Bi-Level Phase Load Balancing Methodology with Clustering-Based Consumers’ Selection Criterion for Switching Device Placement in Microgrids

In the last years, the Distribution Grid Operators (DGOs) assumed transition strategies of the distribution grids towards an active area associated with the "Smart Grids" concept. They are considering the use of Artificial Intelligence techniques, combined with advanced technologies and real-time remote communication solutions of the enormous data amounts, to develop smart solutions into the small size distribution grids, also called microgrids (μGs). These solutions will provide support for the DGOs to ensure an optimal operation of the technical infrastructure of the μGs. In this context, a bi-level methodology for solving the phase load balancing problem in the μGs with complex topologies and a high number of single-phase consumers, considering a clustering-based selection criterion of the consumers for placement of the switching devices, was proposed in the paper. A real μG from a rural area, with 114 consumers integrated into the Smart Metering System (SMS), belonging to the DGO from Romania, was considered in testing the proposed methodology. An implementation degree of 17.5%, corresponding to the phase load balancing equipment installed to only 20 consumers from the μG, led to a faster computational time with 43% and reducing the number of switching operations by 92% than in the case of a full implementation degree (100%). The performance indicators related to the unbalance factor and energy-saving used in the evaluation of the technical benefits highlighted the efficiency of the proposed methodology.

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Bi-Level Phase Load Balancing Methodology with Clustering-Based Consumers’ Selection Criterion for Switching Device Placement in Low Voltage Distribution Networks

Mathematics ◽

10.3390/math9050542 ◽

2021 ◽

Vol 9 (5) ◽

pp. 542

Author(s):

Gheorghe Grigoraș ◽

Bogdan-Constantin Neagu ◽

Florina Scarlatache ◽

Livia Noroc ◽

Ecaterina Chelaru

Keyword(s):

Decision Making ◽

Load Balancing ◽

Low Voltage ◽

Selection Criterion ◽

Distribution Networks ◽

Computational Time ◽

Smart Devices ◽

Remote Communication ◽

Full Implementation ◽

Starting Point

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.

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A Two-Step State Estimation Algorithm for Hybrid AC-DC Distribution Grids

Energies ◽

10.3390/en14071967 ◽

2021 ◽

Vol 14 (7) ◽

pp. 1967

Author(s):

Gaurav Kumar Roy ◽

Marco Pau ◽

Ferdinanda Ponci ◽

Antonello Monti

Keyword(s):

State Estimation ◽

Distribution System ◽

Renewable Energy Sources ◽

Estimation Algorithm ◽

The State ◽

Second Step ◽

Distribution Grid ◽

Dc Distribution ◽

Attractive Option ◽

Distribution Grids

Direct Current (DC) grids are considered an attractive option for integrating high shares of renewable energy sources in the electrical distribution grid. Hence, in the future, Alternating Current (AC) and DC systems could be interconnected to form hybrid AC-DC distribution grids. This paper presents a two-step state estimation formulation for the monitoring of hybrid AC-DC grids. In the first step, state estimation is executed independently for the AC and DC areas of the distribution system. The second step refines the estimation results by exchanging boundary quantities at the AC-DC converters. To this purpose, the modulation index and phase angle control of the AC-DC converters are integrated into the second step of the proposed state estimation formulation. This allows providing additional inputs to the state estimation algorithm, which eventually leads to improve the accuracy of the state estimation results. Simulations on a sample AC-DC distribution grid are performed to highlight the benefits resulting from the integration of these converter control parameters for the estimation of both the AC and DC grid quantities.

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Improving PV Resilience by Dynamic Reconfiguration in Distribution Grids: Problem Complexity and Computation Requirements

Energies ◽

10.3390/en14040830 ◽

2021 ◽

Vol 14 (4) ◽

pp. 830

Author(s):

Filipe F. C. Silva ◽

Pedro M. S. Carvalho ◽

Luís A. F. M. Ferreira

Keyword(s):

Distributed Generation ◽

Optimal Solution ◽

Dynamic Reconfiguration ◽

Scheduling Problem ◽

Low Carbon ◽

Distribution Grid ◽

Medium Voltage ◽

Classical Computing ◽

The Impact ◽

Distribution Grids

The dissemination of low-carbon technologies, such as urban photovoltaic distributed generation, imposes new challenges to the operation of distribution grids. Distributed generation may introduce significant net-load asymmetries between feeders in the course of the day, resulting in higher losses. The dynamic reconfiguration of the grid could mitigate daily losses and be used to minimize or defer the need for network reinforcement. Yet, dynamic reconfiguration has to be carried out in near real-time in order to make use of the most updated load and generation forecast, this way maximizing operational benefits. Given the need to quickly find and update reconfiguration decisions, the computational complexity of the underlying optimal scheduling problem is studied in this paper. The problem is formulated and the impact of sub-optimal solutions is illustrated using a real medium-voltage distribution grid operated under a heavy generation scenario. The complexity of the scheduling problem is discussed to conclude that its optimal solution is infeasible in practical terms if relying upon classical computing. Quantum computing is finally proposed as a way to handle this kind of problem in the future.

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Progresses in Analytical Design of Distribution Grids and Energy Storage

Energies ◽

10.3390/en14144270 ◽

2021 ◽

Vol 14 (14) ◽

pp. 4270

Author(s):

Gianpiero Colangelo ◽

Gianluigi Spirto ◽

Marco Milanese ◽

Arturo de Risi

Keyword(s):

Renewable Energy ◽

Power Flow ◽

State Of The Art ◽

Renewable Energy Sources ◽

Energy Sources ◽

Distribution Grid ◽

Mathematical Methods ◽

Power Generators ◽

Bidirectional Power Flow ◽

Distribution Grids

In the last years, a change in the power generation paradigm has been promoted by the increasing use of renewable energy sources combined with the need to reduce CO2 emissions. Small and distributed power generators are preferred to the classical centralized and sizeable ones. Accordingly, this fact led to a new way to think and design distributions grids. One of the challenges is to handle bidirectional power flow at the distribution substations transformer from and to the national transportation grid. The aim of this paper is to review and analyze the different mathematical methods to design the architecture of a distribution grid and the state of the art of the technologies used to produce and eventually store or convert, in different energy carriers, electricity produced by renewable energy sources, coping with the aleatory of these sources.

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