• Energy Research

Concerns about the impact of global warming caused by air pollution and depletion of fossil fuels have attracted attention and opportunities in transportation especially in maritime industry. In all electric ships, the electrical equipment including electric propulsion is connected to the common ship electrical network to achieve better fuel consumption with less emission. However, the low-load factor of the parallel diesel generators (DGs) in some operating conditions, can negatively affect the fuel consumption rate. As an alternative, two or more power sources such as batteries and renewable-based prime movers can be integrated into the system aboard to improve the overall system performance. By optimal scheduling of the power sources, poor low-load efficiency can be avoided and controllable units can be dispatched in an emission-aware and cost-effective manner. This paper analyzes how much the operating cost of a shipboard system can be improved (based on estimation of specific fuel consumption (SFC) curve of a real system) considering the dynamic load profile with and without energy storage systems (ESSs). The case study in this paper is a ferry with a conversion from traditional diesel mechanical power to electrical propulsion powered by DGs and ESSs.

A grid-connected photovoltaic system is the leastexpensive and lowest-maintenance option for a home solarelectric system. This system consists of solar cells for energyextraction from the sun and power converters for grid interfacethat converts the output dc voltage into ac. In this paper, aninnovative control method for grid-connected PV converter isproposed. This method can control the injection of power createdby solar arrays into the network; compensate reactive power andharmonics of load currents. Furthermore, the proposed controlmethod permits the supplying of the load power consumption(active and reactive), in the case of grid failure. One of the mostsignificant benefits of this technique is that if the power producedby solar arrays is less than the power of inverters, the rest of thecapacity can be utilized for compensating the reactive power andfiltering harmonics of load currents, so as to make the currentdrawn from the grid completely sine at unity power factor.

Artificial intelligence (AI) and machine learning (ML) can assist in the effective development of the power system by improving reliability and resilience. The rapid advancement of AI and ML is fundamentally transforming energy management systems (EMSs) across diverse industries, including areas such as prediction, fault detection, electricity markets, buildings, and electric vehicles (EVs). Consequently, to form a complete resource for cognitive energy management techniques, this review paper integrates findings from more than 200 scientific papers (45 reviews and more than 155 research studies) addressing the utilization of AI and ML in EMSs and its influence on the energy sector. The paper additionally investigates the essential features of smart grids, big data, and their integration with EMS, emphasizing their capacity to improve efficiency and reliability. Despite these advances, there are still additional challenges that remain, such as concerns regarding the privacy of data, challenges with integrating different systems, and issues related to scalability. The paper finishes by analyzing the problems and providing future perspectives on the ongoing development and use of AI in EMS.

This paper proposes a robust optimization framework for energy hub management. It is well known that the operation of energy systems can be negatively affected by uncertain parameters, such as stochastic load demand or generation. In this regard, it is of high significance to propose efficient tools in order to deal with uncertainties and to provide reliable operating conditions. On a broader scale, an energy hub includes diverse energy sources for supplying both electrical load and heating/cooling demands with stochastic behaviors. Therefore, this paper utilizes the Information Decision Gap Theory (IGDT) to tackle this uncertainty as an efficient robust optimization tool with low complexity to ensure the optimal operation of the system according to the priorities of the decision maker entity. The proposed optimization framework is also implemented on a benchmark energy hub which includes different energy sources and evaluated under different working conditions.

The modern power system is progressing from a system based on synchronous generators toward systems with high penetration of renewable energy sources (RESs) such as photovoltaic (PV) and wind power generating units which are connected to the grid through inverters. RES units will represent a significant share of the power generation in near future; therefore, the conventional approach of integrating them into the grid may lead to frequency instability. Many researchers have suggested the use of inverters with virtual inertial control methods to act as synchronous generators in the grid and maintain and increase the frequency stability. This paper presents a comprehensive overview of virtual inertial strategies and current control strategies and makes a comprehensive comparison while describing their characteristics. Then, different types of stability analyses in the presented methods are examined and examples of each are presented. In continuation and in addition to the review studies conducted in this field, methods presented with the aim of improving the virtual inertial control are carefully examined and their characteristics according to the number of resources used, the adaptivity of parameters, the use of optimization methods, the issue of coordination between several resources and the type of communication network are studied. Moreover, a comprehensive review of multiple- virtual synchronous generator (VSG) methods to develop and implement the concept of virtual inertia in weak grids are presented. Finally, a discussion of challenges and research directions is presented, particularly pointing to the integration of multiple virtual inertial units at the system level.

