Microgrid clusters (MGCs) have the ability to enhance energy efficiency, resilience, and reliability of individual microgrids (MGs). By integrating different power generation, consumption, and storage technologies, MGCs can combine direct current (DC) and alternating current (AC) technologies, thus offering flexibility to MGs. However, suitable control systems for MGCs are required to manage their operation, ensuring robustness and efficiency of the power dispatch. This work contributes to this effort by presenting and implementing a novel control approach for an MGC. The MGC consists of DC and AC MGs connected to a local electricity grid. The DC MG integrates a wind turbine, fuel cell, an electrolyzer, an ultracapacitor, and DC loads. In contrast, the AC MG integrates an electric battery bank, a photovoltaic generator and AC loads. The control system uses local controllers for each device in the cluster and a dynamic centralized energy management system to coordinate optimally energy dispatch and distribution among all energy storage systems. To assess the control approach, fluctuating incident solar radiation and winds speed, and dynamic loads conditions are introduced in the system. The control system demonstrates robust behavior across the different simulation scenarios. Key words. Energy management system, microgrid cluster, sequence quadratic programming algorithm.

The increasing deployment of inverter based renewable energy sources in the Sultanate of Oman’s power system is reducing overall system inertia and leading to higher rate of change of frequency (ROCOF). To support higher renewable penetration in line with national energy targets, effective virtual inertia solutions are required. This study evaluates the impact of increased share of renewable energy on the actual inertia of the Oman Main Interconnected System (MIS) and proposes a novel interval Type-2 Fuzzy-PID-based virtual inertia control strategy. Unlike conventional approaches, the proposed method is assessed under multiple operating scenarios, including high renewable penetration, uncertainty, and severe disturbances. The system inertia is evaluated using DIgSILENT Power Factory, while the controller is designed and validated in MATLAB/Simulink. The proposed controller is successfully tested under renewable energy variability and uncertainty conditions at a penetration level of 20% of total system demand. Simulation results show a significant improvement in frequency stability, including reduced ROCOF, enhanced damping, and improved frequency response compared to conventional methods. These findings demonstrate the effectiveness and practical applicability of the proposed controller for real-world low-inertia power systems.

Due to the impacts of climate change, the world is experiencing rapidly rising temperatures and shifting weather patterns. In Northern hemisphere countries like the United Kingdom (UK), dual-seasonal thermal energy demand is faced, with intense winter heating needs and elevated summer cooling requirements due to the increasing frequency and intensity of heatwaves. Thus, understanding the changing demand for both heating and cooling is becoming increasingly important towards meeting legally-binding net-zero targets based on how the required additional demand on electricity networks is met. Despite this, most attention has been given to heat demand, with space cooling not receiving significant consideration so far and the intrinsic interdependence between heating and cooling requirements not fully accounted for. In addition, domestic buildings have not been sufficiently investigated, with a substantial focus placed instead on commercial and industrial sites. This paper presents a database of yearly thermal energy demand for typical UK dwellings. The work captures the seasonal variation in thermal demand between warmer and colder months. To this end, typical meteorological weather files, alongside six selected geographical locations, multiple dwelling types, different construction data and four building orientations were considered to create year-round datasets. The database includes approximately 220,000 high-resolution datasets in total, which are provided alongside the paper. Each dataset comprises the hourly thermal energy demand required to maintain a set-point indoor temperature of 21 degrees C either through heating or cooling provision. Internal load profiles considering lighting, appliances and occupancy are made available, which can be further modified and incorporated to the thermal loads. A methodology to adjust internal set-point temperatures to encapsulate a wide variety of thermal comfort levels is also presented. Representative examples demonstrate how the database can be used to support broader analyses of seasonal heating demand while also exploring emerging cooling demand in a warming world.

