Strategic scheduling of the electric vehicle-based microgrids under uncertainties: Optimizing Roof-Mounted Solar and Storage

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Strategic scheduling of the electric vehicle-based microgrids under uncertainties: Optimizing Roof-Mounted Solar and Storage

With increasing worldwide attention on environmental sustainability, microgrids that harness renewable sources have become more prominent. The changing characteristics of renewable energy sources and energy demand’s unpredictable patterns might cause disruptions in the sustainable working of microgrids. Moreover, EVs (electric vehicles), being dynamic loads, might significantly affect the security administration of the microgrid.

This study examines the functioning of the microgrid when it is coupled to the main power grid. The multi-objective financial dispatching modeling for the microgrid’s operation is built with the primary goals of minimizing both operational and environmental expenses. The operating cost, referred to as C_oc, includes the fuel expenses for micro-gas turbine and fuel cell, as well as the costs of operation and maintenance, depreciation of power supply equipment, costs resulting from interactions among the distribution network and the microgrid, and the expense of purchasing electric power from vehicles.

The microgrid has the ability to influence consumer electricity usage through pricing strategies in order to improve economic efficiency. The real-time power selling price, p_sl,RT(t), and purchasing price, p_puc,RT(t), are determined based on the net microgrid’s flexible load. This signifies the equilibrium between the renewables generation and the anticipated demand. The modification of electricity pricing aims to encourage rational involvement from both vehicle owners and microgrid administrators.

The operating approach for the microgrid focuses on prioritizing renewable sources, such as wind turbines, which provide electricity without causing environmental pollution. Their power output can be predicted utilizing forecasting methods. Furthermore, the charging behaviors of electric vehicles are modeled employing the Monte Carlo approach, guaranteeing that they are prioritized for use in order to fulfill anticipated demand. The strategy additionally evaluates the capability and functioning method of the energy storage systems, enabling efficient and ecologically aware planning of the micro-gas turbine and fuel cell operations.

In order to enhance the microgrid’s dependability, it is coupled to the primary grid, which acts as a backup power supply. This study classifies the load management and potential power imbalances caused by wind turbines and electric vehicles into two scenarios: when the supply of power is greater than the demand, and when the demand for power is greater than the supply. The microgrid’s dispatching method is determined by the economic cost at each occurrence, with the goal of achieving a balance between cost-efficiency and reliable service supply.

There are several ways to deal with numerous objectives, and one common option is the linear weighting approach. This strategy combines many goals into a one-objective via giving various weights to any target, therefore altering the objective problem. An important drawback of this strategy is the difficulty in accurately assigning weights to each target because of the subjective nature of current weight determination systems. Moreover, the incorporation of renewable energy sources into the microgrid system examined in this research increases variability, which adds complexity to the scheduling tasks because of their unpredictable nature.

In order to mitigate the impact of these uncertainties and calculate unbiased weights, a weight definition technique under the principles of a TPZSG (two-person zero-sum game) was employed. By attributing human characteristics to nature and considering it as a rival to the human decision-maker, a scheduling method that is more impartial is attained, hence reducing the disruptive influence of nature.

To effectively balance the dual objectives of economic efficiency and environmental sustainability, we employed a TPZSG approach within our optimization framework. This approach plays a critical role in converting the multi-objective optimization problem into a single-objective model by using a linear weighting strategy. In this game-theoretic framework, the two players represent competing objectives: one player seeks to minimize the operational costs of the microgrid, while the other focuses on reducing environmental impacts.

The ASA-PSOA (adaptive simulated annealing-particle swarm optimization algorithm) was implemented to find the best solutions in this context. The ASA-PSOA dynamically modifies the factors of the PSOA (particle swarm optimization algorithm), integrating the Metropolis criteria of the SAA (simulated annealing algorithm) to improve the algorithm’s ability to overcome local optima.

The simulation outcomes indicate that the multi-function weighting strategy can reduce the impact of uncertainties, hence optimizing the use of renewable resources and load management. Furthermore, implementing systematic charging and discharging procedures for electric vehicles has the potential to decline both operational and environmental expenses in microgrids. The total expense of the system under the proposed algorithm (ASA-PSOA) can be reduced by 11.1%, 10.1%, 6.5%, and 4.5% compared to the PSOA, standard-PSOA, adaptive-PSOA, and simulated annealing-PSOA, respectively. Therefore, the improved optimization technique greatly enhances the economic and ecological efficiency of the microgrid.

