• Energy Research

Abstract Most governments are applying financial instruments and other polices to encourage distributed renewable electricity generation (DREG). DREG is less predictable and more volatile than traditional forms of energy generation. Closure of larger fossil-fuelled power plants and rising share of DREG is reducing system inertia on energy networks such that new methods of demand response are required. Usually participation in non-dynamic frequency response is reactive, affecting the duty cycle of thermostatically controlled loads. However, this can adversely affect building thermal efficiency. The research presented takes a proactive approach to demand response employing heat transfer dynamics. Here, thermal dynamics exhibit a significantly larger inertia than electrical power consumption. Thus, short-term fluctuations in energy use should have less effect on temperature regulation and user comfort in buildings than existing balancing services. A prototype frequency sensor and control unit for proactive demand response in building stock is developed. The paper reports on hardware-in-the-loop simulations, testing real thermal loads within a simulated power network. The instrumented approach adopted enables accurate real-time electrical frequency measurement, while the control method offers effective demand response, which suggest the feasibility of using decentralised frequency control regulation as a novel approach to existing demand response mechanisms.

Research surrounding demand response (DR) is beginning to consider how blocks of buildings can operate collectively within energy networks. DR at the level of a block of buildings involves near real-time optimisation of energy demand, storage and supply (including self-production) using intelligent energy management systems with the objective of reducing the difference between peak-power demand and minimum night-time demand, thus reducing costs and greenhouse gas emissions. To enable this it will be necessary to integrate and augment the telemetry and control technologies embedded in current building management systems and identify potential revenue sources: both of which vary according to local and national contexts. This paper discusses how DR in blocks of buildings might be achieved. The ideas proposed are based on a current EU funded collaborative research project called “Demand Response in Blocks of Buildings” (DR-BOB), and are envisaged to act as a starting-point for future research and innovation.

Load scheduling will play an important role in achieving Demand Response (DR) at the consumption level of the emerging smart grid. This paper presents the evaluation of a heuristic approach for scheduling residential smart home appliances. The proposed heuristic schedules appliances one after the other in accordance with a greedy strategy without backtracking. This is such that the worst-case computation time is reduced compared to an exact search, at the expense of a potential loss of optimality in the obtained solution. In this paper the performance of the proposed heuristic is evaluated against an exact algorithm across the course of a full year using representative hourly prices of electricity. The results verify the suitability of the algorithm for the implementation of residential energy management decision support system.

In the context of increasing digitalization and the emergence of applications such as smart cities, embedded devices are becoming ever more pervasive, mobile, and ubiquitous. Due to increasing concerns around energy efficiency, gate density, and scalability in the semiconductor industry, there has been much interest recently in the fabrication of viable low-power energy-efficient devices. The Hetero-Dielectric Gate-All-Around (HD-GAA) MOSFET represents a cutting-edge transistor architecture designed for superior sustainability and energy efficiency, improving the overall efficiency of the system by reducing leakage and enhancing gate control; therefore, as part of the transition to a sustainable future, several semiconductor industries, including Intel, Samsung, Texas Instruments, and IBM, are using this technology. In this study, Hetero-Dielectric Single-Metal Gate-All-Around MOSFET (HD-SM-GAA MOSFET) devices and circuits were designed using Schottky source/drain contacts and tunable high-k dielectric HfxTi1−xO2 in the TCAD simulator using the following specifications: N-Channel HD-SM-GAA MOSFET (‘Device-I’) with a 5 nm radius and a 21 nm channel length alongside two P-Channel HD-SM-GAA MOSFETs (‘Device-II’ and ‘Device-III’) with radii of 5 nm and 8 nm, respectively, maintaining the same channel length. Thereafter, the inverters were implemented using these devices in the COGENDA TCAD simulator. The results demonstrated significant reductions in short-channel effects: subthreshold swing (SS) (‘Device-I’ = 61.5 mV/dec, ‘Device-II’ = 61.8 mV/dec) and drain-induced barrier lowering (DIBL) (‘Device-I’ = 8.2 mV/V, ‘Device-II’ = 8.0 mV/V) in comparison to the existing literature. Furthermore, the optimized inverters demonstrated significant improvements in noise margin values such as Noise Margin High (NMH) and Noise Margin Low (NML), with Inverter-1 showing 38% and 44% enhancements and Inverter-2 showing 40% and 37% enhancements, respectively, compared to the existing literature. The results achieved illustrate the potential of using this technology (e.g., for power inverters) in embedded power control applications where energy efficiency and scalability are important, such as sustainable smart cities.