Multi-energy prosumers have emerged as a promising avenue for curtailing energy consumption by integrating diverse energy vectors in a synchronised operation. Prevailing investigations have predominantly focused on static analyses directed at optimising cost functions for power dispatch problems, often overlooking the dynamic facets of the system. This paper introduces a dynamic real-time control scheme for a multi-energy prosumer encompassing electricity and heat as energy vectors. A novel state-based energy management system (EMS) is designed, with the goal of ensuring energy balance (electrical and thermal), while prioritising the utilization of renewable energy and diminishing reliance on local electrical distribution networks. To this end, three different operating modes are defined regarding the real-time renewable capacity. The EMS and multi-energy prosumer are subjected to evaluation across several weather conditions in a 4-h variable load profile. A sensibility analysis considering 250 simulations of 1-h duration, with a wide range of irradiance, water demand, underfloor heating load, and state-of-charge conditions were used to validate the control response, demonstrating the lack of use of the local grid. Moreover, a real-time experiment employing hardware-in-the-loop testing with an OPAL-RT4512 unit and a dSPACE MicroLabBox control prototype confirms the adequate response in a practical scenario. Comparative validation against a fuzzy-logic benchmark throughout a 24-h dynamic horizon revealed that the proposed state-based EMS reduced grid dependency by 36.61% and auxiliary gas boiler by 2.23%. Furthermore, the architecture achieved a 30-fold reduction in computational time while maintaining negligible control errors, establishing its superior suitability for real-time implementation in prosumers environments.

The design of energy management systems (EMS) and dynamic control systems for multi-energy microgrids (MEMGs) combining diverse energy vectors (electricity, heating/cooling, and hydrogen) has not been extensively explored. Instead, prior research on MEMGs has predominantly focused on daily or weekly time horizons, adopting a static perspective primarily aimed at cost optimization or emission reduction. To address this gap, this article introduces a novel intelligent EMS based on fuzzy logic and model predictive control designed to minimize energy consumption within a MEMG while avoiding reliance on the main electrical grid. The MEMG comprises a photovoltaic power plant, a battery, electrical residential demand, a fuel cell, an electrolyzer, a hydrogen tank, a gas boiler, an electric boiler, an absorption chiller and thermal residential demand, with a connection to the main grid. By efficiently adjusting the operating points of thermal components based on renewable production and the available energy in the battery and hydrogen system, the MEMG achieves a synergistic integration of various energy vectors. The efficient energy dispatch leads to a 9.56 % reduction in operational costs, and a 2.82 % decrease in the CO2 emissions. Moreover, the findings demonstrate a notable reduction in the usage of both the gas boiler (by 2.82 %) and electric boiler (by 18.75 %), as well as the absorption chiller (8.91 %). Additionally, there is an increase in the state of charge of the electrical battery (by 21.43 %), hydrogen level (by 4.8 %), and state of energy (by 18.85 %). The results of this work thus highlight the adaptability and resilience of the presented EMS in establishing an effective intelligent energy dispatch among multiple energy vectors.

While energy management and control techniques have been extensively studied in electrical microgrids, optimizing the operation of electrical networks alongside hydrogen, heating and cooling systems, remains a significant challenge. Effective real-time control management within multi-energy microgrids (MEMGs) is particularly challenging due to the intermittent and unpredictable nature of renewable energy sources and varying multi-energy demand. Existing research on MEMGs often lacks a holistic, real-time approach that simultaneously incorporates multiple intelligent techniques. Furthermore, the integration of co-generation systems, particularly those involving hydrogen and gas technologies, presents additional challenges in optimizing MEMG operations. This paper proposes a novel dynamic control strategy that directly addresses these challenges by integrating fuzzy logic, model predictive control, and nonlinear optimization in real time. The strategy is designed to enhance MEMG performance by seamlessly coordinating the operation of multiple energy systems, with a particular focus on the effective management of hydrogen storage and electrical batteries within a hybrid energy storage system (HESS). The objective is to minimize operational costs, gas consumption, and grid dependence, while maximizing system flexibility. The strategy is applied to an 8-unit residential building in Cardiff, UK, equipped with a photovoltaic plant, fuel cell, electrolyzer, hydrogen storage, electrical battery, gas and electric boilers, chiller, and a combined heat and power unit. When compared to two alternative strategies-one that does not consider optimal cost allocation and another using a state-based EMS-the proposed framework yields a substantial reduction in costs by 33.86% and 18.38%. Gas consumption is reduced by 7.41% and 3.15%, respectively, while the HESS state-of-energy increases significantly by 100.06% and 20.02%, respectively. Furthermore, real-time experimental verification corroborates the practicality and efficacy of the proposed framework.

