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Electric heat pumps (HPs), hybrid heating technology and district heating networks (DHN) are the main low-carbon heating technologies that can deliver the ambitious carbon reduction target in the UK. The optimal design of the heating system on a national scale to maximize the economic benefits regarding both investment cost and operation cost while satisfying the carbon target remains an open question. This paper aims to compare the economic advantages as well as the associated impacts on the electricity system under the full deployments of ASHP (air source HP), hybrid HP-Bs (ASHP and gas boilers) and DHNs. To do so, a novel whole-system approach is applied with the explicit consideration of the coordinated operation between heat and electricity system. The optimized strategy for heat sector decarbonisation is also demonstrated, providing an outline of the optimal deployment for different heating technologies in terms of their penetrations and deployed areas. The UK case study suggests the significant economic advantage of the hybrid HP-B over the other two heating technologies, while DHN may play an important role in urban areas under the optimized heat decarbonisation strategy. The results also clearly demonstrate the changes on the electricity side driven by the different decarbonisation strategies in the heating system. [ABSTRACT FROM AUTHOR]

• Social acceptance research should be critical, systematic, and holistic. • Domestic hydrogen acceptance is shaped by the interplay of five distinct dimensions. • Hydrogen acceptance hinges on cognitive, sociopolitical, and sociocultural legitimacy. • Hydrogen acceptance is composed of a rich matrix of potential consumer responses. • Overcoming attitudinal and behavioral barriers is critical to the hydrogen transition. The 'deep' decarbonization of the residential sector is a priority for meeting national climate change targets, especially in countries such as the UK where natural gas has been the dominant fuel source for over half a century. Hydrogen blending and repurposing the national grid to supply low-carbon hydrogen gas may offer respective short- and long-term solutions to achieving emissions reduction across parts of the housing sector. Despite this imperative, the social acceptance of domestic hydrogen energy technologies remains underexplored by sustainability scholars, with limited insights regarding consumer perceptions and expectations of the transition. A knowledge deficit of this magnitude is likely to hinder effective policymaking and may result in sub-optimal rollout strategies that derail the trajectory of the net zero agenda. Addressing this knowledge gap, this study develops a conceptual framework for examining the consumer-facing side of the hydrogen transition. The paper affirms that the spatiotemporal patterns of renewable energy adoption are shaped by a range of interacting scales, dimensions, and factors. The UK's emerging hydrogen landscape and its actor-network is characterized as a heterogenous system, composed of dynamic relationships and interdependencies. Future studies should engage with domestic hydrogen acceptance as a co-evolving, multi-scalar phenomenon rooted in the interplay of five distinct dimensions: attitudinal, sociopolitical, community, market, and behavioral acceptance. If arrived to, behavioral acceptance helps realize the domestication of hydrogen heating and cooking, established on grounds on cognitive, sociopolitical, and sociocultural legitimacy. The research community should internalize the complexity and richness of consumer attitudes and responses, through a more critical and reflexive approach to the study of social acceptance. [ABSTRACT FROM AUTHOR]

