Show Me!: Large-Scale Smart Grid Demonstrations for European Distribution Networks

Varela, J.; Puglisi, Lisandro J.; Wiedemann, Thomas; Ysberg, Ulf; Stein, Daniele; Pokorna, Zdenka; Arnoult, Christophe; Garaud-Verdier, Remy; Consiglio, Lilia

doi:10.1109/mpe.2014.2363536

Cited by 14 publications

(4 citation statements)

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“…The network and substation costs and capacities were determined based on inputs from ENWL and conventional AC power flow calculations (MatPower [44] was used; however, other tools could be used). For this purpose the hourly power flows of the network were sequentially simulated throughout a year (i.e., 8760 simulations) in light of the demand growth scenarios (three scenarios for 45 years) 1 . Firm capacity was thus determined based on thermal and voltage limits, and security considerations based on P2/6 criteria [23] (i.e., assuming the potential failure of the lines connected to the point of connection) and emergency ratings (i.e., the elements can withstand a 20% overload above their firm capacity for up to two hours within a 24 hours period).…”

Section: Case Study Descriptionmentioning

confidence: 99%

“…IGNIFICANT focus has recently been placed on the importance of distribution networks as a means to facilitate meeting environmental and security of supply targets [1] [2]. Distribution networks are thus expected to accommodate different low carbon technologies and increased demand (e.g., due to the electrification of the heating sector [3]), while maintaining high reliability levels needed to foster economic growth.…”

Section: Introductionmentioning

confidence: 99%

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Practical recursive algorithms and flexible open-source applications for planning of smart distribution networks with Demand Response

Ceseña

Mancarella

2016

Sustainable Energy, Grids and Networks

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-Distribution networks are currently undergoing fundamental changes due to the rise of smart solutions such as for instance Demand Response (DR), which increases network complexity and challenges the adequacy of traditional planning practices. This calls for the use of suitable planning methodologies. However, the planning problem may be too cumbersome for most commercial software tools, or may lead to complex bespoke optimisation models that may not be easy to use by network planners. In this light, this work proposes a recursive function that can be used in practical algorithms for planning of smart distribution networks. The recursive function can emulate business-as-usual planning practices and further optimise them, including DR options as potential substitutes for network reinforcement. Several case studies based on real UK networks highlight the robustness and flexibility of the proposed algorithms to address different problems, including uncertainty analysis and risk management. The results clearly show that the proposed tool leads to increased economic and social benefits, particularly when optimising investment strategies considering smart DR solutions. The model is herein distributed open-source as accompanying material to this paper with the aim of encouraging new practices required for smart distribution network planning beyond the traditional academic applications.

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Section: Case Study Descriptionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Practical recursive algorithms and flexible open-source applications for planning of smart distribution networks with Demand Response

Ceseña

Mancarella

2016

Sustainable Energy, Grids and Networks

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“…A dedicated platform using asymmetric digital subscriber line infrastructure will control the operation of these residential BESS units and power usage of individual clients. Every two minutes, the smart meter will send client power consumption and generation data to the control box in the actual time [4].…”

Section: Introductionmentioning

confidence: 99%

Performance Comparison of Artificial Intelligence Approaches for Battery Energy Storage Size Optimization in PV Micro-grid

Kerdphol¹,

Fuji²,

Mitani³

et al. 2016

JOCET

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Abstract-Studies into the determining size of battery energy storage system (BESS) has become significant recently, owing to their use in a variety of complex, high performance and energy storage system applications. This paper presents a comparative study of optimization techniques between particle swarm optimization (PSO) and artificial neural network (ANN) for evaluating the optimum size of BESS in the micro-grid system. In this paper, the micro-grid system consists of two micro turbine systems, solar photovoltaic (PV) system and BESS, and it is connected to the utility grid. Simulation results show that the optimal size of BESS-based PSO approach achieves the lowest performance in achieving the optimal BESS size compared to the optimal size of BESS-based ANN. However, the optimal sizing of BESS-based ANN approach gives the fastest conversion time compared to the optimal sizing of BESS-based PSO.Index Terms-Artificial neural network (ANN), battery energy storage system, frequency control, micro-grid, particle swarm optimization.

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“…Distribution companies, ie, distribution system operators (DSOs) in the European context, have been gradually adopting different automation solutions, increasing the degree of monitoring and telecontrol in their networks, 2,3 and have launched numerous pilot projects testing these smart solutions with the aim of understanding the impact and implications of a large-scale deployment. [4][5][6][7][8][9][10][11][12] Alone in Europe, the Joint Research Center has reported an accumulated investment of 300 million € on smart network management research and development projects and over 560 million € on smart network management demonstration and deployment projects. 13 Network automation comprises communications and intelligence, and the implementation of automation at MV level enables remote network monitoring, remote control of switchgear, and remote reconfiguration of the network.…”

mentioning

confidence: 99%

Assessing the potential of MV automation for distribution network reliability improvement

Rodriguez-Calvo

Cossent

Frías

2017

Int Trans Electr Energ Syst

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Summary Distribution network automation is a key functionality in the evolution towards smarter electricity grids. The main driver to deploy this kind of smart grid solutions is the improvement of continuity of supply for network users; thanks to the fact that an enhanced network monitoring and telecontrol can improve the fault management process conducted by distribution system operators. However, the parameters that affect their successful implementation are not completely understood yet. Addressing this gap, this paper presents a detailed analysis to identify the main factors affecting the implementation of automation at medium voltage distribution networks and discusses the regulatory implications. Several realistic case studies are analyzed, corresponding to a number distribution networks that resemble the architectural characteristics representative of the European distribution grids in different contexts. Hence, the results allow deriving general conclusions that can be widely applicable to European distribution networks.

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Show Me!: Large-Scale Smart Grid Demonstrations for European Distribution Networks

Cited by 14 publications

References 0 publications

Practical recursive algorithms and flexible open-source applications for planning of smart distribution networks with Demand Response

Practical recursive algorithms and flexible open-source applications for planning of smart distribution networks with Demand Response

Performance Comparison of Artificial Intelligence Approaches for Battery Energy Storage Size Optimization in PV Micro-grid

Assessing the potential of MV automation for distribution network reliability improvement

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