Ground-source heat pump (GSHP) systems are a promising technology for space and water heating due to their high efficiency and ability to leverage the stable thermal conditions of the ground. As a low-carbon alternative to conventional technologies, GSHPs have potential for reducing carbon emissions in buildings. However, widespread adoption of GSHPs has been hindered by challenges related to long-term ground thermal imbalance, which can degrade performance and increase operational costs. To address these limitations, this paper investigates the performance of an integrated solar photovoltaic-thermal and GSHP system designed for a residential community in Wales, United Kingdom. A numerical model was developed to integrate steady-state thermodynamic calculations with quasi-dynamic simulations to evaluate the thermal and electrical performance of the system. Seasonal analyses were conducted to assess thermal recharge, ground temperature response and system efficiency. Results show that solar-assisted ground recharge can mitigate thermal imbalance, improve heat pump performance and contribute to the development of flexible net-zero energy buildings.

Global warming has led to higher ambient temperatures in traditionally cold regions in Europe such as the UK. While implementing strategies in residential dwellings to meet the rising demand for cooling during hot summers is thus of interest, an accurate estimation of such demand is however a prerequisite for developing or implementing upgrades to cooling infrastructure. To contribute to this effort, this paper presents an estimation tool to quantify the cooling demand of a housing community. The tool was developed with the open-source software OpenModelica and was used to model diverse heat transfer phenomena in house envelope components, individual houses, and groups of houses. It uses multiple levels of design hierarchies and enables exploring different heat mitigation strategies. The tool was employed to estimate the potential future cooling demand of a UK housing community. The results highlight that houses of the same design may exhibit substantial variations in demand based on their location and orientation within the community. For instance, the annual demand of the houses ranges from 4505.8 kWh to 5873.4 kWh for the years under study if a cooling setpoint temperature of 21 degrees C is adopted. By increasing this setpoint by 1.5 degrees C, the community's annual demand could be reduced by similar to 20 MWh. Furthermore, incorporation of mitigation strategies reduced both the overall and peak demands for the individual houses and the community as a whole while also decreasing the disparity in demand across households. By having access to the estimation tool, shared alongside the paper, interested users may be boosted to conduct ad-hoc assessments to understand cooling demand variations within any housing community of interest.

According to the 2020 UN emissions report an increase by 3 degrees C of the average global temperature compared to pre-industrial levels is to be expected if no corrective measures are implemented. Alongside this, the UK Meteorological Office predicts that the UK will see a surge in both the recurrence and severity of heatwaves during summers-leading to an increased demand for space cooling. Although commercial infrastructures are likely to incorporate cooling provisions, residential properties are generally at a nascent stage to facilitate indoor cooling. Upgrading the cooling capabilities of residential dwellings would require a clear understanding of cooling demand. To this end, this paper presents a methodology to quantify cooling demand for typical UK dwellings. Following an in-depth literature review of the current UK housing stock to retrieve physical building data, a physics-based model was created using commercial building envelope modelling software. This considered building construction methods, ages, and layouts. To provide confidence in the approach, the model was verified with real data taken from a semi-detached dwelling in Loughborough, UK, and subsequently, thermal models for the most common type of dwellings were developed. Results highlight how cooling demand varies for differing dwelling types, orientations, locations, and constructions. For instance, for a typical design year, corner flats on the top floor of a 3-storey building in Cardiff, UK, with an orientation of 0 degrees (north-facing) have the highest monthly cooling demand of 27.21 kWh and the bottom floor mid-flats have the lowest demand of 17.36 kWh. The presented methodology provides an initial framework to generate residential cooling demand data, which could be used to inform building developers, utilities, and local authorities on cooling demand peaks, overheating risks, and energy efficiency of typical UK dwellings in a warming world.

