Damian Giaouris

The digitalisation of power systems introduces substantial vulnerabilities, particularly concerning system stability and security, due to the growing risk of malicious cyber attacks. These threats, often enabled by the proliferation of Internet of Things assets, can target critical infrastructure by manipulating system load or altering renewable power generation, both of which directly affect the system security margin. This paper proposes a novel cyber-resilient optimisation strategy (CROS) framework to enhance cyber-resilience in power systems, particularly under conditions of high renewable power penetration. The framework explicitly accounts for the interplay between cyber and physical components and addresses how adversarial interventions can impact load and renewable generation inputs. Central to this approach is a cyber-conscious strategy (CCS) which intends to increase the cyber-resilience radius in the face of unknown cyber attacks without any information about the intention and characteristics of attacks. By embedding the cyber-flexible security service (CFSS), the framework supports real-time corrective actions, allowing operators to adapt flexibly to evolving cyber-threat scenarios. The CFSS defines a dynamic security margin, reflecting the system's ability to maintain system security under unknown cyber threats. The proposed multi-stage, bi-level co-optimisation model integrates a cost-risk analysis to guide techno-economic decision-making, empowering operators with a proactive and adaptive operational strategy to mitigate the impact of cyber incidents on modern, renewables-rich power systems.

The expansion of data centers (DCs), accelerated by artificial intelligence (AI) and cloud computing, is redefining their role in power systems. DCs are shifting from being passive loads to active prosumers, with the potential to provide flexibility from microgrids to supergrids. This article explores modeling approaches and integration strategies, focusing on static and dynamic representations of IT, uninterruptible power supplies (UPSs), and auxiliary systems. Hybrid physics-based and data-driven models are identified as essential to capture stochastic workload dynamics. The role of DC energy management systems (EMSs) is analyzed across system levels: optimization within microgrids, various functions within distribution networks, stability support in transmission systems, and balancing in supergrids. Potential contributions to ancillary services, including frequency response, voltage regulation, and black start, are assessed, with attention to novel configurations, such as small modular reactor (SMR) colocation. The study concludes that DCs can evolve into controllable, flexible assets that enhance the resilience, security, and reliability of future electricity networks.

Microgrids (MGs) have evolved as critical components of modern energy distribution networks, providing increased dependability, efficiency, and sustainability. Effective control strategies are essential for optimising 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 (GRU), basic recurrent neural network (RNN) 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 optimises MG operation, especially under demanding conditions such as variable renewable energy supply and unanticipated load fluctuations. This study investigates special RNN architectures and hyper-parameter 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.

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.

Switched model predictive control (S-MPC) and recurrent neural network with long short-term memory (RNN-LSTM) are powerful control methods that have been extensively studied for energy management in microgrids (MGs). These methods are complementary in terms of constraint satisfaction, computational demand, adaptability, and comprehensibility, but typically one method is chosen over the other. The S-MPC method selects optimal models and control strategies dynamically based on the system’s operating mode and performance objectives. On the other hand, integration of auto-regressive (AR) with these powerful control methods improves prediction accuracy and system conditions’ adaptability. This paper compares the two approaches to control 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. 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%. Also, this study evaluated our predictive model’s accuracy: (i) R-squared error is 0.951, indicating 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 actual values.

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.

Research and development are critical for driving economic growth. To realise the UK government’s Industrial Strategy, we develop an energy research and innovation infrastructure roadmap and landscape for the energy sector looking to the long term (2030). This study is based on a picture of existing UK infrastructure on energy. It shows the links between the energy sector and other sectors, the distribution of energy research and innovation infrastructures, the age of these infrastructures, where most of the energy research and innovation infrastructures are hosted, and the distribution of energy research and innovation infrastructures according to their legal structure. Next, this study identifies the roadmap of energy research and innovation infrastructures by 2030, based on a categorisation of the energy sector into seven subsectors. Challenges and future requirements are explored for each of the sub-sectors, encompassing fossil fuels and nuclear energy to renewable energy sources and hydrogen, and from pure science to applied engineering. The study discusses the potential facilities to address these challenges within each sub-sector. It explores the e-infrastructure and data needs for the energy sector and provides a discussion on other sectors of the economy that energy research and innovation infrastructures contribute to. Some of the key messages identified in this study are the need for further large-scale initiative and large demonstrators of multi-vector energy systems, the need for multi-disciplinary research and innovation, and the need for greater data sharing and cyber-physical demonstrators. Finally, this work will serve as an important study to provide guidance for future investment strategy for the energy sector.

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.

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.