Peer-to-peer (P2P) energy trading is a promising energy trading mechanism due to the deployment of distributed energy resources in recent years. Trading energy between prosumers and consumers in the local energy market is undergoing massive research and development, paying significant attention to the business model of the energy market. In this paper, an extensive review was conducted on the current research in P2P energy trading to understand the business layer of the energy market concerning business model dimensions: bidding strategies and the market-clearing approach. Different types of game theoretical-based and auction-based market-clearing mechanisms are investigated, including a detailed classification of auctions. This study considers the possibility of employing the P2P technique in developing countries and reviewing existing business models and trading policies. The business layer of the P2P structure plays a vital role in developing an effective trading mechanism based on interactive energy markets.

Microgrids (MGs) have evolved as critical components of modern energy distribution networks, providing increased dependability, efficiency, and sustainability. Effective control strategies are essential for optimizing MG operation and maintaining stability in the face of changing environmental and load conditions. Traditional rule-based control systems are extensively used due to their interpretability and simplicity. However, these strategies frequently lack the flexibility for complex and changing system dynamics. This paper provides a novel method called hybrid intelligent control for adaptive MG that integrates basic rule-based control and deep learning techniques, including gated recurrent units (GRUs), basic recurrent neural networks (RNNs), and long short-term memory (LSTM). The main target of this hybrid approach is to improve MG management performance by combining the strengths of basic rule-based systems and deep learning techniques. These deep learning techniques readily enhance and adapt control decisions based on historical data and domain-specific rules, leading to increasing system efficiency, stability, and resilience in adaptive MG. Our results show that the proposed method optimizes MG operation, especially under demanding conditions such as variable renewable energy supply and unanticipated load fluctuations. This study investigates special RNN architectures and hyperparameter optimization techniques with the aim of predicting power consumption and generation within the adaptive MG system. Our promising results show the highest-performing models indicating high accuracy and efficiency in power prediction. The finest-performing model accomplishes an $R^2$ value close to 1, representing a strong correlation between predicted and actual power values. Specifically, the best model achieved an $R^2$ value of 0.999809, an MSE of 0.000002, and an MAE of 0.000831.

Microgrids (MGs) are a growing energy industry segment and represent a paradigm shift from remote central power plants to more localized distributed generation. Controlling MGs represents a challenge mainly due to their complexity and the different properties each asset in the MG has. Various methods have been proposed to address this challenging problem of MG control. Some of these methods are considered the optimal operation of MG assets. Other works are based on a systems approach and address the scalability and simplicity of synthesizing a MG’s energy management system (EMS). ε-variables based logical control strategies, which are practical methods to model control strategies in MGs, can make the control structure more scalable. However, this method is not optimal. On the other hand, Switched Model Predictive Control (S-MPC) is an advanced method utilized to control power systems while satisfying several constraints to achieve an optimal solution based on various criteria. Nevertheless, its implementation is not straightforward. Therefore, to overcome these existing problems, this paper proposes a novel systems approach method called an extended optimal ε-variable method developed by combining the ε-variable based control method with the S-MPC method. This unique method has demonstrated a significant improvement in optimizing an MG’s energy management and enhanced the adaptation and scalability of a control structure of the MG. Our results show that the proposed extended optimal ε-variable method: (i) reduces the operational cost of MG by nearly 35%; (ii) reduces the usage of the battery energy storage system by 42%, and (iii) enhances the practicality of photovoltaic (PV) usage by 28%. Our novel extended optimal ε-variable technique also increases the adaptation and scalability of the control structure of the MG significantly by translating the results of S-MPC to the ε-variable method.

Accurate short-term load forecasting is vital for the reliable and efficient operation of smart grids, particularly under the uncertainty introduced by variable renewable energy sources (RESs) such as solar and wind. This study introduces ST-CALNet, a novel hybrid deep learning framework that integrates convolutional neural networks (CNNs) with an Attentive Long Short-Term Memory (LSTM) network to enhance forecasting performance in renewable-integrated smart grids. The CNN component captures spatial dependencies from multivariate inputs, comprising meteorological variables and generation data, while the LSTM module models temporal correlations in historical load patterns. An embedded attention mechanism dynamically weights input sequences, enabling the model to prioritise the most influential time steps, thereby improving its interpretability and robustness during demand fluctuations. ST-CALNet was trained and evaluated using real-world datasets that include electricity consumption, solar photovoltaic (PV) output, and wind generation. Experimental evaluation demonstrated that the model achieved a mean absolute error (MAE) of 0.0494, root mean squared error (RMSE) of 0.0832, and a coefficient of determination (R²) of 0.4376 for electricity demand forecasting. For PV and wind generation, the model attained MAE values of 0.0134 and 0.0141, respectively. Comparative analysis against baseline models confirmed ST-CALNet’s superior predictive accuracy, particularly in minimising absolute and percentage-based errors. Temporal and regime-based error analysis validated the model’s resilience under high-variability conditions such as peak load periods, while visualisation of attention scores offered insights into the model’s temporal focus. These findings underscore the potential of ST-CALNet for deployment in intelligent energy systems, supporting more adaptive, transparent, and dependable forecasting within smart grid infrastructures.

The energy sector is undergoing a paradigm shift among all the stages, from generation to the consumer end. The affordable, flexible, secure supply–demand balance due to an increase in renewable energy sources (RESs) penetration, technological advancements in monitoring and control, and the active nature of distribution system components have led to the development of microgrid (MG) energy systems. The intermittency and uncertainty of RES, as well as the controllable nature of MG components such as different types of energy generation sources, energy storage systems, electric vehicles, heating, and cooling systems are required to deploy efficient energy management systems (EMSs). Multi-agent systems (MASs) and model predictive control (MPC) approaches have been widely used in recent studies and have characteristics that address most of the EMS challenges. The advantages of these methods are due to the independent characteristics and nature of MAS, the predictive nature of MPC, and their ability to provide affordable, flexible, and secure MG operation. Therefore, for the first time, this state-of-the-art review presents a classification of the MG control and optimization methods, their objectives, and help in understanding the MG operational and EMS challenges from the perspective of the energy trilemma (flexibility, affordability, and security). The control and optimization architectures achievable with MAS and MPC methods predominantly identified and discussed. Furthermore, future research recommendations in MG-EMS in terms of energy trilemma associated with MAS, MPC methods, stability, resiliency, scalability improvements, and algorithm developments are presented to benefit the research community.

High penetration of intermittent renewable energy sources (RES) and unexpected disruptions (e.g., natural disasters) are fundamental challenges which can threaten the secure operation of microgrids, especially during the islanded condition, with no support from the upstream grid. This paper introduces a hierarchical tri-layer min-max-min joint risk- and security-constrained model predictive control (RSC-MPC) framework for real-time energy scheduling of islanded microgrids (IMGs) under the influence of uncertainty and real-time time-varying contingency conditions. While the first layer processes a pre-scheduling day-ahead optimisation, the second layer detects the worst-case contingency conditions by maximising the load curtailment and the mismatch between pre-scheduling (i.e., first layer) and real-time operation. The third layer implements the corrective security measures to minimise the negative effect of contingency conditions while accounting to the cost associated with the risk of uncertainty in the forecasted inputs. The third layer also explores the economic effects of the RES’ uncertainty on the proposed RSC-MPC, considering the risk and energy procurement cost as conflicting objectives. The computational efficiency of the proposed hierarchical control system in terms of accuracy and processing time is guaranteed through a mixed integer conic programming model. The proposed RSC-MPC is tested in different case studies and its efficiency is validated by numerical results.