Microgrid clusters (MGCs) provide an opportunity for system operators to enhance efficiency, resilience, and reliability of energy systems. MGCs can combine direct current (DC) and alternating current (AC) technologies by integrating different power generation, consumption and storage technologies, thus offering flexibility and resilience to individual microgrids (MGs). However, verification of practical control systems for MGCs is required to ensure robustness and efficiency of the power dispatch. This work contributes to this effort by presenting, implementing and verifying a control system for an MGC. This MGC comprises separate DC and AC MGs interconnected to a local grid: the DC MG incorporates DC loads, a wind turbine, a fuel cell, an electrolyzer, and an ultracapacitor; and the AC MG comprises AC loads, an electrical battery bank, and a photovoltaic power plant. The control system uses a dynamic centralized energy management system (EMS) that coordinates power dispatch and energy distribution between all energy storage systems and local device controllers. The control system, specifically the EMS, is evaluated across different operating scenarios in an experimental validation environment composed of an OPAL-RT unit and a SIMATIC ET 200SP Open Controller PLC. The results show that the implemented EMS exhibits robust real-time behavior across different operating scenarios.

Thermal energy storage systems (TESSs) enhance multi-energy microgrids (MEMGs) operation by optimizing energy management. While previous research primarily focused on optimizing the MEMG operation using static MEMG models, this paper analyzes the dynamic impact of TESS on a grid-connected residential MEMG. This includes a photovoltaic plant, an electrical battery, and a hydrogen system with an electrolyzer, a fuel cell, and hydrogen tank. The thermal subsystem includes a gas boiler, a micro-combined heat and power (CHP) unit, an electric boiler, and a TESS tank. A novel intelligent control architecture based on fuzzy logic, model predictive control, and nonlinear optimization is presented to control the MEMG. Simulation results with TESS reveal a balanced heat production and demand, and improved temperature control. The integral time squared error (ITSE) is reduced by 91 % for the hot water circuit control and 81 % for the overall thermal balance of the MEMG. The improved control scheme also reduces the gas consumption, with a reduction of 12.44 % for the gas boiler, 1.81 % for the CHP, and 8.66 % in total, leading in turn to reduced operational costs (by 6 %) and CO2 emissions (by 8.37 %) compared to the MEMG operation without a TESS under the same control scheme.

The building sector significantly impacts greenhouse gas emissions, making decarbonisation of heating and cooling essential for achieving carbon neutrality. Replacing conventional fossil fuel technologies with low-carbon alternatives like reversible heat pumps (HPs), alongside integrating thermal energy storage systems, can provide flexibility by reducing thermal demand during peak hours-which could also be reflected in economic savings. In view of this, the detailed dynamic model of an energy system based on a reversible HP integrated with thermal stores is presented in this paper. The adopted configuration has been designed to meet not only heat demand during cold months, but also cooling demand over summer, which is expected to increase in future years according to climate projections. A multi-zone modelling approach was employed to simulate a residential building. Internal heat gains due to appliances, lighting, and occupancy schedules were incorporated in the model to accurately represent the zonal temperature level control in the thermal envelope. The performance of the energy system utilising the reversible HP was compared to when a gas boiler is used, demonstrating the capabilities of low-carbon technologies to meet thermal demand during different seasons of the year. Moreover, the performance of HP-based system under extreme weather conditions was evaluated. The HP configuration consumed 1.15-2.34 times less monthly energy to meet the thermal needs compared to the boiler-based system. The inclusion of internal heat gains showed a considerable effect, with a monthly energy consumption increment of up to 63.5 % observed for the HP-based energy system.

