The increasing emphasis on clean emission-free transportation has stressed upon the high adoption and penetration of electric vehicles worldwide. However, challenges like range anxiety and long charging time hinder the widespread acceptance. Among various solution the usage of battery swapping station seems more promising as it provide quick battery refueling within a very short time period. The battery swapping station's progress is limited due to the associated investment and operational cost which needs to be addressed to ensure the global acceptance. In this paper, an optimal battery swapping station operation is proposed based on a multi-objective optimization which combines the generation mix of grid, solar PV, and biogas generation along with the battery arrival using mixed integer programming and orderly charging of discharged batteries to allow the swapping station to operate in battery-to-grid mode using time-dependent dynamic programming. The former reduced the cost of charging while the later increases the swapping station revenue. The combined multi-objective optimization increases the daily net profit by almost 20 times as compared to the base case and by 8% in the optimal operation when the batteries are allowed to be discharged in battery-to-grid mode. The results show that the proposed strategy has potential to significantly boost the daily net profit of the swapping station by increasing revenue and cut cost and can contribute towards the acceptance of swapping station in the current electric vehicle market.
The increasing importance of electric vehicles lies in their lower emissions compared to fossil fuel vehicles.However, challenges like long charging times and range anxiety hinder their widespread adoption.Battery swapping stations offer a practical solution to expedite EV refueling, reducing wait times and range concerns.This research proposes a battery-swapping architecture that provides battery-swapping services to electric vehicles while exploring additional revenue sources and cost reductions.The model uses batteries of the battery swapping station as a battery energy storage system, supplying power to mobile or stationary loads during grid or renewable energy source downtime.By offering cost-effective electricity during peak hours or non-availability, the model demonstrates up to a 35% reduction in consumer electricity costs during peak hours and an 8.8% reduction in overall costs during 24-hour operation.The implementation combines linear programming with machine learning to forecast renewable energy output and electric vehicle energy demand, considering flexible battery charging and discharging controls and degradation processes.These optimization results show the potential of the proposed model to boost battery swapping station income and cut costs, contributing significantly to the electric vehicle market's growth.
Accurate short-term load forecasting is crucial for the effective operation of the power sector. Due to heightened volatility and intrinsic stochasticity, forecasting load at a fine resolution, such as weekly load, is difficult. The issue becomes significantly simpler at higher scales (for example, clusters of users), where the inherent stochasticity and fluctuations are averaged out. Numerous methods have been suggested to anticipate consumer loads and clusters with high accuracy in a variety of locations. These techniques only rely on temporal data and auto-regressive processes to forecast load. However, the power grid has a graphical structure that provides spatial information too. This paper proposes an innovative short-term load forecasting method fusing both spatial and temporal information. To solve the above problems, we propose a creative way to convert load data into graphical form, which is fed into Graph Convolutional Networks (GCN) to learn spatial embeddings. The GCN embeddings are used along with temporal features to predict the load. We perform extensive experiments using state-of-the-art machine learning and deep learning techniques to validate our approach. The results demonstrate that using spatial information in the form of GCN embeddings can substantially enhance the forecasting performance (upto 39% improvement).
Power generation and consumption is an instantaneous process and maintaining the balance between demand and supply is crucial since the demand and supply mismatch leads to various risks like over-investment, over-generation, under-generation, and the collapse of the power system. Therefore, the reduction in demand and supply mismatch is critical to ensure the safety and reliability of power system operation and economics. A typical and common approach, called full load shedding (FLS), is practiced in cases where electric power demand exceeds the available generation. FLS operation alleviates the power demand by cutting down the load for an entire area or region, which results in several challenges and problems for the utilities and consumers. In this study, a demand-side management (DSM) technique, called Soft-load shedding (SLS), is proposed, which uses data analytics and software-based architecture, and utilizes the real-world time-series energy consumption data available at one-minute granularity for a diversified group of residential consumers. The procedure is based on pattern identification extracted from the dataset and allocates a certain quota of power to be distributed on selected consumers such that the excessive demand is reduced, thereby minimizing the demand and supply mismatch. The results show that the proposed strategy obtains a significant reduction in the demand and supply mismatch such that the mismatch remains in the range of 10–15%, especially during the period where demand exceeds generation, operating within the utility constraints, and under the available generation, to avoid power system failure without affecting any lifeline consumer, with a minimum impact on the consumer’s comfort.
The stored energy of charged batteries at a Battery Swapping Station (BSS) can be used as a source of electricity. This paper discusses, an optimal dispatch of the stored electrical energy at a BSS to power up the external load. The problem is solved using linear programming for a small farm cold storage with a pre-cooling effect. With the precooling effect, a 5% reduction in the price of electricity is obtained. The BSS can be used to provide cheaper electricity to the consumer and gain profits for the BSS owner by selling excess energy enhancing the potential of BSS technology adoption.
Renewable energy is built as the future of energy value chain. In particular, solar energy is being utilized at a faster pace than ever. The problem sometimes occurs that large scale Floating Solar PV (FSPV) plants have to be developed, away from the population which adds to the cost of transmission and distribution. Floating Solar PV plants have recently gained traction as a suitable alternative of ground based large scale PV installation. FSPV, not only utilizes the water as real estate, but it has a number of other advantages. For example, FSPV could utilize the existing transmission and distribution infrastructure that is the part of hydroelectric power plant anyways. In this paper, we analyze hydro-FSPV combination on a small dam in Pakistan. Our results show that FSPV could complement the existing hydroelectric production and could provide electricity in conjunction with the existing generators of the hydroelectric plant. Furthermore, we also discuss options of integrating FSPV with the existing electrical infrastructure of Ghazi Barotha Dam.
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