EV specific time-of-use rate plans have been recently introduced by several utilities to overcome the demand charge issue that is the main barrier impeding EV growth in the commercial and industrial sector. This study analyses two EV specific TOU rates in place from a customer and the grid perspectives. The analysis relies on a developed optimal cost model with coordinated charging strategies that minimizes the total cost of a workplace charging station over its lifetime. From a customer perspective, it is shown that the cost benefits are not always achievable and depends on the rates provided. From the grid perspective, the peak demand is found to be increased. Thus, the EV specific rates may not always provide an efficient use of the grid assets.
This study proposes a novel multi-criteria decision-making (MCDM) model based on a rough extension of the Ordinal Priority Approach (OPA) to determine the order of importance of users' perspectives on Electric Vehicle (EV) purchases. Unlike conventional methods that rely on predefined ranks for criteria weighting coefficients, the proposed rough OPA method employs an aggregated rough linguistic matrix, enabling a more precise and unbiased calculation of interval values. Moreover, the model addresses inherent uncertainties by incorporating nonlinear aggregation functions, accommodating decision makers' risk attitudes for flexible decision-making. To validate the model's efficacy, a large-scale post-EV test drive survey is conducted, enabling the determination of relative criterion importance. Sensitivity analysis confirms the robustness of the model, demonstrating that marginal changes in parameters do not alter the ranking order. The results unveil the significance of the reliability criterion and reveal that vehicle-related characteristics outweigh economic and environmental attributes in the decision-making process. Overall, this innovative MCDM model contributes to a more accurate and objective analysis, enhancing the understanding of users' preferences and supporting informed decision-making in EV purchases.
This paper investigates the impact of interrupted and uninterrupted charging strategies of plug-in electric vehicles (PEVs) on the aggregated load profile while considering the user convenience, i.e., the desired state-of-charge of battery at the departure time. First, this paper introduces a new coordinated charging algorithm with interrupted charging intervals. Then, coordinated charging algorithms with uninterrupted and interrupted charging strategies are compared, with heuristic prioritizing policies, on different base-load characteristics under different PEV penetration rates. The impact is quantified in tenns of the variance of the aggregated load profile. The impact of the priority assignment policies on the aggregated load variance is also explored.
This study proposes a multi-objective optimization model to determine the optimal charging infrastructure for a transition to plug-in electric vehicles (PEVs) at workplaces. The developed model considers all cost aspects of a workplace charging station, i.e., daily levelized electric vehicle supply equipment (EVSE) infrastructure cost, PEV energy and demand charges. These single-objective functions are aggregated in a multi-objective optimization framework to find the Pareto optimal solutions. Smart charging strategies with interrupted and uninterrupted power profiles are proposed to maximize the use of EVSE units. The charging behavior model is developed based on collected workplace charging data. The model is tested with various scheduling policies to investigate their impact on the behaviors of EVSE types from different perspectives. Finally, a sensitivity analysis is performed to assess the impacts of battery sizes and onboard charger ratings on cost behavior. It is shown that the proposed model can achieve up to 7.8% and 14.6% cost savings as compared to single-objective optimal models and the current charging practice, respectively. The unit cost is found to be more sensitive to scheduling policies than the charging strategies. It is also found that the flexibility ratio policy gives the best PEV scheduling with the lowest unit cost and the most efficient use of the grid assets.
Abstract With the increasing penetration of grid‐scale wind energy systems, accurate wind power forecasting is critical to optimizing their integration into the power system, ensuring operational reliability, and enabling efficient system asset utilization. Addressing this challenge, this study proposes a novel forecasting model that combines the long‐short‐term memory (LSTM) neural network with two signal decomposition techniques. The EMD technique effectively extracts stable, stationary, and regular patterns from the original wind power signal, while the VMD technique tackles the most challenging high‐frequency component. A deep learning‐based forecasting model, i.e. the LSTM neural network, is used to take advantage of its ability to learn from longer sequences of data and its robustness to noise and outliers. The developed model is evaluated against LSTM models employing various decomposition methods using real wind power data from three distinct offshore wind farms. It is shown that the two‐stage decomposition significantly enhances forecasting accuracy, with the proposed model achieving values up to 9.5% higher than those obtained using standard LSTM models.
This study deals with the transient behavior of induction machines starting directly from the power distribution network and proposes a model to predict the starting current and torque. Direct starting may cause important faults on both power network and load. Torque oscillations during transient may resonate with mechanical load system. The first peak value of torque is also critical for machine loads. On the other hand, during direct starting, high transient currents make large voltage drops on power system resulting in failing other connected loads. Therefore an accurate transient machine model becomes imperative in order to estimate starting torque and starting current for the overall system design. The prediction of induction machine transient performance requires proper account of the saturation effects. In the proposed model, the main flux path and the leakage flux path saturations are separately considered. They are modeled by functions of corresponding currents for both stator and rotor sides. As reliable experimental data on high power induction machines are not easily available, experimental measurements have been carried out on a 3 phase, 45 kW, 4 poles delta-connected induction motor. Finally, experimental and simulation results are compared to prove the performance of the proposed model.
Wind energy is the leading form of non-hydro renewable energy source in terms of installed capacity in Turkey. It is among the most promising option for Turkey to decrease the energy dependence of external primary energy resources such as national gas and oil that diversifies the domestic share of energy sources in the national energy mix. However, offshore wind energy deployment has not gained satisfactory attention even though the country is surrounded by seas on three of its sides. Exploring Turkey's offshore wind power potential becomes an important task to serve this energy policy. This study presents a methodological framework for finding the most suitable offshore wind farm locations, meeting various multi-layer site selection criteria. The offshore wind energy resource is first assessed using the wind energy potential for 55 coastal regions where the nearshore meteorological stations are available in Turkey. Following on this analysis, a multi-criteria site selection work is carried out to identify the most suitable areas for offshore wind development. Wind Atlas Analysis and Application Program (WAsP) is then used to conduct statistical analysis to identify the most promising offshore wind farm locations. According to the pre-processing step of the framework, Bozcaada, Bandirma, Gokceada, Inebolu, and Samandag coastlines are found to be the most suitable locations for offshore wind farm development. Finally, the offshore wind energy potential of Turkey is estimated by using the micro-sitting configuration of wind turbines, considering sea depth, main wind direction, and distance to shore for the most feasible project locations. It is found that total estimated offshore wind power capacity at the specified sites is 1,629 MW.
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