Abstract
This paper proposes an efficient power management approach for the 24 h-ahead optimal maneuver of Mega–scale grid–connected microgrids containing a huge penetration of wind power, dispatchable distributed generation (diesel generator), energy storage system and local loads. The proposed energy management optimization objective aims to minimize the microgrid expenditure for fuel, operation and maintenance and main grid power import. It also aims to maximize the microgrid revenue by exporting energy to the upstream utility grid. The optimization model considers the uncertainties of the wind energy and power consumptions in the microgrids, and appropriate forecasting techniques are implemented to handle the uncertainties. The optimization model is formulated for a day-ahead optimization timeline with one-hour time steps, and it is solved using the ant colony optimization (ACO)-based metaheuristic approach. Actual data and parameters obtained from a practical microgrid platform in Atlanta, GA, USA are employed to formulate and validate the proposed energy management approach. Several simulations considering various operational scenarios are achieved to reveal the efficacy of the devised methodology. The obtained findings show the efficacy of the devised approach in various operational cases of the microgrids. To further confirm the efficacy of the devised approach, the achieved findings are compared to a pattern search (PS) optimization-based energy management approach and demonstrate outperformed performances with respect to solution optimality and computing time.
Highlights
The increasing deployment of distributed generations (DGs), the advantage of renewable energy in reducing carbon emissions, the intermittency of renewable generations, the advent of advanced controllers and the need to have a more reliable and resilient power grid are some of major causes for the ongoing energy transition reforms globally [1,2,3].A microgrid (MG) is the assemblage of integrated electricity consumers, distributed generations (DGs) and distributed energy storages (DESs) at a distribution grid voltage level with clear electrical margins
We propose an efficient power management technique for the 24 h-ahead optimal operation of mega-scale grid-coupled microgrids that consists of wind energy, a diesel generator, an energy storage system with several units and local loads
∆T is the duration of the time steps that equals one hour for the hourly optimization model; c(t) is the electricity price at time t, and it equals the purchasing price when the microgrid imports electricity from the upstream utility network and the selling price when the microgrid exports electricity to the main grid; P g (t) is the electricity of the utility grid—the sign convention here is that P g (t) is positive when the MG purchases electricity, negative when the MG
Summary
The increasing deployment of distributed generations (DGs), the advantage of renewable energy in reducing carbon emissions, the intermittency of renewable generations, the advent of advanced controllers and the need to have a more reliable and resilient power grid are some of major causes for the ongoing energy transition reforms globally [1,2,3]. The power-trading advancement inspires microgrid aggregators to adjust their power-exchange engagements with the upstream grid based on time-of-use dynamic pricing schemes in order to reduce power generation costs (fuel expenses), guarantee the enhanced utilization of RESs and DSs and improve the energy-trading profit To accomplish these objectives, robust and optimal EMS should be implemented and integrated in the microgrid control architecture [6,9,10,11]. The major target of the devised energy management methodology is to reduce the microgrid expenditure for fuel, operation and maintenance and main grid power import It targets maximizing the MG profit by exporting electricity to the upstream utility network.
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