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The growing demand for electricity and the reconstruction of poor areas in Africa require an effective and reliable energy supply system. The construction of reliable, clean, and inexpensive microgrids, whether isolated or connected to the main grid, has great importance in solving energy supply problems in remote desert areas. It is a complex interaction between the level of reliability, economical operation, and reduced emissions. This paper investigates the establishment of an efficient and cost-effective microgrid in a remote area located in the Djado Plateau, which lies in the Sahara Ténéré desert in northeastern Niger. Three cases are presented and compared to find the best one in terms of low costs. In case 1, the residential area is supplied by PVs and a battery energy storage system (BESS), while in the second case, PVs, a BESS, and a diesel generator (DG) are utilized to supply the load. In the third case, the grid will take on load-feeding responsibilities alongside PVs, a BESS, and a DG (used only in scenario 1 during the 2 h grid outage). The central objective is to lower the cost of the proposed microgrid. Among the three cases, case 3, scenario 2 has the lowest LCC, but implementing it is difficult because of the nature of the site. The results show that case 2 is the best in terms of total life cycle cost (LCC) and no grid dependency, as the annual total LCC reaches about $2,362,997. In this second case, the LCC is 11.19% lower compared to the first case and 5.664% lower compared to the third case, scenario 1.

Electric vehicles ( EVs) are the future of the automobile industry, as they produce zero emissions and address environmental and health concerns caused by traditional fuel-poared vehicles. As more people shift towards EVs, the demand for power consumption forecasting is increasing to manage the charging stations effectively. Predicting power consumption can help optimize operations, prevent grid overloading, and power outages, and assist companies in estimating the number of charging stations required to meet demand. The paper uses three time series models to predict the electricity demand for charging stations, and the SARIMA (Seasonal Auto Regressive Integrated Moving Average) model outperforms the ARMA (Auto Regressive Moving Average) and ARIMA (Auto Regressive Integrated Moving Average) models, with the least RMSE (Root Mean Squared Error), MAE (Mean Absolute Error) and MAPE (Mean Absolute Percentage Error) scores in forecasting power demand and revenue. The data used for validation consists of charging activities over a four-year period from public charging outlets in Colorado, six months of charging data from ChargeMOD's public charging terminals in Kerala, India. Power usage is also forecasted based on wheels of vehicles, and finally, a plan subscription data from the same source is utilized to anticipate income, that helps companies develop pricing strategies to maximize profits while remaining competitive. Utility firms and charging networks may use accurate power consumption forecasts for a variety of purposes, such as power scheduling and determining the expected energy requirements for charging stations. Ultimately, precise power consumption forecasting can assist in the effective planning and design of EV charging infrastructure. The main aim of this study is to create a good time series model which can estimate the electric vehicle charging stations usage of power and verify if the firm has a good income along with some accuracy measures. The results show that SARIMA model plays a vital role in providing us with accurate information. According to the data and study here, four wheelers use more power than two and three wheelers. Also, DC charging facility uses more electricity than AC charging stations. These results can be used to determine the cost to operate the EVs and its subscriptions.

This paper solves an optimal reactive power scheduling problem in the deregulated power system using the evolutionary based Cuckoo Search Algorithm (CSA). Reactive power scheduling is a very important problem in the power system operation, which is a nonlinear and mixed integer programming problem. It optimizes a specific objective function while satisfying all the equality and inequality constraints. In this paper, CSA is used to determine the optimal settings of control variables such as generator voltages, transformer tap positions and the amount of reactive compensation required to optimize the certain objective functions. The CSA algorithm has been developed from the inspiration that the obligate brood parasitism of some Cuckoo species lay their eggs in nests of other host birds which are of other species. The performance of CSA for solving the proposed optimal reactive power scheduling problem is examined on standard Ward Hale 6 bus, IEEE 30 bus, 57 bus, 118 bus and 300 bus test systems. The simulation results show that the proposed approach is more suitable, effective and efficient compared to other optimization techniques presented in the literature.

