Existing literature on planning for electric vehicle charging stations (EVCS) fails to consider uncertain factors in power systems, such as load fluctuations and the impact of EV integration. Consequently, using deterministic power flow (DPF) algorithms for EVCS planning is unreliable. To address this, we propose a probabilistic model for EV charging loads and introduce a novel dynamic system voltage stability (DSVS) index. We then present an effective optimization model for EVCS site and size planning using stochastic power flow (SPF). Our model aims to maximize capital gains on investment costs of EVCS, minimize yearly EV users’ average wait time and distance to charge costs and minimize the DSVS index. To simplify the problem, we use the super-efficiency data envelopment analysis (SEDEA) method to determine objective weights and transform the multi-objective optimization problem into a single-objective one. Finally, we jointly solve the model using the voronoi diagram and adaptive differential evolution optimization algorithm (ADEOA). We verify the effectiveness of our proposed method using a case study with the IEEE 33-node distribution network topology diagram and a planning area diagram.

As a new type of transportation, electric vehicles (EV) can effectively adjust the supply and demand balance of power systems using their vehicle-to-grid (V2G) characteristics. To better promote the participation of EV resources in the energy market and interact with power systems, we propose a novel framework of an electric vehicle aggregator (EVA) that can aggregate schedulable EVs within its jurisdiction to provide auxiliary services for the power grid. Due to EV charging behavior’s uncertain nature, we employ a probability mass function (PMF) based model to provide more accurate forecasts of future EV behaviors. To reduce EVA operation costs and maximize the travel utility for EV users participating in this service, we develop an EVA optimization schedule model that combines a day-ahead optimization schedule and real-time optimization schedule. Finally, we create three case studies to verify the results of the proposed method. Matlab is used to simulate and analyze each case study concerning uncoordinated charging, coordinated charging while considering day-ahead optimization schedules, and an ensemble of coordinated charging activities that consider the day-ahead optimization schedule and real-time optimization schedule. Through comparative analysis, it is verified that the proposed strategy can effectively reduce EVAs’ operating costs and meet the travel requirements of EV users. The impact of different degrees of error of EV plug-out information on the proposed method is also analyzed.

Massive electrical load exhibits many patterns making it difficult for forecast algorithms to generalise well. Most learning algorithms produce a better forecast for dominant patterns in the case of weekday consumption and otherwise for less dominant patterns in weekend and holiday consumption. In view of this, there is the need to cluster the load patterns, so learning algorithms can focus on the patterns independently to produce forecasts with better accuracy for all cases. However, clustering time-series data breaks the time-series dependency, making model training difficult. This paper presents a novel sequence-to-sequence cluster framework to reform time-series dependency after clustering; this enables independent clusters to be modelled using Convolutional Neural Network-Gated Recurrent Unit, which learns spatiotemporal features for future forecasts. A real-world dataset by the Korea Power Exchange composed of nationwide consumption is used for case studies and experiments. Experimental results verify that the proposed study effectively improves the accuracy of electric load forecasting by about 50%, with a WAPE of 0.67%. The proposed method also speeds up the training process of the forecast algorithm by about 35%, given that only a subset of the dataset is trained due to clustering. Korea Water Resources Corporation has implemented the proposed method for load forecasting and system marginal price estimation.

Differential Evolution (DE) has been extensively adopted for multi-objective optimization due to its efficient and straightforward framework. In DE, the mutation operator influences the evolution of the population. In this paper, an adaptive Grid-based Multi-Objective Differential Evolution is proposed to address multi-objective optimization (ad-GrMODE). In ad-GrMODE, an adaptive grid environment is employed to perform a mutation strategy in conjunction with performance indicators. The grid reflects the convergence and diversity performance together but is associated with the user-specified parameter “div”. To solve this problem, we adaptively tune the parameter “div”. Among the DE mutation strategies, “DE/current-to-best/1” is applied extensively in single-objective optimization. This paper extends the application of “DE/current-to-best/1” to multi-objective optimization. In addition, a two-stage environmental selection is adopted in ad-GrMODE, where in the first stage, one-to-one selection between the parent and its corresponding offspring solution is performed. In addition, to preserve elitism, a stochastic selection is adopted with respect to performance metrics. We conducted experiments on 16 benchmark problems, including the DTLZ and WFG, to validate the performance of the proposed ad-GrMODE algorithm. Besides the benchmark problem, we evaluated the performance of the proposed method on real-world problems. Results of the experiments show that the proposed algorithm outperforms the eight state-of-the-art algorithms.

A significant amount of the literature is focused on converters that supply the required voltage with low input-current ripple to electronic devices. A hybrid converter that combines boost and Cuk converters was developed recently. This hybrid converter achieved a relatively low input-current ripple based on earlier strategies. This paper proposes a new model that simulates a hybrid power converter system using a unified evolutionary algorithm (EA). As part of this paper, we present an improved framework for a hybrid power converter. Moreover, a unified EA is developed to incorporate differential evolution (DE) and genetic algorithm (GA) properties in order to analyze the proposed modified hybrid power converter. This research describes a modified hybrid power converter that optimizes the zero-ripple duty cycle (D_Z) through the proposed algorithm for minimizing the input-current ripple. Based on our simulation results, comparing the proposed method with the baseline algorithm reveals that the proposed approach is significantly more efficient than the baseline algorithm and achieves the minimum input-current ripple in different gain values. In addition, we observe that the proposed algorithm performs better than the DE and GA algorithms in terms of obtaining low input-current ripple results. Therefore, the proposed hybrid algorithm is becoming more efficient with hybridization.