The evolution towards Smart Grids (SGs) represents an important opportunity for modernization of the energy industry. It is characterized by a bidirectional flow of information and energy between consumers and suppliers. However, the rapid increase of energy demands in residential areas is becoming a challenging problem. In order to address this issue, Demand-Side Management (DSM) has proven to be an effective solution. In this paper, we propose LOSISH, a price-based Demand Response (DR) system for load scheduling in residential Smart Homes (SHs) that achieves a trade-off between electricity payments and consumer's discomfort. Our proposed system considers Renewable Energy Sources (RESs), Battery Energy Storage System (BESS) and Plug-in Electric Vehicle (PEV). We formulate our scheduling as a constrained optimization problem and we propose a new hybrid algorithm to solve it. The latter combines two well known heuristic algorithms: Particle Swarm Optimization (PSO) and Binary Particle Swarm Optimization (BPSO). Moreover, we propose a new clustering algorithm based on Machine Learning (ML) to extract consumer's preferences from a real dataset that contains the historical consumption patterns of his smart appliances. We test our approach on real data traces obtained from a SH and we set up an experiment to evaluate our algorithm on a Raspberry Pi and measure its energy consumption. To prove the effectiveness of our approach, we compare our results with another approach from the literature in terms of electricity bill, Peak-to-Average Ratio (PAR), energy consumption, and execution time. Numerical results show that LOSISH outperforms the other approach in terms of electricity bill (up to 52.92% cheaper), PAR (up to 44% decrease in peak demands), energy consumption (up to 69.44% less consumption), and execution time (up to 63.15% faster). • The load scheduling problem in residential smart homes is studied. • A constrained problem is formulated to make a trade-off between costs and comfort. • A hybrid heuristic algorithm and a Machine Learning clustering model are proposed. • Real data collected from a smart home in the UK was used to validate our approach. • Simulation and experimental results demonstrate the effectiveness of our approach. [ABSTRACT FROM AUTHOR]

In order to pursue the low-carbon development around the world, a large scale of renewable generation will be connected to the power systems. Take the Great Britain (GB) as an example, the GB power system has a large wind energy integration potential. The intermittency of wind generation will have great impact on the system frequency stability. Electric vehicles (EVs) have a crucial role in decarbonizing the transport sector. To increase the utilization of wind energy, EVs are suggested to provide frequency response service to the power system due to their quick power reaction characteristic. This paper proposes a general dynamic EV frequency control strategy considering the travelling behavior of the EV users. A droop control method is used to regulate the EV charging/discharging power according to the frequency signal. A Forced-Charge Boundary (FCB) and a Forced-Charge Area (FCA) are proposed to guarantee sufficient energy in the EV battery for user’s travel at the plug-out time. A dynamic Virtual Energy Storage System (VESS) is developed to evaluate the frequency response capacity of the EV clusters. In the case study, the model of the GB power system is used to investigate the frequency control effect of the control strategy. The simulation results show that the proposed strategy provides effective EV frequency response to the power system and thus is able to facilitate the integration of wind energy. [ABSTRACT FROM AUTHOR]

Energy policy is too often not designed for energy consumers in a low-cost and consumer-friendly manner. This paper proposes a novel Stackelberg game and Blockchain-based framework that enables consumer-centric decarbonization by automating iterative negotiations between policy makers and consumers or generators to reduce carbon emissions. This iterative negotiation is modeled as a Stackelberg game-theoretic problem, and securely facilitated by Blockchain technologies. The policy maker formulates carbon prices and monetary compensation rates to dynamically incentivize the carbon reduction, whereas consumers and generators schedule their power profiles to minimize bills and maximize profits of generation, respectively. The negotiating agreement is yielded by reaching a Stackelberg equilibrium. The exchanged information and controlling functions are realized by using smart contracts of Blockchain technologies. Case studies of GB power systems show that the proposed framework can incentivize 9% more bill savings for consumers and 45.13% more energy generation from renewable energy sources. As a consumer-centric decarbonization framework, it can at least reduce carbon emissions by 40%. [Display omitted] • A novel consumer-centric and negotiation-based framework to decarbonize power systems. • A Blockchain based platform to integrate the exchanged information and controlling functions of the power system scheduling into data structures, nodes, and smart contracts of Blockchain networks. • How Blockchain based platform can prevent cyber attacks is discussed. • Case studies demonstrate the effectiveness of the proposed framework in the context of Great Britain power systems. [ABSTRACT FROM AUTHOR]