In order to address the impact of the uncertainty and intermittency of a photovoltaic power generation system on the smooth operation of the power system, a microgrid scheduling model incorporating photovoltaic power generation forecast is proposed in this paper. Firstly, the factors affecting the accuracy of photovoltaic power generation prediction are analyzed by classifying the photovoltaic power generation data using cluster analysis, analyzing its important features using Pearson correlation coefficients, and downscaling the high-dimensional data using PCA. And based on the theories of the sparrow search algorithm, convolutional neural network, and bidirectional long- and short-term memory network, a combined SSA-CNN-BiLSTM prediction model is established, and the attention mechanism is used to improve the prediction accuracy. Secondly, a multi-temporal dispatch optimization model of the microgrid power system, which aims at the economic optimization of the system operation cost and the minimization of the environmental cost, is constructed based on the prediction results. Further, differential evolution is introduced into the QPSO algorithm and the model is solved using this improved quantum particle swarm optimization algorithm. Finally, the feasibility of the photovoltaic power generation forecasting model and the microgrid power system dispatch optimization model, as well as the validity of the solution algorithms, are verified through real case simulation experiments. The results show that the model in this paper has high prediction accuracy. In terms of scheduling strategy, the generation method with the lowest cost is selected to obtain an effective way to interact with the main grid and realize the stable and economically optimized scheduling of the microgrid system.

With the proposal of China’s green energy strategy, the research and development technologies of green energy such as wind energy and hydrogen energy are becoming more and more mature. However, the phenomenon of wind abandonment and anti-peak shaving characteristics of wind turbines have a great impact on the utilization of wind energy. Therefore, this study firstly builds a distributed wind-hydrogen hybrid energy system model, then proposes the power dispatching optimization technology of a wind-hydrogen integrated energy system. On this basis, a power allocation method based on the AUKF (adaptive unscented Kalman filter) algorithm is proposed. The experiment shows that the power allocation strategy based on the AUKF algorithm can effectively reduce the incidence of battery overcharge and overdischarge. Moreover, it can effectively deal with rapid changes in wind speed. The wind hydrogen integrated energy system proposed in this study is one of the important topics of renewable clean energy technology innovation. Its grid-connected power is stable, with good controllability, and the DC bus is more secure and stable. Compared with previous studies, the system developed in this study has effectively reduced the ratio of abandoned air and its performance is significantly better than the system with separate grid connected fans and single hydrogen energy storage. It is hoped that this research can provide some solutions for the research work on power dispatching optimization of energy systems.

Grids structure is evolving rapidly in view of contemporary energy policies which ensure the addition of more renewable sources to reduce carbon footprint. Compared to the centralized approach, low voltage grid (decentralized and distributed) are promising approaches to integrate non-dispatchable renewable energy sources (RESs). Installing local micro level power generation sources like fuel cell, microturbine, and energy storage system are a recent trend which helps in intermittent the effect of RESs and makes microgrids less dependable on the main grid. Due to the increasing variety of distributed generation sources having diverse characteristics, power dispatch scheduling of distributed microgrids is getting challenging. A dispatch scheduling solution from an operator's point of view is presented by authors. The core objective of the study is to minimize the carbon emissions and the cost of each microgrid. Further, it is observed that sales and purchases from the main grid are reduced. Consequently, transmission losses are also decreased.

