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Peer-to-peer energy trading is becoming an efficient methodology for trading flexibility between buildings due to the increasing utilisation of small-scale generation and storage technologies. In the buildings, however, this trading mechanism could be affected by occupant comfort and uncertainty around it, affecting the building operation and consumption. This study introduces a multi-level peer-to-peer energy trading framework for residential buildings under the influence of occupants preferences. The proposed method considers the effect of occupants comfort as an important factor on the control and energy management of buildings in local markets. The robustness of the proposed real-time control framework in face of uncertainty in a real-life building parameter (i.e. occupants comfort level) is improved through the-state-of-the-art information gap decision theory technique. This method requires very little information about uncertain parameters, making it a suitable technique for dealing with the uncertainty in parameters with unknown patterns. Finally, the operational models of energy storage and electric vehicles are adopted for full utilisation of available photovoltaic generation. The simulation results show that participating in the local energy trading can increase the robustness of the control systems in the residential microgrids in face of uncertainty in the occupant comfort level. Also, results show that 0.1% increase in the uncertainty radius of occupants comfort level requires a 6% increase in the energy bill. This shows the importance of considering the occupant comfort in the conventional building energy management strategies, and uncertainty around it on the energy bill.

Seaports are responsible for consuming a large amount of energy and producing a sizeable amount of environmental emissions. However, optimal coordination and cooperation present an opportunity to transform this challenge into an opportunity by enabling flexibility in their generation and load units. This paper introduces a coordination framework for exploiting flexibility across multiple ports. The proposed method fosters cooperation between ports in achieving lower environmental emissions while leveraging flexibility to increase their revenue. This platform allows ports to participate in providing flexibility for the energy grid through the introduction of a green port-to-grid concept while optimising their cooperation. Furthermore, the proximity to offshore wind farms is considered an opportunity for the ports to investigate their role in harnessing green hydrogen. The proposed method explores the hydrogen storage capability of ports as an opportunity for increasing the techno-economic benefits, particularly through coupling them with offshore wind farms. Compared to existing literature, the proposed method enjoys a comprehensive logistics-electric model for the ports, a novel coordination framework for multi-port flexibility, and the potentials of hydrogen storage for the ports. These unique features position this paper a valuable reference for research and industry by demonstrating realistic cooperation among ports in the energy network. The simulation results confirm the effectiveness of the proposed port flexibility coordination from both environmental and economic perspectives.

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). $\varepsilon $ -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 $\varepsilon $ -variable method developed by combining the $\varepsilon $ -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 $\varepsilon $ -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 $\varepsilon $ -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 $\varepsilon $ -variable method.

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 R2 value close to 1, representing a strong correlation between predicted and actual power values. Specifically, the best model achieved an R2 value of 0.999809, an MSE of 0.000002, and an MAE of 0.000831.

Abstract Automatic reconfiguration is one of the key actions in self‐healing distribution networks. In these networks, after detecting and isolating the faulted portion, an automatic reconfiguration procedure is performed to restore the maximum possible affected loads without further interruptions during repair operations. This procedure becomes more complicated in the networks with integrated distributed generation units as they can bring security challenges for the reconfigured network after a fault event. To overcome these challenges, a stochastic framework is proposed here. In this framework, the reconfiguration procedure is conducted with a fast and reliable method which is based on the graph theory. Besides, the security challenges of utilizing distributed generations after an event are highlighted. Then, since a faulted network is more prone to subsequent faults, different actions of changing the distribution generations output power, preventing the insecure increment of short circuit capacity, and also considering the loadability improvement are proposed in the reconfiguration framework. Then in the final stage, the vulnerability of the distribution system to the uncertainties of load demand is resolved through a chance‐constrained programming‐based approach. To see the performance of the proposed stochastic framework, it is tested on a standard test system and the results prove its goodness and applicability for real distribution networks.

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.

Ensuring the security and quality of supply in a power system after a contingency event is one of the most challenging tasks for an electricity system operator. This work is initiated by this challenge and proposes a solution based on the use of provided reserves by fast generators, storage devices, and wind farms. A coordinated model is proposed in a joint energy and reserves market considering their corresponding cost to ensure the adequacy in the simultaneous deployment of reserves for the different sources of uncertainties. The Benders decomposition approach is used in the modeling of the stochastic security-constrained unit commitment, and considering the large-scale and complex nature of the model, acceleration techniques are suggested to reduce the execution time. The proposed model is tested on the 6-bus and the IEEE 118-bus test systems. Numerical results show that the optimal values of reserves successfully address contingencies in both of the critical and normal periods after the contingencies and the optimal solution is calculated in a reasonable computing time.

