Load forecasting (LF) is an essential factor in power system management. LF helps the utility maximize the utilization of power-generating plants and schedule them both reliably and economically. In this paper, a novel and hybrid forecasting method is proposed, combining a long short-term memory network (LSTM) and neural prophet (NP) through an artificial neural network. The paper aims to predict electric load for different time horizons with improved accuracy as well as consistency. The proposed model uses historical load data, weather data, and statistical features obtained from the historical data. Multiple case studies have been conducted with two different real-time data sets on three different types of load forecasting. The hybrid model is later compared with a few established methods of load forecasting found in the literature with different performance metrics: mean average percentage error (MAPE), root mean square error (RMSE), sum of square error (SSE), and regression coefficient (R). Moreover, a guideline with various attributes is provided for different types of load forecasting considering the applications of the proposed model. The results and comparisons from our test cases showed that the proposed hybrid model improved the forecasting accuracy for three different types of load forecasting over other forecasting techniques.

This task‐force paper documents and summarizes the models of power transformers that have been proposed, used, and validated in the exercise of digital real‐time simulation. Power transformer is one of the most important equipment in power systems, and its modelling for electromagnetic transient simulation has evolved over time, especially in the area of real‐time simulation. The focus of the paper is to document and archive the models that have been well accepted and used for transient analysis in digital real‐time simulation so that readers can use it as a master document for transformer modelling in real‐time simulation studies. It includes both conventional and specialized models of power transformers that have been broadly acknowledged by the power engineering community. The models provided here come with detailed mathematical representation and their implementation techniques. A comparative study is also performed to illustrate the differences in their performances. In the end, an application guideline has been provided to guide the readers to select the appropriate model for their study.

As the adoption of electric vehicles (EVs) continues to rise, efficient scheduling methods that minimize operational costs are critical. This paper introduces a novel EV scheduling method utilizing a heuristic graph-search algorithm for cost minimization due to its admissible nature. The approach optimizes EV charging and discharging schedules by considering real-time energy prices and battery degradation costs. The method is tested on systems with solar generation, electric loads, and EVs featuring vehicle-to-grid (V2G) connections. Various charging rates, such as standard, fast, and supercharging, along with uncertainties in EV arrival and departure times, are factored into the analysis. Results from various case studies demonstrate that the proposed method outperforms popular heuristic optimization techniques, such as particle swarm optimization and genetic algorithms, by 3–5% for different real-time energy prices. Additionally, the method’s effectiveness in reducing operational costs for workplace EVs is confirmed through extensive case studies under varying uncertain conditions. Finally, the system is implemented on a digital real-time simulator with DNP3 communication, where real-time results align closely with offline simulations, confirming the algorithm’s efficacy for real-world applications.

The smart building energy management system (BEMS) has become essential for prosumers to manage their power consumption efficiently and conveniently, as building energy management evolves with the integration of distributed resources. This paper combines a reference point-based non-dominated sorting genetic algorithm (NSGA-III) with a Neural Prophet-based forecasting model to address the multi-objective BEMS problem and identify an optimal solution by considering the 24 h ahead real-time energy prices. The BEMS problem is designed as a multi-objective formulation that includes minimizing operating costs and alleviating user discomfort with their corresponding weights. To implement the proposed method, a building energy system is modeled, encompassing energy storage, electric vehicles, renewable energy resources, and various building loads. Multiple test cases conducted on residential and commercial buildings demonstrate significant cost savings in both types of buildings during both summer and winter days. A comparative cost analysis shows that relative to GA and NSGA-II, the proposed model reduced the reported operating cost by approximately 6.2% and 11.9%, and the execution time by approximately 30.3% and 14.4%, respectively. A real distribution feeder model with additional building energy system components is modeled on a digital real-time simulator using a reduced model approach to create a digital real-time system. The close agreement between the offline and digital real-time simulation results supports the feasibility of implementing the proposed framework under the operating conditions.