Conference paper
Probabilistic prosumer node modeling for estimating planning parameters in distribution networks with renewable energy sources
With the increase in distributed generation, the demand-only nature of many secondary substation nodes in medium voltage networks is becoming a mix of temporally varying consumption and generation with significant stochastic components. Traditional planning, however, has often assumed that the maximum demands of all connected substations are fully coincident, and in cases where there is local generation, the conditions of maximum consumption and minimum generation, and maximum generation and minimum consumption are checked, again assuming unity coincidence.
Statistical modelling is used in this paper to produce network solutions that optimize investment, running and interruption costs, assessed from a societal perspective. The decoupled utilization of expected consumption profiles and stochastic generation models enables a more detailed estimation of the driving parameters using the Monte Carlo simulation method.
A planning algorithm that optimally places backup connections and three layers of switching has, for real-scale distribution networks, to make millions of iterations within iterations to form a solution, and therefore cannot computationally afford millions of parallel load flows in each iteration. The interface that decouples the full statistical modelling of the combinatorial challenge of prosumer nodes with such a planning algorithm is the main offering of this paper.
Language: | English |
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Publisher: | IEEE |
Year: | 2018 |
Pages: | 1-8 |
Proceedings: | 2017 IEEE 58th International Scientific Conference on Power and Electrical Engineering of Riga Technical University |
ISBN: | 1538638444 , 1538638460 , 1538638479 , 9781538638446 , 9781538638460 and 9781538638477 |
Types: | Conference paper |
DOI: | 10.1109/RTUCON.2017.8124833 |
ORCIDs: | Koivisto, Matti Juhani |
Conferences Distributed Generation Distribution Network Planning Electrical engineering Monte Carlo Simulation Monte Carlo simulation method Planning Statistical Load Analysis Wind generation Analysis Wind power generation backup connections connected substations decoupled utilization demand-only nature distributed generation distributed power generation driving parameters expected consumption profiles interruption costs investment iteration method load flow local generation maximum demands medium voltage networks planning algorithm planning parameters power distribution economics power distribution planning power distribution reliability power grids probabilistic prosumer node real-scale distribution networks renewable energy sources secondary substation nodes statistical modelling stochastic components stochastic generation models stochastic processes substations temporally varying consumption unity coincidence