Demand response (DR) involves economic incentives aimed at balancing energy demand during critical demand periods. In doing so DR offers the potential to assist with grid balancing, integrate renewable energy generation and improve energy network security. Buildings account for roughly 40% of global energy consumption. Therefore, the potential for DR using building stock offers a largely untapped resource. Heating, ventilation and air conditioning (HVAC) systems provide one of the largest possible sources for DR in buildings. However, coordinating the real-time aggregated response of multiple HVAC units across large numbers of buildings and stakeholders poses a challenging problem. Leveraging upon the concepts of Industry 4.0, this paper presents a large-scale decentralized discrete optimization framework to address this problem. Specifically, the paper first focuses upon the real-time dispatch problem for individual HVAC units in the presence of a tertiary DR program. The dispatch problem is formulated as a non-linear constrained predictive control problem, and an efficient dynamic programming (DP) algorithm with fixed memory and computation time overheads is developed for its efficient solution in real-time on individual HVAC units. Subsequently, in order to coordinate dispatch among multiple HVAC units in parallel by a DR aggregator, a flexible and efficient allocation/reallocation DP algorithm is developed to extract the cost-optimal solution and generate dispatch instructions for individual units. Accurate baselining at individual unit and aggregated levels for post-settlement is considered as an integrated component of the presented algorithms. A number of calibrated simulation studies and practical experimental tests are described to verify and illustrate the performance of the proposed schemes. The results illustrate that the distributed optimization algorithm enables a scalable, flexible solution helping to deliver the provision of aggregated tertiary DR for HVAC systems for both aggregators and individual customers. The paper concludes with a discussion of future work.

In this paper, it is argued that some low-level aspects of the usual IEC 61850 mapping to Ethernet are not well suited to microgrids due to their dynamic nature and geographical distribution as compared to substations. It is proposed that the integration of IEEE time-sensitive networking (TSN) concepts (which are currently implemented as audio video bridging (AVB) technologies) within an IEC 61850 / Manufacturing Message Specification framework provides a flexible and reconfigurable platform capable of overcoming such issues. A prototype test platform and bump-in-the-wire device for tunneling horizontal traffic through AVB are described. Experimental results are presented for sending IEC 61850 GOOSE (generic object oriented substation events) and SV (sampled values) messages through AVB tunnels. The obtained results verify that IEC 61850 event and sampled data may be reliably transported within the proposed framework with very low latency, even over a congested network. It is argued that since AVB streams can be flexibly configured from one or more central locations, and bandwidth reserved for their data ensuring predictability of delivery, this gives a solution which seems significantly more reliable than a pure MMS-based solution.

Energy storage is recognized as a key technology for enabling the transition to a low-carbon, sustainable future. Energy storage requires careful management, and capacity prediction of a lithium-ion battery (LIB) is an essential indicator in a battery management system for Electric Vehicles and Electricity Grid Management. However, present techniques for capacity prediction rely mainly on the quality of the features extracted from measured signals under strict operating conditions. To improve flexibility and accuracy, this paper introduces a new paradigm based on a multi-domain features time-frequency image (TFI) analysis and transfer deep learning algorithm, in order to extract diagnostic characteristics on the degradation inside the LIB. Continuous wavelet transform (CWT) is used to transfer the one-dimensional (1D) terminal voltage signals of the battery into 2D images (i.e., wavelet energy concentration). The generated TFIs are fed into the 2D deep learning algorithms to extract the features from the battery voltage images. The extracted features are then used to predict the capacity of the LIB. To validate the proposed technique, experimental data on LIB cells from the experimental datasets published by the Prognostics Center of Excellence (PCoE) NASA were used. The results show that the TFI analysis clearly visualised the degradation process of the battery due to its capability to extract different information on electrochemical features from the non-stationary and non-linear nature of the battery signal in both the time and frequency domains. AlexNet and VGG-16 transfer deep learning neural networks combined with stochastic gradient descent with momentum (SGDM) and adaptive data momentum (ADAM) optimization algorithms are examined to classify the obtained TFIs at different capacity values. The results reveal that the proposed scheme achieves 95.60% prediction accuracy, indicating good potential for the design of improved battery management systems.

Intelligent energy management in renewable-based power distribution applications, such as microgrids, smart grids, smart buildings, and EV systems, is becoming increasingly important in the context of the transition toward the decentralization, digitalization, and decarbonization of energy networks. Arguably, many challenges can be overcome, and benefits leveraged, in this transition by the adoption of intelligent autonomous computer-based decision-making through the introduction of smart technologies, specifically artificial intelligence. Unlike other numerical or soft computing optimization methods, the control based on artificial intelligence allows the decentralized power units to collaborate in making the best decision of fulfilling the administrator’s needs, rather than only a primitive decentralization based only on the division of tasks. Among the smart approaches, reinforcement learning stands as the most relevant and successful, particularly in power distribution management applications. The reason is it does not need an accurate model for attaining an optimized solution regarding the interaction with the environment. Accordingly, there is an ongoing need to accomplish a clear, up-to-date, vision of the development level, especially with the lack of recent comprehensive detailed reviews of this vitally important research field. Therefore, this paper fulfills the need and presents a comprehensive review of the state-of-the-art successful and distinguished intelligent control strategies-based RL in optimizing the management of power flow and distribution. Wherein extensive importance is given to the classification of the literature on emerging strategies, the proposals based on RL multiagent, and the multiagent primary secondary control of managing power flow in micro and smart grids, particularly the energy storage. As a result, 126 of the most relevant, recent, and non-incremental have been reviewed and put into relevant categories. Furthermore, salient features have been identified of the major positive and negative, of each selection.