Energy-Efficient Quantum-Spiking Multi-Agent Reinforcement Learning for Adaptive Energy Management in Microgrid Networks

Developing Hybrid Demand Response Technique for Energy Management in Microgrid Based on Pelican Optimization Algorithm

Towards the integration of interconnected microgrids to deregulated electricity markets

In this manuscript, a hybrid method for optimum energy management of micro grid (MG) connected system is proposed. The MG connected sources are Photovoltaic (PV) array, Wind Turbine (WT), Fuel Cell (FC), Micro Turbine (MT), Diesel Generator (DG), battery storage (BESS). The proposed technique is the consolidation of Artificial Neural Network (ANN), Artificial Bee Colony (ABC), and Firefly Algorithm (FA). The proposed method is utilized to maintain the power flow (PF) amid the energy sources and grid. Initially, the predicted load requirement based on the ANN technique predicts the load requirement and ABC optimizes the MG configuration. Finally, the FA technique helps to reduce the FC including operation with maintenance cost. The proposed method is executed in MATLAB/Simulink site and the performance is analyzed with different existing methods like online management, ABC, ABC-ABC, and ABC-Gravitational Search (GS). Here, the maximal power generation of photovoltaic is 5.9 kW, wind turbine is 5.9 kW, micro turbine is 4 kW, FC is 4 kW, DG is 6 kW, and battery is 0.5 kW. The total cost of ABC, ABC-ABC, ABC-GS, and the proposed technique is about 4.1$/h, 3.9$/h, 3.3$/h, and 2.7$/h. Optimum micro grid combination using proposed method, overall cost comparison using different methods, statistical evaluation of MG management, cost accuracy percentage (CAP) of proposed and existing techniques, efficiency under various number of trails are also analyzed in this work. The comparison results demonstrate that the proposed technique has minimal cost-efficient based on its load requirement.

This paper investigates the application of ant colony optimization (ACO) for energy management in microgrids, incorporating distributed generation resources such as solar panels, fuel cells, wind turbines, battery storage, and microturbine. The study evaluates energy management in two scenarios, with the first utilizing all available resources and the second applying specific operational constraints to wind turbines and solar panels. Results demonstrate that ACO effectively reduces operational costs and enhances system reliability, contributing to sustainable energy management practices. Additionally, the study compares ACO with the imperialist competitive algorithm, highlighting ACO’s superior performance in cost reduction and efficiency improvements. This research advances energy system optimization and supports the development of sustainable solutions in microgrids.

Microgrids (MGs) are essential in advancing energy systems towards a low-carbon future, owing to their highly efficient network architecture that facilitates the flexible integration of various DC/AC loads, distributed renewable energy sources, and energy storage systems. They also offer enhanced resilience and cost-effective control, operation, and energy management for both on-grid and off-grid scenarios. Nevertheless, as new entrants to the utility grid, MGs encounter challenges stemming from the economic deregulation of energy systems, the restructuring of generation, and market-driven operations. This paper provides a thorough overview of existing research on MGs .It categorizes MGs and energy storage systems into several branches and typical combinations, which serve as the foundation for MG energy management.

This paper addresses the optimal energy management and scheduling of renewable based microgrids (MGs) in the highly correlated uncertain environments. The proposed MG incorporates different types of dispatchable and non-dispatchable units such as micro turbine (MT), fuel cell (FC), wind turbine (WT), photovoltaic panel (PV) and a battery bank (BAT). Due to the nonlinear and non-convex nature of the proposed problem, a heuristic approach based on the bird mating optimization (BMO) algorithm is employed to solve the problem. The uncertainties associated with the market price (MP), PV output power, WT output power and hourly load uncertainties are modeled using the unscented transformation (UT) method. In addition, two sampling procedures are adopted, namely as the (2m+1) and the (2m) point methods, to provide a comparative analysis showing the highly capabilities of the proposed framework. Moreover, the correlation among uncertain variables is taken into consideration and their effects on the MG's optimal operation are analyzed. The simulation results reveal the high accuracy, effectiveness, relatively timesaving and tending to avoid local minimums by the proposed model.

