access icon free Double-deck optimal schedule of micro-grid based on demand-side response

In order to fully consume renewable energies and schedule demand-side resources more hierarchically, a double-deck optimal schedule model of micro-grid, which connects to power grids and concludes battery energy storage system (BESS), is proposed. Unlike the original peak-valley time-of-use (TOU) price, in the upper layer optimal schedule, the improved TOU price which takes account into the user's satisfaction can express the adjusted loads accurately and the resulting net loads can be treated as a link between upper and lower layer scheduling. For the reason of having no precise model of BESS, the lower model for the goal of minimising the operation cost is solved by action dependent heuristic dynamic programming algorithm that is not relying on the accurate controlled object model. This algorithm is used to obtain the most optimal performance index function and control strategy by the optimal iterative process, which is based on the back propagation neural network used for evaluating the optimal performance index. Analysis of examples and results has been presented to show the effectiveness of the proposed strategies.

Inspec keywords: backpropagation; neural nets; demand side management; power engineering computing; distributed power generation; power generation scheduling; dynamic programming; power generation economics; iterative methods; battery storage plants

Other keywords: upper layer optimal schedule; action dependent heuristic dynamic programming algorithm; optimal performance index control strategy; renewable energies; double-deck optimal schedule model; optimal iterative process; battery energy storage system; demand-side response; operation cost minimisation; demand-side resources; lower layer scheduling; back propagation neural network; improved TOU price; BESS; optimal performance index function; microgrid; upper layer scheduling; power grids

Subjects: Neural computing techniques; Interpolation and function approximation (numerical analysis); Distributed power generation; Power engineering computing; Power system management, operation and economics; Other power stations and plants; Interpolation and function approximation (numerical analysis); Optimisation techniques; Optimisation techniques

http://iet.metastore.ingenta.com/content/journals/10.1049/iet-rpg.2018.5495
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