Efficient thermal management plays a critical role in maintaining the safety and reliability of battery systems, especially as battery technology advances and is integrated into applications such as aerospace, electric vehicles, and portable devices. To ensure the performance and longevity of such systems, it is important to effectively manage the temperature of the battery packs. Active cooling methods, which rely on external mechanisms to disperse heat from the battery, are fundamental to maintaining the battery temperature within a secure operating range and increasing its service life. This chapter presents an overview of different active cooling techniques for battery thermal management systems, including liquid and nanofluid, forced air, refrigeration, thermoelectric, and hybrid cooling-based methods. Each method inherits some advantages and disadvantages, and selecting the most appropriate approach is based on factors like size of the battery, operating conditions, and chemistry. In general, active cooling methods are indispensable for ensuring the optimal safety and performance of battery systems, and their improvement and optimization will continue to be the subject of research and innovation in diverse industries.

While multi-energy microgrids (MEMGs) offer a promising approach to reduce energy consumption through coordinated integration of various energy vectors, research has primarily focused on static studies. These studies aim to optimize a particular cost function but neglect the dynamic aspects of the system operation. This paper presents a dynamic model of an MEMG comprising of electricity and thermal vectors. A novel dynamic fuzzy logic-based energy management system (EMS) is investigated, aiming to ensure energy balance (electric and thermal), optimize renewable energy utilization, and reduce the reliance on the local electricity grid and gas. Both the EMS and MEMG have been evaluated under different weather conditions and a 4-hour variable load profile. Furthermore, the EMS effectiveness has been verified through a real-time experiment using an OPAL-RT4512 unit and a dSPACE MicroLabBox prototype. The results show that the proposed fuzzy logic-based EMS outperforms a conventional EMS based on machine states (states-based EMS), achieving a notable reduction in electricity grid consumption of 80%, as well as a consumption reduction of 7.4% in the gas boiler and 5.4% in the electric boiler. Furthermore, the control performance results in a remarkable reduction in ITAE (42.57%), ITSE (89.10%), IAE (54.36%) and ISE (57.55%) for the hot water temperature control, and in ITAE (17.06%), ITSE (52.50%), IAE (31.19%) and ISE (29.99%) for the heating control.

Thermal networks require thermal energy storage (TES) provisions for balancing thermal energy sources with variable consumer demand. Harvesting ice is an economical option for latent heat TES systems in cooling networks given the wide availability of the storage medium. This paper presents an artificial intelligence (AI) based model to monitor the state-of-charge (SoC) and the outlet temperature of the heat transfer fluid (To) of an ice tank under fluctuating operating conditions. The AI model is a non-linear autoregressive network with exogenous inputs (NARX) that was trained and tested with datasets obtained from experimental measurements of a practical ice tank and a physics-based model of the tank. The NARX model was sensitised with physics-informed attributes to recognise different heating and cooling zones. The model exhibits a high accuracy in predicting the operating conditions of the ice tank when benchmarked against both experimental measurements of a practical tank and outputs from the physics-based model. For instance, it achieves R2 values of 0.9943 and 0.9842 for SoC and To, with root mean square errors of 1.73% for SoC and 0.3161°C for To. The NARX model is 86% faster than its physics-based counterpart and its implementation requires limited computational resources—making it suitable as a standalone estimator for the TES operation and the accelerated simulation of energy systems containing latent heat TES units. Furthermore, given the limited availability of NARX models in open-source libraries, the presented NARX model and relevant datasets have been made available alongside this paper to contribute to open-science in energy research and the broader AI community.

High-voltage direct-current (HVDC) rated at 270 Vdc is one of the main power supply technologies expected for future more electric aircraft (MEA). However, dc protection is still one major challenge preventing the wide deployment of HVDC. To overcome this, Z-source solid-state circuit breakers (Z-SSCBs) could be employed due to their simple structure and fast speed of response. However, Z-SSCBs alone cannot effectively isolate a short-circuit fault when a large fault resistance and a small fault current ramp rate are present, which would greatly damage MEA. In this paper, an auxiliary protection strategy based on Z-SSCBs is presented to address this problem. The strategy combines inverse-time overcurrent and voltage protection to force the opening of the Z-SSCB when its automatic triggering fails. The principle of operation of a Z-SSCB is discussed, and the design process of the protection strategy is presented in detail. Software simulations using Saber and experimental tests have been carried out to validate the protection strategy. Both sets of results match well, offering a good performance and meeting IEEE protection (Std C37.112-2018) and aircraft electrical standards (MIL-StD-704F). It is shown that with the auxiliary protection strategy, the Z-SSCB successfully isolates faults against overcurrent, overvoltage and undervoltage operating conditions.