Hydropower facilities represent a key electricity generating resource in the U.S. Western Interconnection. These facilities rely upon forecasts of inflow when scheduling releases to generate electricity. However, hydropower operations represented in bulk power systems models do not reflect uncertainty in inflow forecasts. This study aims to evaluate how inflow forecast uncertainties impact hydropower generation and revenues at the scale of an entire power grid at a spatial scale relevant to power system modeling. The question is critical and timely as more flexibility is called upon to integrate other technologies without understanding the flexibility already exercised. New advances are needed to represent hydropower contributions under operational uncertainty at the interconnection scale. Our contribution includes the development of consistent and coincident medium‐range (0–7 days) inflow forecasts and a generic hydropower scheduler, Forecast‐Informed Scheduler for Hydropower (FIScH), that captures non‐powered water management objectives and constraints and allows for varying electricity prices. This scheduler was applied at 242 hydropower facilities representing 86% of the conventional nameplate capacity in the Western Interconnection. Hydropower revenues were examined for schedules developed using three sets of inflow forecasts with differing levels of accuracy over a 20‐year period from 2000 to 2019. In aggregate, we find that annual hydropower revenue decreases 0.08% when using more skillful forecasts, and 0.11% when using baseline persistence forecasts as compared to revenue using perfect forecasts. Regional and interannual results were more varied and ranged between −1 and 4%. The translation of improved forecast skill into higher revenues is non‐linear and varies regionally, with larger revenue changes on the west coast and smaller responses across the interior western U.S. Overall, we demonstrate that scheduling mostly alleviates the impact of inflow forecast errors on hydropower revenue. The study motivates the need for a more detailed evaluation into which specific hydrologic events impact hydropower scheduling and revenue at the system scale.

Hydropower is the most prevalent source of renewable energy production worldwide. As the global demand for robust and ecologically sustainable energy production increases, developing and enhancing the current energy production processes is essential. In the past decade, machine learning has contributed significantly to various fields, and hydropower is no exception. All three horizons of hydropower models could benefit from machine learning: short-term, medium-term, and long-term. Currently, dynamic programming is used in the majority of hydropower scheduling models. In this paper, we review the present state of the hydropower scheduling problem as well as the development of machine learning as a type of optimization problem and prediction tool. To the best of our knowledge, this is the first survey article that provides a comprehensive overview of machine learning and artificial intelligence applications in the hydroelectric power industry for scheduling, optimization, and prediction.

The research focuses on managing power within renewable flexible integrated energy systems in intelligent distribution systems, considering factors such as harmonic compensation, voltage stability, and environmental indices. The proposed system is based on a deterministic model that aims to optimize four distinct objectives. This objective function collectively minimizes the network’s operating costs, emissions, total voltage harmonics, and the symmetrical value of the voltage stability index. Key constraints involve the operational and flexible models of the renewable integrated energy system, along with the linearized AC harmonic optimal power flow model and voltage stability limits. The study acknowledges inherent uncertainties related to the power output from renewable units, electric vehicles energy, price of energy, and load. To address these uncertainties, adaptive robust optimization is employed to ensure resilient solutions. Results indicate that despite these uncertainties, the operation of SDNs remains robust even with a prediction error margin of up to 45%. Moreover, the proposed system reduces voltage drop by 57.7%, emissions by 49.3%, operational cost by 55.2%, energy loss by 45.4%, and harmonic index by 48.6% under 45% uncertainty. In this condition, voltage stability increases 15%.

Local area packetised-power network (LAPPN) provides flexible local power dispatching in the future energy internet. With interconnections among multiple LAPPNs, power dispatching can be further extended to intra- and inter-LAPPN power interchanges. It becomes a significant issue to schedule the two kinds of power interchanges as, from a system perspective high utilisation of available scheduling time slots and low overall transmission loss should be guaranteed, and from a subscriber perspective a high scheduled ratio of transmission requests with a fair transmission sequence in terms of transmission urgency is expected. To this end, the authors propose a cooperative power dispatching framework for connected LAPPNs, including subscriber matching and coordinated power transmission scheduling. The former matches subscribers from different LAPPNs, considering both subscriber preferences and power transmission loss. The latter coordinates the intra- and inter-LAPPN power packet transmission to maximise the amount of energy delivered with guaranteed fairness on user urgency. Simulation results of a two-LAPPN system are provided, which demonstrate that the proposed framework can achieve effective and efficient power dispatching in terms of the mentioned concerns, and reveal facts on ideal system capacity and how to manipulate the proportions of the two kinds of transmissions according to the network status.