Akhtar Hussain

The performance of networked microgrids primarily depends on the design of internal market structure for maximum resource utilization, optimized power sharing, and enhanced economic efficiency. This article presents a novel framework for resource optimization within the networked microgrids. First, each microgrid is optimized using a local energy management system to compute power shortage/surplus depending on various parameters including local generation (solar photovoltaic), battery energy storage system, and load profile. The total shortage/surplus is obtained by aggregating the shortage/surplus of each microgrid, facilitating the calculation of adaptive internal trade price. The internal trade prices are responsive to load variations and time-of-use prices, thereby encouraging internal trading within the networked microgrids. The internal trading price is strategically set to be lower than buying price from the grid and higher than selling price to the grid to maximize overall revenue of the entire network. Subsequently, a central energy management system is formulated, determining optimized power sharing between microgrids based on the adaptive internal trading. The proposed strategy is validated on the IEEE-33 bus test feeder using improved accelerated particle swarm optimization, achieving an annual power loss reduction of 1795.8 kW and an overall cost reduction of 7.2%. These results confirm the practicality and effectiveness of the proposed framework in real-world scenarios.

Microgrids are considered a practical solution to revolutionize power systems due to their ability to island and sustain the penetration of renewables. Most existing studies have focused on the optimal management of microgrids with a fixed configuration. This restricts the application of developed algorithms to other configurations without major modifications. The objective of this study is to design a rule-based modular energy management system (EMS) for microgrids that can dynamically adapt to the microgrid configuration. To realize this framework, first, each component is modeled as a separate entity with its constraints and bounds for variables. A wide range of components such as battery energy storage systems (BESSs), electric vehicles (EVs), solar photovoltaic (PV), microturbines (MTs), and different priority loads are modeled as modules. Then, a rule-based system is designed to analyze the impact of the presence/absence of one module on the others and update constraints. For example, load shedding and PV curtailment can be permitted if the grid module is not included. The constraints of microgrid components present in any given configuration are communicated to the EMS, and it optimizes the operation of the available components. The configuration of microgrids could be as simple as flexible loads operating in grid-connected mode or as complex as a hybrid microgrid with AC and DC buses with a diverse range of equipment on each side. To facilitate the realization of diverse configurations, a hybrid AC/DC microgrid is considered where the utility grid and interlinking converter (ILC) are also modeled as separate modules. The proposed method is used to test performance in both grid-connected and islanded modes by simulating four typical configurations in each case. Simulation results have shown that the proposed rule-based modular method can optimize the operation of a wide range of microgrid configurations.

The integration of solar power systems into cruising ships is gaining popularity in the marine sector due to the restrictions imposed by the Marine Pollution Protocol and the rapid growth of photovoltaic (PV) technology. However, this integration brings various challenges in the operation of the ship energy system, including resource uncertainty, power imbalance, and reduced service reliability. Therefore, this study proposes a novel three‐stage operation strategy for ship multienergy systems to compensate for the uncertainty of PV generation. In Stage 1, a day‐ahead scheduling process is performed to determine the setpoints of major system components. The goal is to minimize operating costs while meeting electrical, heating, and cooling demands. In Stage 2, a deep neural network‐based PV prediction model is developed. Particle swarm optimization is used to achieve fast convergence and high accuracy. A detailed statistical analysis is then applied for early detection of data drift, which may cause a significant drop in prediction accuracy. The uncertainty of PV output is then estimated based on the new trends observed in the incoming dataset. In Stage 3, a demand response (DR)‐based scheme is introduced to compensate for the uncertainty of PV power, identified in Stage 2. The DR programs allow sharing the load demand among different intervals by adjusting controllable loads. As a result, the amount of power mismatches caused by the uncertainty factor has decreased. Finally, simulation results also demonstrate that the amount of load shedding requirement in the ship energy system is significantly reduced using the proposed method.

With the continuous rise in the energy consumption of buildings, the study and integration of net-zero energy buildings (NZEBs) are essential for mitigating the harmful effects associated with this trend. However, developing an energy management system for such buildings is challenging due to uncertainties surrounding NZEBs. This paper introduces an optimization framework comprising two major stages: (i) renewable energy prediction and (ii) multi-objective optimization. A prediction model is developed to accurately forecast photovoltaic (PV) system output, while a multi-objective optimization model is designed to identify the most efficient ways to produce cooling, heating, and electricity at minimal operational costs. These two stages not only help mitigate uncertainties in NZEBs but also reduce dependence on imported power from the utility grid. Finally, to facilitate the deployment of the proposed framework, a graphical user interface (GUI) has been developed, providing a user-friendly environment for building operators to determine optimal scheduling and oversee the entire system.

With the continuous rise in the energy consumption of buildings, the study and integration of net-zero energy buildings (NZEBs) are essential for mitigating the harmful effects associated with this trend. However, developing an energy management system for such buildings is challenging due to uncertainties surrounding NZEBs. This paper introduces an optimization framework comprising two major stages: (i) renewable energy prediction and (ii) multi-objective optimization. A prediction model is developed to accurately forecast photovoltaic (PV) system output, while a multi-objective optimization model is designed to identify the most efficient ways to produce cooling, heating, and electricity at minimal operational cost. These two stages not only help mitigate uncertainties in NZEBs but also reduce dependence on imported power from the utility grid. Finally, to facilitate the deployment of the proposed framework, a graphical user interface (GUI) has been developed, providing a user-friendly environment for building operators to determine optimal scheduling and oversee the entire system.

Renewable distributed generations are associated with generation intermittency. Exacerbated by the consumption and demand uncertainty and their resulting mismatch, its energy trading suffers similar uncertainty. The situation is severe in the standalone distributed generations (SDG) for lacking transaction access to the utility grid. This paper proposes the energy transaction time determination and minimization algorithm for consumers in the SDG arena. First, blockchain technology is adopted for transaction enhancement and transaction data acquisition. The acquired blockchain data includes the hourly nodes (number of blockchain members), transaction sizes, and corresponding transaction durations. Next, the blockchain-recorded transaction data are fitted using the linear regression (LR) algorithm to obtain their fitting formula. The fitting formula was subsequently optimized in hourly intervals to obtain the optimal transaction time (energy delivery time) using particle swarm optimization (PSO). Finally, the optimal results are presented in a decision tree (DT) to energy consumers in the blockchain platform. Consequently, their transaction decision-making is guided by the result against the inherent transaction time uncertainty. Consumers can thus correctly adjust their transaction habits to suitably adapt to the transaction duration fluctuations in the energy trading arena. Energy transaction delays and transaction costs are consequently minimized leading to greater penetration of renewables and bridging the generation and consumption gap.