Switched model predictive control (S-MPC) and recurrent neural networks with long short-term memory (RNN-LSTM) are powerful control methods that have been extensively studied for the energy management of microgrids (MGs). These methods ease constraint satisfaction, computational demands, adaptability, and comprehensibility, but typically one method is chosen over the other. The S-MPC method dynamically selects optimal models and control strategies based on the system’s operating mode and performance objectives. On the other hand, integration of auto-regressive (AR) control with these powerful control methods improves the prediction accuracy and the adaptability of the system conditions. This paper compares the two control approaches and proposes a novel algorithm called switched auto-regressive neural control (S-ANC) that combines their respective strengths. Using a control formulation equivalent to S-MPC and the same controller model for learning, the results indicate that pure RNN-LSTM cannot provide constraint satisfaction. The novel S-ANC algorithm can satisfy constraints and deliver comparable performance to MPC, while enabling continuous learning. The results indicate that S-MPC optimization increases power flows within the MG, resulting in efficient utilization of energy resources. By merging the AR and LSTM, the model’s computational time decreased by nearly 47.2%. In addition, this study evaluated our predictive model’s accuracy: (i) the R-squared error was 0.951, indicating a strong predictive ability, and (ii) mean absolute error (MAE) and mean square error (MSE) values of 0.571 indicate accurate predictions, with minimal deviations from the actual values.

Energy Management (EM) in hybrid Microgrids (MGs) is essential for coordinating Renewable Energy Sources (RESs) and Hybrid Energy Storage Systems (HESSs) to ensure Power Quality (PQ), stable operation, and efficient power flow. Existing optimization–prediction approaches often address these issues in isolation or require high computational overhead, limiting their real-time applicability. To overcome these challenges, this paper proposes a novel dual-optimization framework combining the Artificial Lemming Algorithm (ALA) with the Temporal Kolmogorov–Arnold Network (TKAN), referred to as ALA-TKAN. Unlike conventional methods, ALA-TKAN integrates metaheuristic-based optimization of power flow and HESS scheduling with sequence-aware forecasting of load and renewable generation, enabling proactive and coordinated EM under dynamic conditions. Implemented in MATLAB, the proposed method demonstrates superior performance compared with state-of-the-art techniques such as PDO-MACNN, BWO, PSO, ANN, and MRA-FLC, achieving minimal power loss (2.9 MW), highest efficiency (99.2%), lowest energy cost (0.8 $/Wh), and reduced THD (1.4%). These results confirm the novelty and practical potential of ALA-TKAN as a unified, computationally efficient strategy for PQ enhancement and reliable operation of hybrid MGs.

In this paper, we present a Value-Decomposition Deep Deterministic Policy Gradients (V3DPG) based Reinforcement Learning (RL) method for energy trading and management of regional interconnected microgrids (MGs). In practice, the state of an MG is time-varying and the traded energy flows continuously, which is generally neglected in researches. To address this problem, an Actor-Critic framework is adopted. Each MG has to make energy trading decision based on local observation and has no access to any knowledge of other MGs. We bring in the idea of value-decomposition in the training process to ensure the generation of feasible cooperative policies while maintaining MGs' privacy and autonomous decision-making ability. Furthermore, in light of the uncertainty and fluctuation of renewable energy generation and users' demand, a recurrent neural network (RNN) with Burn-In initialization is combined with critic network to achieve implicit predictions. Meanwhile, we also take Energy Storage System (ESS) with operational constraints into consideration and deem it as a virtual market innovatively. Experiments have been carried out under real-world data to verify the merit of the proposed method, compared to existing RL-based works.

