Abstract
In the last years, the distribution network operators (DNOs) assumed transition strategies of the electric distribution networks (EDNs) towards the active areas of the microgrids where, regardless of the operating regimes, flexibility, economic efficiency, low power losses, and high power quality are ensured. Artificial intelligence techniques, combined with the smart devices and real-time remote communication solutions of the enormous data amounts, can represent the starting point in establishing decision-making strategies to solve one of the most important challenges related to phase load balancing (PLB). In this context, the purpose of the paper is to prove that a decision-making strategy based on a limited number of PLB devices installed at the consumers (small implementation degree) leads to similar technical benefits as in the case of full implementation in the EDNs. Thus, an original bi-level PLB methodology, considering a clustering-based selection criterion of the consumers for placement of the switching devices, was proposed. A real EDN from a rural area belonging to a Romanian DNO has been considered in testing the proposed methodology. An implementation degree of the PLB devices in the EDN by 17.5% represented the optimal solution, leading to a faster computational time with 43% and reducing the number of switching operations by 92%, compared to a full implementation degree (100%). The performance indicators related to the unbalance factor and energy-saving highlighted the efficiency of the proposed methodology.
Highlights
Future electric distribution networks (EDNs) will cover the supply areas corresponding to the low voltage (LV) or medium voltage (MV) distribution feeders, which can be associated with the microgrids, each requiring efficient energy management [1]
The results indicates that this solution is efficient, having a reduced cost in their implementation
The methodology is applied for a single EDN, with the possibility to be extended for a multi-EDN system (MμG system), provided that the EDNs have the same supply point connected to the distribution network of the distribution network operators (DNOs) to achieve the proposed objective, namely the minimization of the unbalance factor at the SP level
Summary
Future electric distribution networks (EDNs) will cover the supply areas corresponding to the low voltage (LV) or medium voltage (MV) distribution feeders, which can be associated with the microgrids (μGs), each requiring efficient energy management [1]. Investments in the technical infrastructure of the actual EDNs to achieve flexibility able to adapt to new requirements; increasing the automation degree in the EDNs to ensure the resilience and integration of the distributed generation sources for an optimal operation; optimizing the topology to ensure continuity in the energy supply of the consumers In this context, the directions assumed by the DNOs aim to bring the EDNs from the passive in the active area, where Artificial Intelligence techniques, combined with advanced technologies and real-time remote communication solutions of the enormous data amounts, led to the development of the concepts, equipment, technologies, and solutions associated with the “smart grids” concept. These systems will provide support to operate optimal the technical infrastructure [4]
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