The purpose of this paper is to suggest short-term Seasonal forecasting for hourly electricity demand in the New England Control Area (ISO-NE-CA). Precision improvements are also considered when creating a model. Where the whole database is split into four seasons based on demand patterns. This article’s integrated model is built on techniques for machine and deep learning methods: Adaptive Neural-based Fuzzy Inference System, Long Short-Term Memory, Gated Recurrent Units, and Artificial Neural Networks. The linear relationship between temperature and electricity consumption makes the relationship noteworthy. Comparing the temperature effect in a working day and a temperature effect on a weekend day where at night, the marginal effects of temperature on the demand in a working day for power are likewise at their highest. However, there are significant effects of temperature on the demand for a holiday, even a weekend or special holiday. Two scenarios are used to get the results by using machine and deep learning techniques in four seasons. The first scenario is to forecast a working day, and the second scenario is to forecast a holiday (weekend or special holiday) under the effect of the temperature in each of the four seasons and the cost of electricity. To clarify the four techniques’ performance and effectiveness, the results were compared using the Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), Normalized Root Mean Squared Error (NRMSE), and Mean Absolute Percentage Error (MAPE) values. The forecasting model shows that the four highlighted algorithms perform well with minimal inaccuracy. Where the highest and the lowest accuracy for the first scenario are (99.90%) in the winter by simulating an Adaptive Neural-based Fuzzy Inference System and (70.20%) in the autumn by simulating Artificial Neural Network. For the second scenario, the highest and the lowest accuracy are (96.50%) in the autumn by simulating Adaptive Neural-based Fuzzy Inference System and (68.40%) in the spring by simulating Long Short-Term Memory. In addition, the highest and the lowest values of Mean Absolute Error (MAE) for the first scenario are (46.6514, and 24.759 MWh) in the spring, and the summer by simulating Artificial Neural Networks. The highest and the lowest values of Mean Absolute Error (MAE) for the second scenario are (190.880, and 45.945 MWh) in the winter, and the autumn by simulating Long Short-Term Memory, and Adaptive Neural-based Fuzzy Inference System.

This paper introduces a robust optimal week-ahead generation scheduling approach that takes into account plug in hybrid electric vehicles (PHEVs) considering uncertainty in loads, renewable energy resources, and PHEV charging behavior. Due to the complexity of the scheduling process there is crucial need for a reliable optimal algorithm. The proposed approach can be applied in energy management platforms of decarbonized eco-friendly power systems. Generation scheduling is modeled as a multi-objective optimization problem: (a) minimize generation production cost and (b) minimize emission costs. The focal concern is to (a) handle the scheduling of renewable energy resources against their volatilities, (b) integrating PHEVs with uncertainties related to their state of charge, and (c) stochastic load behavior over a whole week. Two heuristic-based algorithms are used to solve the optimization problem, namely Water Cycle Algorithm and Gravitational Search Algorithm The proposed scheduling approach is implemented in MATLAB ® Platform, and is tested using two different microgrids sizes, 3 generator, and 10 generator unit systems integrating the effect of week days profile, renewable energy intermittency and different PHEV state of charges using the IEEE Reliability Test System (RTS) data. The results show promising performance of GSA over the WCA in the energy management studies integrating three different types of sources; thermal units, Renewable Energy Resources (RERs), and the PHEVs.

Distributed generators (DG) which installed into distribution network to face the increasing of load demand. DG can used to enhance power generation systems and improve distribution network efficiency. However, the distributed generators units’ implementation at not convenient position and sizing can lead to negative impacts such as a growing in power losses and invasion of system constraints. Because the rising of demand in energy. The appropriate placement and sizing of DG’s units is a credible solution to much problems in distribution system, for example power loss reduction and voltage regulation. The allocation of distributed generators into the distribution network can significantly impact in the transmission and distribution systems. Therefore, a method, which can identify an optimum DG location and size, is necessary. In this paper, Genetic Algorithm (GA) and Ant colony Algorithm (ACO) optimization techniques are proposed to find optimal sizing and location for distributed generation in electrical networks. The objective function of the work relies upon a linearized model to compute the active power losses as a function of power supplied from the generators. This strategy based on a strong coupling between active power and power flow taking into consideration the voltage angles. With the end goal to exhibit the adequacy of the proposed method, the proposed strategy is applied on IEEE 57-bus standard systems. Different maximum penetration level capacity of DG units and various possible places of DG units among several types of DG (active, reactive or active and reactive power) are considered. Results show that the optimization tools employing GA and ACO are effective in reducing active power losses by finding the optimal placement and sizing of DG units