Optimal placement of data concentrators for expansion of the smart grid communications network

Abstract

Evolving power systems with increasing renewables penetration, along with the development of the smart grid, calls for improved communication networks to support these distributed generation sources. Automatic and optimal placement of communication resources within the advanced metering infrastructure is critical to provide a high-performing, reliable, and resilient power system. Three network design formulations based on mixed-integer linear and non-linear programming approaches are proposed to minimise network congestion by optimising residual buffer capacity through the placement of data concentrators and network routeing. Results on a case study show that the proposed models improve network connectivity and robustness, and increase average residual buffer capacity. Maximising average residual capacity alone, however, results in both oversaturated and underutilised nodes, while maximising either minimum residual capacity or total reciprocal residual capacity can yield much-improved network load allocation. Consideration of connection redundancy improves network reliability further by ensuring quality-of-service in the event of an outage. Analysis of multi-period network expansion shows that the models do not deviate significantly from optimal when used progressively (within 5% deviation), and are effective for utility planners to use for smart grid expansion.

Towards an Efficient Method for Large-Scale Wi-SUN-Enabled AMI Network Planning

“…For the positioning of concentrators in problems of smart grid networks expansion, the authors in [ 17 ] used mixed-integer non-linear programming (MINLP) and mixed-integer linear programming approaches (MILP) to minimize network congestion by optimizing residual buffer capacity by positioning data concentrators and network routing. They proposed an approach for adding new devices to AMI networks that are already in operation, maximizing the quality of service (QoS) performance for customers, providing redundant connectivity in cases of security threat or interruption in power supply, and assisting in network expansion.…”

“…This problem is generally classified as an optimization problem because of the number of equipment, connection possibilities, constraints, and objectives. Regarding the problem formulation, some authors cite that this problem can be modeled as a linear/non-linear programming problem, as expressed by [ 17 , 21 , 29 , 30 , 32 ]. In addition, some authors classify the problem as a set covering problem [ 29 , 31 , 33 ], or as a facility location problem [ 17 , 18 , 34 ].…”

“…Regarding the problem formulation, some authors cite that this problem can be modeled as a linear/non-linear programming problem, as expressed by [ 17 , 21 , 29 , 30 , 32 ]. In addition, some authors classify the problem as a set covering problem [ 29 , 31 , 33 ], or as a facility location problem [ 17 , 18 , 34 ]. Metaheuristic approaches, including genetic algorithms, are also explored [ 21 , 34 ], demonstrating that various strategies can be used to solve the problem.…”

Greedy Genetic Algorithm for the Data Aggregator Positioning Problem in Smart Grids

“…Trabalhos relacionados já adaptaram esse problema como um SCP [22] ou vertex-covering problem [8] e até mesmo modelos de otimização adaptados [1], todos visando a redução dos custos de implementação e expansão, assim como este trabalho. Por outro lado, há trabalhos na literatura que visam a melhoria do sinal entre os componentes da REI [25] ou melhor estabilidade da rede [23].…”

Otimização do posicionamento de inteligentes em redes elétricas de baixa tensão baseados em tecnologia ZigBee

“…Assim, em diversos países, é comum utilizar concentradores inteligentes que agreguem as informações individuais do consumo de energia e demanda elétrica, provenientes de grupos de medidores inteligentes nos pontos de consumo. (Gu et al, 2019;Murugesan, Gunasekar, Rathinadurai Louis, Atla, & Shanmugham, 2015), (Zhen, Elgindy, Alam, Hodge, & Laird, 2019). Os concentradores devem preferencialmente permitir a troca bidirecional de informações entre os medidores no ponto de consumo final e o centro de controle para possibilitar, além dos serviços de monitoramento de consumo e falhas, serviços de controle de cargas e reposta da demanda.…”