Over the last decades, environmental concerns and the global tendency to reduce the use of fossil fuels and replacing them with renewable energy sources (RESs) to face the increasing rate of greenhouse gas (GHG) emissions have increased. Buildings consume a significant amount of energy and therefore, they are responsible for a noticeable part of the total GHG emission. Thus, when we talk about decarbonization of the energy systems, buildings are an important sector of the energy system that needs to be considered. Using RESs, smart technologies, and information and communication technologies along with the improvement in energy efficiency, are a number of endeavors to increase the role of building on the way toward decarbonization. In the new environment, the buildings are not passive players of the energy systems and they are able to take an active role and participate in the energy-efficient operation. While they are able to manage their resources and serve the local energy requirements of the residents in the best possible manner, they can participate in the energy and balancing markets and support the network operators as a service provider. In this paper, we present a comprehensive review of active buildings’ concept, challenges and outlook to pave the way for the researchers from academia and industry who want to start working in this area.

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.

Summary In this article, a method to study the nonlinear dynamics of a PV‐fed boost converter is developed. The model for the solar panel introduces an algebraic constraint into the system and the resulting mathematical model is a set of hybrid differential algebraic equations (DAEs). The behavior of the system is observed to exhibit undesired operation as parameter values vary. Conventional mathematical tools developed to analyze DC‐DC converters are not directly applicable to systems modeled as a set of hybrid DAEs. In order to find the monodromy matrix to assess the eigenvalues of the system, we modify the Filippov method to calculate the saltation matrix. The derived formula is general and versatile such that it can be applied to any PV‐fed DC‐DC converter irrespective of the topology or control algorithm employed. Two case studies are investigated: current mode control and maximum power point tracking. Each case study serves to demonstrate different considerations that must be taken into account when conducting stability analysis of hybrid‐DAEs.

New advances in small-scale generation and consumption technologies have shifted conventional buildings’ functionality towards energy-efficient active buildings (ABs). Such developments drew the attention of researchers all around the world, resulting in a variety of publications, including several review papers. This study conducts a systematic literature review so as to analyse the concepts/factors enabling active participation of buildings in the energy networks. To do so, a relatively large number of publications devoted to the subject are identified, introducing the taxonomy of control and optimisation methods for the ABs. Then, a study selection methodology is proposed to nominate potential literature that has investigated the role of ABs in the energy networks. The modelling approaches in enabling flexible ABs are identified, while the potential challenges have been highlighted. Furthermore, the citation network of included papers is illustrated by Gephi software and analysed using “ForceAtlas2” and “Yifan Hu Proportional” algorithms so as to analyse the insights and possibilities for future developments. The survey results provide a clear answer to the research question around the potential flexibility that can be offered by ABs to the energy grids, and highlights possible prospective research plans, serving as a guide to research and industry.

As part of the inteGRIDy project, funded by the European Commission, an investigation is carried out on a real distribution network, where high penetrations of distributed generations (DGs) exist, in the UK. In this study, a model of this network is built. In this model, additional energy storage systems are located in the network close to DGs to represent future smartgrid architecture. This architecture is proposed to reduce the power import and export between this network and the grid. Four test cases are designed to explore the impacts of DG and the benefits of ESSs.

This paper proposes a probabilistic power pinch analysis (PoPA) approach based on Monte–Carlo simulation (MCS) for energy management of hybrid energy systems uncertainty. The systems power grand composite curve is formulated with the chance constraint method to consider load stochasticity. In a predictive control horizon, the power grand composite curve is shaped based on the pinch analysis approach. The robust energy management strategy effected in a control horizon is inferred from the likelihood of a bounded predicted power grand composite curve, violating the pinch. Furthermore, the response of the system using the energy management strategies (EMS) of the proposed method is evaluated against the day-ahead (DA) and adaptive power pinch strategy.

Virtual power plant (VPP) is a hybrid power system with a mesh of distributed resources, local loads and storage with both discrete and continuous variables involved. Control of such system requires advanced control schemes to make distributed assets act together to mimic an actual power plant. Previously, to study the behaviour of such systems, complex algorithms and modelling tools have been used but this paper proposes the use of Hybrid Automata (HA) as a convenient tool to model these systems. To demonstrate this, HA is used to model a VPP for a research farm owned by Newcastle University as a first step to design the control for the system.

Residential variable energy price schemes can be made more effective with the use of a demand response (DR) strategy along with smart appliances. Using DR, the electricity bill of participating customers/households can be minimised, while pursuing other aims such as demand-shifting and maximising consumption of locally generated renewable-electricity. In this article, a two-stage optimization method is used to implement a price-based implicit DR scheme. The model considers a range of novel smart devices/technologies/schemes, connected to smart-meters and a local DR-Controller. A case study with various decarbonisation scenarios is used to analyse the effects of deploying the proposed DR-scheme in households located in the west area of the Isle of Wight (Southern United Kingdom). There are approximately 15,000 households, of which 3000 are not connected to the gas-network. Using a distribution network model along with a load flow software-tool, the secondary voltages and apparent-power through transformers at the relevant substations are computed. The results show that in summer, participating households could export up to 6.4 MW of power, which is 10% of installed large-scale photovoltaics (PV) capacity on the island. Average carbon dioxide equivalent (CO2e) reductions of 7.1 ktons/annum and a reduction in combined energy/transport fuel-bills of 60%/annum could be achieved by participating households.