The salient contributions of this research, in contrast to previous investigations, can be outlined as follows:

  1. We implemented a microgrid under a five-port output specifically designed for electric vehicles. We examined the chaotic charging patterns of EVs and employed real-time electricity pricing to control and manage the charging and discharging of EVs in an organized manner.

  2. We utilized the TPZSG to calculate the numerous objectives’ weights, so improving the fairness in balancing economic concerns and environmental implications. This methodology not only mitigates the impact of unpredictable factors but also decreases the amount of electric power bought from the grid.

  3. In order to enhance the optimization abilities of the model and provide an optimized point that is globally optimal, we made improvements to the PSOA and utilized ASA-PSOA to optimize the microgrid’s dispatch strategy.

The increasing worldwide need for electricity has elevated energy and environmental problems to become key concerns in society. The burning of fossil fuels in typical plants had entailed significant environmental damage, while conventional power systems face inefficiencies and expensive operational expenses. As a result, electric vehicles, which use renewables to produce electric power, had been widely accepted worldwide.

A MG (microgrid) refers to a compact electric energy generation and distribution structure that integrates renewables and storage units. MG plays a vital role in improving the reliability, energy efficiency, and cost-effectiveness of power systems. Nevertheless, the inconsistency and instability of renewables, combined with the fluctuating demand, present a challenge to maintaining a stable equilibrium between supply/demand in the MG, as well as ensuring its safe and cost-effective operation.

Due to the progress in electric vehicle technology, these vehicles are being more and more integrated into microgrid plans as crucial distributed power sources. This has stimulated an increasing amount of research that is specifically aimed at improving the efficiency of microgrid operations that use electric vehicles. Electric vehicles, which function as portable energy storage devices within the microgrid, have a substantial influence on load demands.

In the absence of adequate management, electric vehicles have the potential to charge in a disorganized manner, which can worsen the strain on the power grid at peak periods. On the other hand, when EVs are directed by V2G (Vehicle-to-Grid) technology, they help meet the demand for renewable energy and fulfill the energy needs of microgrids. As the integration of electric vehicles becomes more prevalent, it is crucial to develop a well-thought-out dispatch plan to minimize operational expenses for microgrid systems that include EVs.

The scheduling of a microgrid with electric vehicles is a complex task that involves optimizing multiple objectives. In this context, the term “multi-objective optimal scheduling” refers to the process of finding the most efficient and effective schedule for the microgrid, taking into account various objectives such as minimizing energy costs, maximizing renewable energy utilization, and ensuring reliable power supply. This scheduling problem becomes even more challenging when electric vehicles are involved, as their charging and discharging patterns need to be coordinated with the overall operation of the microgrid.

Therefore, finding a solution that satisfies all these objectives simultaneously is crucial for the successful operation of a microgrid with electric vehicles. Artificial intelligence (AI) has significantly transformed various fields by integrating human-like capabilities such as learning, reasoning, and perception into software. This technological advancement enables computers to undertake tasks traditionally performed by humans.

The proposed framework in this study employs a linear weighting strategy under a TPZSG to maximize the utilization of renewables options and provide support for the load. The ultimate objective is to achieve a more efficient balance between these two goals. In addition, a more advanced approach called enhanced ASA-PSOA is employed to find the best solutions in this context.

The simulation outcomes indicate that the multi-function weighting strategy can reduce the impact of uncertainties, hence optimizing the use of renewable resources and load management. Furthermore, implementing systematic charging and discharging procedures for electric vehicles has the potential to decline both operational and environmental expenses in microgrids. The total expense of the system under the proposed algorithm (ASA-PSOA) can be reduced by 11.1%, 10.1%, 6.5%, and 4.5% compared to the PSOA, standard-PSOA, adaptive-PSOA, and simulated annealing-PSOA, respectively. Therefore, the improved optimization technique greatly enhances the economic and ecological efficiency of the microgrid.

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