As one of the largest frequency regulation markets, the Pennsylvania-New Jersey-Maryland Interconnection (PJM) market allows extensive access of Battery Energy Storage Systems (BESSs). The designed signal regulation D (RegD) is friendly for use with BESSs with a fast ramp rate but limited energy. Designing operating strategies and optimizing the sizing of BESSs in this market are significantly influenced by the regulation signal. To represent the inherent randomness of the RegD signal and reduce the computational burden, typical frequency regulation scenarios with lower resolution are often generated. However, due to the rapid changes and energy neutrality of the RegD signal, generating accurate and representative scenarios presents challenges for the methods based on shape similarity. This paper proposes a novel probability-based method for generating typical regulation scenarios. The method relies on the joint probability distribution of two features with a 15-min resolution, extracted from the RegD signal with a 2 s resolution. The two features can effectively portray the characteristic of RegD signal and its influence on BESS operation. Multiple regulation scenarios are generated based on the joint probability distributions of these features at first, with the final typical scenarios chosen based on their probability distribution similarity to the actual distribution. Utilizing regulation data from the PJM market in 2020, this paper validates and analyzes the performance of the generated typical scenarios in comparison to existing methods, specifically K-means clustering and the forward scenarios reduction method.

Integrated distribution systems (IDSs) and multi-carrier energy microgrids (MCEMs) can play a crucial role in enhancing distribution energy systems’ overall efficiency and flexibility. By cascading energy usage and cooperating through energy trading, IDSs and MCEMs can reduce overall system costs and provide more flexibility for system operators. Adding resilience to the planning problem of IDSs can reduce planning costs in the long term, as proactive preparedness is key to coping with high-impact rare (HR) events. Adding resilience to the planning problem of IDSs can reduce the planning costs in the long term since proactive preparedness is a key necessity to cope with high-impact rare (HR) events. This paper proposes a resilience-oriented stochastic tri-level and two-stage cooperative expansion planning of IDSs and MCEMs, considering energy trading between IDSs and MCEMs. The first stage comprises two levels; the first level minimizes the investment and operation costs of IDSs and MCEMs, while the second level desires to maximize the energy exchange profit for MCEMs and thus reduce the overall costs. The second stage includes the third level problem involving two objective functions: resilience cost minimization and resilience index (RI) maximization. The multi-objective problem in the second stage is converted into a single-objective problem using the min–max regret method. The DC and AC configurations for the power distribution system (PDS) and power microgrids (PMGs) are studied to identify the optimal configuration of these networks in the expansion planning problem. A new framework is proposed based on an aggregator-agent splitting solution using the aggregator coupling coordinator unit (ACC) responsible for coordinating IDNs and MCEMs. The studied large-scale complex optimization problem is efficiently solved computationally by introducing a combined adaptive dynamic programming (ADP) and linearized alternating direction method of multipliers with parallel splitting (LADMMPSAP) algorithm. Three cases are studied to demonstrate the effectiveness of the proposed model and method. The results depict that MCEMs help reduce expansion planning costs and improve the system’s resilience. Adding resilience to the expansion planning problem enhances the resilience of the whole system and simultaneously reduces the costs by 2.7%. The expansion planning costs for the AC and DC configuration are close, and the AC is the optimal choice in all case studies. By increasing the planning horizon from 5 to 10 years, DC will be the optimal solution since network reinforcement costs and power losses are significantly lower.

Dynamic line rating (DLR) is considered a key concept in transmission lines that can guarantee the variable nature of renewable energy sources with minimal economic constraints. So far, various schemes have been selected for DLR forecasting that offers acceptable capacity but require measuring instruments and communication networks with precise calibration on the conductor surface, which in addition to high economic costs, are always available for cyber attackers. In this study, to forecast the DLR values, a deep learning-based technique called long short-term memory (LSTM) is proposed. Additionally, a novel data integrity attack detection approach based on image processing is developed to maintain the performance of the forecasting model against cyber-attacks. The LSTM forecasts the DLR values of an overhead transmission line located in Tabriz, Iran, using meteorological parameters as input data. The forecasting results confirm the high performance of the LSTM model with minimal error values. Then, a scaling attack is applied as a known data integrity attack on the input variables of wind speed and wind direction to evaluate the performance of the LSTM network against cyber-attacks. The results of this scenario show that a cyber-attack can significantly reduce the accuracy of the forecasting. To prevent this, the image processing-based technique detects and clearly displays the cyber-attacks in each of the input variables by converting the input data parameters to 2-D images.