Abstract
The inherent variability of large-scale renewable energy generation leads to significant difficulties in microgrid energy management. Likewise, the effects of human behaviors in response to the changes in electricity tariffs as well as seasons result in changes in electricity consumption. Thus, proper scheduling and planning of power system operations require accurate load demand and renewable energy generation estimation studies, especially for short-term periods (hour-ahead, day-ahead). The time-sequence variation in aggregated electrical load and bulk photovoltaic power output are considered in this study to promote the supply-demand balance in the short-term optimal operational scheduling framework of a reconfigurable microgrid by integrating the forecasting results. A bi-directional long short-term memory units based deep recurrent neural network model, DRNN Bi-LSTM, is designed to provide accurate aggregated electrical load demand and the bulk photovoltaic power generation forecasting results. The real-world data set is utilized to test the proposed forecasting model, and based on the results, the DRNN Bi-LSTM model performs better in comparison with other methods in the surveyed literature. Meanwhile, the optimal operational scheduling framework is studied by simultaneously making a day-ahead optimal reconfiguration plan and optimal dispatching of controllable distributed generation units which are considered as optimal operation solutions. A combined approach of basic and selective particle swarm optimization methods, PSO&SPSO, is utilized for that combinatorial, non-linear, non-deterministic polynomial-time-hard (NP-hard), complex optimization study by aiming minimization of the aggregated real power losses of the microgrid subject to diverse equality and inequality constraints. A reconfigurable microgrid test system that includes photovoltaic power and diesel distributed generators is used for the optimal operational scheduling framework. As a whole, this study contributes to the optimal operational scheduling of reconfigurable microgrid with electrical energy demand and renewable energy forecasting by way of the developed DRNN Bi-LSTM model. The results indicate that optimal operational scheduling of reconfigurable microgrid with deep learning assisted approach could not only reduce real power losses but also improve system in an economic way.
Highlights
Traditional power systems require innovation to bridge the gap between demand and supply while overcome essential challenges such as grid reliability, grid robustness, customer electricity cost minimization, etc
Forecasting Results for the Optimal Operational Scheduling Problem It is considered that a bulk PV energy generation system is integrated into bus number 6 on the recoTnhfiegruersaublltes iMn nGofromratlhizisedshaonrdt‐utenrnmorompaelrizaetidodnaatlasachreerdeuploinrtgedstiundtyh.isAsescptiuotnf.oTrhthe riensuTlatbslme e1a, sduireesdel oDnGpseawkihthou4rsMoWf eatochtadl amyaixnismteuamd orfeaalll p24owhearrecaaplsaocirteypoorpteerda.te“dN”atanadu“nUity” dpeonwoeter tfhaectnoorrhmaaslibzeeedn ainndstualnlnedoromnadliizfefedrednattabruessepse.ctively
To estimate aggregated power demand and the bulk PV output power over the short-term time period, a DRNN Bi-LSTM model has been proposed since conventional forecasting methods have limitations in modeling complex nonlinear problems and cannot take into consideration the time dependencies in the data set
Summary
Traditional power systems require innovation to bridge the gap between demand and supply while overcome essential challenges such as grid reliability, grid robustness, customer electricity cost minimization, etc. Reference [40] puts forth a method for optimal scheduling and operation of load aggregators with electric energy storage in power markets to schedule the imported power in each period of the day with day-ahead forecasted price and load It has been observed upon examining the power system operational scheduling and planning studies that the simultaneous approach of NR and OD of DG units has not been performed as in [28]. A DRNN model based on Bi-LSTM units has been developed as a significant contribution of this study to forecast the aggregated electrical power load and bulk PV power output of reconfigurable MG during a short-term period and the net load profile is considered as a sum of demand consumption and PV power generation for each hour in a day.
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