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Society 5.0 represents an opportunity to transform the economy and create a digital society with the goal of long-term sustainable development and economic growth. There is a growing importance of boosting ICT as an effective and efficient means of achieving this transformation, and Target 9c of the UN Sustainable Development Goals is to ‘Significantly increase access to information and communications technology and strive to provide universal and affordable access to the Internet in least developed countries’. Mobile telecommunication systems have become the most effective and convenient means of communicating in the world, and as such, they are revolutionizing business operations. Nigeria is the fastest growing telecommunication market in Africa, with approximately 298 million subscribers accommodated by over 53,000 base transceiver stations (BTSs) which are largely concentrated in urban areas. As a result of increasing subscribers, all mobile network service providers in Nigeria are building new BTSs, often without considering existing infrastructure. This has led to a proliferation of masts, defacing the environment and causing unnecessary environmental pollution as BTSs are largely powered by diesel generators. It is therefore becoming paramount for the telecommunication regulatory body in Nigeria to enforce principles of infrastructure sharing and the colocation of sites for all mobile network service provider BTSs to improve network availability, reliability, scalability, customer satisfaction and sustainability. This paper argues, through the development of ICT tools and their application to a case study, that infrastructure sharing and colocation of sites is not only feasible if supported correctly but also offers the potential to reduce operational and capital expenditure, reduce the number of BTSs required for the rapidly growing mobile telecoms industry in Nigeria and in doing so reduce environmental pollution.
Kennedy Amadasun; Michael Short; Rajesh Shankar-Priya; Tracey Crosbie. Transitioning to Society 5.0 in Africa: Tools to Support ICT Infrastructure Sharing. Data 2021, 6, 69 .
AMA StyleKennedy Amadasun, Michael Short, Rajesh Shankar-Priya, Tracey Crosbie. Transitioning to Society 5.0 in Africa: Tools to Support ICT Infrastructure Sharing. Data. 2021; 6 (7):69.
Chicago/Turabian StyleKennedy Amadasun; Michael Short; Rajesh Shankar-Priya; Tracey Crosbie. 2021. "Transitioning to Society 5.0 in Africa: Tools to Support ICT Infrastructure Sharing." Data 6, no. 7: 69.
Heating, ventilating, and air-conditioning (HVAC) systems account for a large percentage of energy consumption in buildings. Implementation of efficient optimisation and control mechanisms has been identified as one crucial way to help reduce and shift HVAC systems’ energy consumption to both save economic costs and foster improved integration with renewables. This has led to the development of various control techniques, some of which have produced promising results. However, very few of these control mechanisms have fully considered important factors such as electricity time of use (TOU) price information, occupant thermal comfort, computational complexity, and nonlinear HVAC dynamics to design a demand response schema. In this paper, a novel two-stage integrated approach for such is proposed and evaluated. A model predictive control (MPC)-based optimiser for supervisory setpoint control is integrated with a digital parameter-adaptive controller for use in a demand response/demand management environment. The optimiser is designed to shift the heating load (and hence electrical load) to off-peak periods by minimising a trade-off between thermal comfort and electricity costs, generating a setpoint trajectory for the inner loop HVAC tracking controller. The tracking controller provides HVAC model information to the outer loop for calibration purposes. By way of calibrated simulations, it was found that significant energy saving and cost reduction could be achieved in comparison to a traditional on/off or variable HVAC control system with a fixed setpoint temperature.
Akinkunmi Adegbenro; Michael Short; Claudio Angione. An Integrated Approach to Adaptive Control and Supervisory Optimisation of HVAC Control Systems for Demand Response Applications. Energies 2021, 14, 2078 .
AMA StyleAkinkunmi Adegbenro, Michael Short, Claudio Angione. An Integrated Approach to Adaptive Control and Supervisory Optimisation of HVAC Control Systems for Demand Response Applications. Energies. 2021; 14 (8):2078.
Chicago/Turabian StyleAkinkunmi Adegbenro; Michael Short; Claudio Angione. 2021. "An Integrated Approach to Adaptive Control and Supervisory Optimisation of HVAC Control Systems for Demand Response Applications." Energies 14, no. 8: 2078.
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.
Ma’D El-Dalahmeh; Maher Al-Greer; Mo’Ath El-Dalahmeh; Michael Short. Time-Frequency Image Analysis and Transfer Learning for Capacity Prediction of Lithium-Ion Batteries. Energies 2020, 13, 5447 .