• Severe PV ramping at high penetration could cause voltage instability in presence of Induction motor loads. • Replacement of Induction motor by Variable Speed Drive (VSD) loads enhances voltage stability of distribution network. • Enhanced stability with VSD is studied theoretically with Q-V analysis and with time simulations. • Minimum IM replacement by VSD is determined for realistic distribution network at various PV penetration levels. The Photovoltaic (PV) sources have become the most popular renewable energy resources as it is modular and are deployed quickly. However, the distributed PVs are highly volatile and can have high ramping characteristics where the ramping can go up to 90% of its capacity in about 20 s due to events such as the passing of fast-moving clouds. This kind of PV ramps with high magnitudes can lead to voltage instability in distribution networks that are usually dominated by Induction motor (IM) loads. This paper explains the mechanism of such an instability analytically using Q-V analysis with distribution feeder and IM load characteristics. Highlighting the gradual replacement of IM loads by Variable Speed Drive (VSD) loads due to energy efficiency policies, the paper demonstrates the enhanced voltage stability under VSD loads by both time simulation and analytical approach. Furthermore, the analysis is extended to a realistic distribution network namely, United Kingdom General Distribution System (UKGDS) and extensive case studies are conducted to analyze the voltage stability during PV ramping events at various load compositions of IMs and VSDs. Moreover, the minimum VSD penetration level required to avert the voltage instability at various PV penetration levels have also been determined. [ABSTRACT FROM AUTHOR]

By constructing digital twins (DT) of an integrated energy system (IES), one can benefit from DT's predictive capabilities to improve coordinations among various energy converters, hence enhancing energy efficiency, cost savings and carbon emission reduction. This paper is motivated by the fact that practical IESs suffer from multiple uncertainty sources, and complicated surrounding environment. To address this problem, a novel DT-based day-ahead scheduling method is proposed. The physical IES is modelled as a multi-vector energy system in its virtual space that interacts with the physical IES to manipulate its operations. A deep neural network is trained to make statistical cost-saving scheduling by learning from both historical forecasting errors and day-ahead forecasts. Case studies of IESs show that the proposed DT-based method is able to reduce the operating cost of IES by 63.5%, comparing to the existing forecast-based scheduling methods. It is also found that both electric vehicles and thermal energy storages play proactive roles in the proposed method, highlighting their importance in future energy system integration and decarbonisation. • A deep learning and digital twins (DT) based scheduling method is proposed. • Physical IES performance is addressed by its virtual replica's predictive capability. • A machine learning method is proposed to address multiple uncertainty challenges. • Historical U.K. datasets are used for performance evaluations. • Results demonstrate the Digital Twin model reduces the extra operating cost by 63.5%. [ABSTRACT FROM AUTHOR]

• Temporal characterization of marine energy resources, at varying time scales. • Characterized wave, tidal, and ocean currents, and compared to wind and solar. • Data based exposition of grid benefits for wave power at high renewable scenarios. • Data based exposition of higher capacity adequacy and stability for wave power. • Detailed discussion on market implications of grid integration of marine energy. In this paper, the applicability of marine renewable energy (MRE) for potential grid applications is presented. We show that many of the unique value streams from marine-based electricity generation resources stem from their inherent temporal characteristics, especially when compared to wind and solar. Specifically, in this work, we evaluate the timing value for three types of MRE resources: (a) tidal, (b) wave, and (c) ocean currents. First, through a suite of novel metrics, such as resource availability, persistence, and versatility, we evaluate the temporal value characteristics of these resources. Second, through a more grid-oriented numerical study, we comment on the potential ramifications of those temporal characteristics in context of energy balancing and effective load carrying capability for one marine-based resource i.e., wave. Finally, we further our understanding of the relative advantages that may be leveraged by operating wave-based generation in tandem with more established renewable resources, such as wind and solar. Our results indicate that compared to wind and solar, MRE resources are consistently more available and persistent on an hourly level throughout an entire year of operation. In addition, wave resources are also seen to reduce the balancing requirements within the power system. Our work focuses on sites specific to the United States (US) and a parallel study for a location in Great Britain (GB). Results are found to be consistent for sites in both the US and GB, implying that the grid benefits discussed in this work could apply to a number of locations globally. [ABSTRACT FROM AUTHOR]