, Campinas 13088-900, Brazil
Sebastian A. Butto: Siglasul Regulatory Consulting, Rua México, 51, Rio de Janeiro 20031-144, Brazil
Solange M. K. Barbosa: Siglasul Regulatory Consulting, Rua México, 51, Rio de Janeiro 20031-144, Brazil
Alejandre C. A. Pose: Siglasul Regulatory Consulting, Rua México, 51, Rio de Janeiro 20031-144, Brazil

Energies, 2023, vol. 16, issue 8, 1-34

Abstract: In the rural regions of southern Brazil, electricity is largely directed to irrigation activities on rice crops at restricted periods of the year. Typically, customers in these regions are called “irrigators”, and have some characteristics different from loads in urban centers, such as high demand levels and sharp load variations. These characteristics can result in problems of excessive loading on distribution grids at certain times of the day, generating concerns for the power utilities in relation to the security of the electrical system, energy supply to customers, and the integrity of electrical equipment. An alternative to avoid or mitigate these possible problems may be the application of a demand management model to irrigator customers. In this context, a load shifting strategy can be inserted to reduce demand in more critical periods and move it to intervals with lower load on the power grid. In this context, this article presents a demand-side management methodology in distribution systems located in rural areas, employing the load shifting strategy for irrigator customers. The methodology proposed in this paper is not an entirely novel approach, but one specifically developed for the context of irrigator customers, a subject little studied in the literature. The load management model proposed by this study is segmented into three hierarchical levels. The first level is the identification of the electrical characteristics of the distribution systems, the second level is the power flow analysis of the distribution networks, and the third and last level consists in the application of load shifting to the irrigator customers of these electrical systems. The load shifting strategy is modeled by a linear programming algorithm and is only applied to irrigator customers in situations of excessive loading on power grid. The case studies were conducted on three distribution systems of a power utility, with more than 150 irrigator customers. The DSM model based on the load shifting strategy reduced the maximum demand and daily load variations on the three rural feeders evaluated. The proposed changes in load patterns can ensure the continuity of electric power supply service in future even with the high concentration of load on distribution networks, benefiting customers and power utilities.

Keywords: demand; demand-side management; excessive loading; irrigator customers; load shifting; load variation (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2023
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