Renewable energy-based ground source heat pump (GSHP) systems have gained traction as cost-effective and environmentally sustainable alternatives for heating and cooling applications in residential, commercial, and civic buildings. However, their prolonged operation may lead to a decline in the geothermal potential of the soil and its thermal imbalance. The integration of thermal energy storage (TES) systems with GSHPs can mitigate these issues by balancing energy supply and demand, providing flexibility to meet heating and cooling demand during peak hours, preserving energy during off-peak hours, and optimising overall system efficiency. In recent years, there has been a significant increase in experimental, numerical, and theoretical studies investigating various TES-assisted GSHP configurations under different operational conditions and climate scenarios. These integrated systems may consider different sensible heat, latent heat, and sensible-latent heat-based TES methods. In this context, this paper presents a comprehensive overview of recent progress in TES-assisted GSHP systems. The main objectives of this work are to bridge the knowledge gap on these integrated systems, provide clarity on the adopted terminology, and highlight advantages and disadvantages of the different configurations presented in the literature. This review is expected to offer valuable insight for researchers and partitioners in the field of TES-assisted GSHPs and guide future research and development efforts in the area—ultimately supporting the path towards decarbonisation of heat (including space cooling) and meeting net-zero targets.

The urgent need to achieve net-zero carbon emissions by 2050 has led to a growing focus on innovative approaches to producing, storing, and consuming energy. Integrated energy systems (IES) have emerged as a promising solution, capitalising on synergies between energy networks and enhancing efficiency. Such a holistic approach enables the integration of renewable energy sources and flexibility provision from one energy network to another, reducing emissions while facilitating strategies for operational optimisation of energy systems. However, emphasis has been mostly made on steady-state methodologies, with a dynamic verification of the optimal solutions not given sufficient attention. To contribute towards bridging this research gap, a methodology to verify the outcomes of an optimisation algorithm is presented in this paper. The methodology has been applied to assess the operation of a civic building in the UK dedicated to health services. This has been done making use of real energy demand data. Optimisation is aimed at improving power dispatch of the energy system by minimising operational costs and carbon emissions. To quantify potential discrepancies in power flows and operational costs obtained from the optimisation, a dynamic model of the IES that better captures real-world system operation is employed. By incorporating slow transients of thermal systems, control loops, and non-linearity of components in the dynamic model, often overlooked in traditional optimisation modules, the methodology provides a more accurate assessment of energy consumption and operational costs. The effectiveness of the methodology is assessed through model-in-the-loop co-simulations between MATLAB/Simulink and Apros alongside a series of scenarios. Results indicate significant discrepancies in power flows and operational costs between the optimisation and the dynamic model. These findings illustrate potential limitations of conventional operational optimisation modules in addressing real-world complexities, emphasising the significance of dynamic verification methods for informed energy management and decision-planning.

The cascaded three-level neutral-point-clamped (C3L-NPC) converter has been adopted in practical projects for power transmission in medium-voltage (MV) distribution networks. However, this type of converter comprises multiple submodules (SMs), in which thermal imbalance may occur due to a mismatch between component parameters of the SMs. This may lead to decreased system reliability. To address this shortcoming, an active thermal sharing control strategy for C3L-NPC converters is presented in this article. A thermal control loop is incorporated into the inner current controller within each SM. Active and reactive power regulation is conducted based on the individual junction temperature of each SM. A high-level controller is used to regulate the total power and to calculate the temperature reference. The control strategy enables decoupled thermal and power regulation, and each control loop can be independently designed. The effectiveness of the approach has been experimentally validated using a testbed down-scaled from the ANGLE-DC project-the first operational MV direct-current (MVdc) link in Europe. It is shown that the junction temperature of the SMs is effectively balanced without affecting the output power under cooling system failures.