Energy management of cooperative microgrids: A distributed optimization approach

Renewable energy sources have emerged as an alternative to meet the growing demand for energy, mitigate climate change, and contribute to sustainable development. The integration of these systems is carried out in a distributed manner via microgrid systems; this provides a set of technological solutions that allows information exchange between the consumers and the distributed generation centers, which implies that they need to be managed optimally. Energy management in microgrids is defined as an information and control system that provides the necessary functionality, which ensures that both the generation and distribution systems supply energy at minimal operational costs. This paper presents a literature review of energy management in microgrid systems using renewable energies, along with a comparative analysis of the different optimization objectives, constraints, solution approaches, and simulation tools applied to both the interconnected and isolated microgrids. To manage the intermittent nature of renewable energy, energy storage technology is considered to be an attractive option due to increased technological maturity, energy density, and capability of providing grid services such as frequency response. Finally, future directions on predictive modeling mainly for energy storage systems are also proposed.

The uncertainty of load is a challenging issue for power systems. These uncertainties can damage the power systems and even cause blackout. Therefore, it is necessary to handle this problem. In this paper, a method for energy management in microgrids with stochastic load is proposed. The design is performed through solving a nonlinear optimization problem. In the centralized optimization technique, high computational capabilities are needed and the customer's privacy might be infringed. On the contrary, in distributed techniques, the power network and its constraints are neglected since it is assumed that all loads and generations are connected to one bus. Consequently, in the proposed distributed energy management strategy (EMS), the optimization problem is decomposed into two optimization levels, which consist of the microgrid centralized controller and local controllers and the distribution network and the corresponding constraints are taken into account. Since the precise prediction of the load is not accessible, the load is assumed stochastic. The proposed distributed EMS is integrated with diesel generators, wind turbines, photovoltaics, and a battery storage system. The simulation results depict the efficiency of the proposed distributed EMS.

In terms of microgrids (MGs) operation, optimal control and management are vital issues that must be addressed carefully. This paper proposes a practical framework for the optimal energy management and control of renewable MGs considering energy storage (ES) devices, wind turbines, and microturbines. Due to the non‐linearity and complexity of operation problems in MGs, it is vital to use an accurate and robust optimization technique to control the power flow of units efficiently. To this end, in the proposed framework, teacher learning‐based optimization (TLBO) is utilized to solve the power flow dispatch in the system efficiently. Moreover, a novel hybrid deep learning model based on principal component analysis (PCA), convolutional neural networks (CNN), and bidirectional long short‐term memory (BLSTM) is proposed to address the short‐term wind power forecasting problem. The feasibility and performance of the proposed framework and the effect of wind power forecasting on operation efficiency are examined using the IEEE 33‐bus test system. Also, the Australian Woolnorth wind site data is utilized as a real‐world dataset to evaluate the performance of the forecasting model. The results show that the proposed framework can be used to schedule MGs in the best way possible.

A novel approach for sizing thermal and electrical energy storage systems for energy management of islanded residential microgrid

This paper presents a stochastic optimization framework for microgrid (MG) energy management, integrating electric bicycle (E-Bike) and electric vehicle (EV) charging stations with a green certificate market (GCM) to enhance sustainability and economic efficiency. Uncertainties in renewable energy generation (solar and wind) and load demand are modeled using the Two-Point Estimation Method (TPEM), enabling robust handling of variability. A novel metaheuristic algorithm, Mountaineering Team-Based Optimization (MTBO), is developed to solve a three-objective optimization problem: minimizing operational costs, minimizing emissions, and maximizing GCM revenue. MTBO is benchmarked against particle swarm optimization, achieving a 21.6% reduction in operational costs and a 13.12% reduction in emissions in deterministic scenarios. Three cases are analyzed: (I) no mobile storage, (II) deterministic mobile storage, and (III) stochastic management. In case II, integrating mobile storage (EVs and E-Bikes) with V2G capabilities reduces operational costs by 18.6% and emissions by 10.9% compared to case I. Case III, incorporating stochastic management, further lowers costs by 1.5% and by 19.8% relative to case I, but increases demand response costs by 7.5% and reduces GCM revenue by 38.9% due to renewable fluctuations. These results highlight the practical benefits of combining E-Bike and EV mobile storage with GCM trading in microgrids, demonstrating MTBO's superior exploration and exploitation capabilities for high-dimensional, uncertainty-aware energy scheduling.