AMA StyleMa’D El-Dalahmeh, Maher Al-Greer, Mo’Ath El-Dalahmeh, Michael Short. Time-Frequency Image Analysis and Transfer Learning for Capacity Prediction of Lithium-Ion Batteries. Energies. 2020; 13 (20):5447.
Chicago/Turabian StyleMa’D El-Dalahmeh; Maher Al-Greer; Mo’Ath El-Dalahmeh; Michael Short. 2020. "Time-Frequency Image Analysis and Transfer Learning for Capacity Prediction of Lithium-Ion Batteries." Energies 13, no. 20: 5447.
This paper presents a decentralized informatics, optimization, and control framework to enable demand response (DR) in small or rural decentralized community power systems, including geographical islands. The framework consists of a simplified lumped model for electrical demand forecasting, a scheduling subsystem that optimizes the utility of energy storage assets, and an active/pro-active control subsystem. The active control strategy provides secondary DR services, through optimizing a multi-objective cost function formulated using a weight-based routing algorithm. In this context, the total weight of each edge between any two consecutive nodes is calculated as a function of thermal comfort, cost (tariff), and the rate at which electricity is consumed over a short future time horizon. The pro-active control strategy provides primary DR services. Furthermore, tertiary DR services can be processed to initiate a sequence of operations that enables the continuity of applied electrical services for the duration of the demand side event. Computer simulations and a case study using hardware-in-the-loop testing is used to evaluate the optimization and control module. The main conclusion drawn from this research shows the real-time operation of the proposed optimization and control scheme, operating on a prototype platform, underpinned by the effectiveness of the new methods and approach for tackling the optimization problem. This research recommends deployment of the optimization and control scheme, at scale, for decentralized community energy management. The paper concludes with a short discussion of business aspects and outlines areas for future work.
Sean Williams; Michael Short; Tracey Crosbie; Maryam Shadman-Pajouh. A Decentralized Informatics, Optimization, and Control Framework for Evolving Demand Response Services. Energies 2020, 13, 4191 .
AMA StyleSean Williams, Michael Short, Tracey Crosbie, Maryam Shadman-Pajouh. A Decentralized Informatics, Optimization, and Control Framework for Evolving Demand Response Services. Energies. 2020; 13 (16):4191.
Chicago/Turabian StyleSean Williams; Michael Short; Tracey Crosbie; Maryam Shadman-Pajouh. 2020. "A Decentralized Informatics, Optimization, and Control Framework for Evolving Demand Response Services." Energies 13, no. 16: 4191.
Solving the issue of energy security for geographical islands presents a one-of-a-kind problem that has to be tackled from multiple sides and requires an interdisciplinary approach that transcends just technical and social aspects. With many islands suffering in terms of limited and costly energy supply due to their remote location, providing a self-sustainable energy system is of utmost importance for these communities. In order to improve upon the status quo, novel solutions and projects aimed at increasing sustainability not only have to consider optimal utilization of renewable energy potentials in accordance with local conditions, but also must include active community participation. This paper analyzes both of these aspects for island communities and brings them together in an optimization scenario that is utilized to determine the relationship between supposed demand flexibility levels and achievable savings in a setting with variable renewable generation. The results, specifically discussed for a use case with real-world data for the La Graciosa island in Spain, show that boosting community participation and thus unlocking crucial demand flexibility, can be used as a powerful tool to augment novel generation technologies with savings from flexibility at around 7.5% of what is achieved purely by renewable sources.
Marko Jelić; Marko Batić; Nikola Tomašević; Andrew Barney; Heracles Polatidis; Tracey Crosbie; Dana Abi Abi Ghanem; Michael Short; Gobind Pillai. Towards Self-Sustainable Island Grids through Optimal Utilization of Renewable Energy Potential and Community Engagement. Energies 2020, 13, 3386 .
AMA StyleMarko Jelić, Marko Batić, Nikola Tomašević, Andrew Barney, Heracles Polatidis, Tracey Crosbie, Dana Abi Abi Ghanem, Michael Short, Gobind Pillai. Towards Self-Sustainable Island Grids through Optimal Utilization of Renewable Energy Potential and Community Engagement. Energies. 2020; 13 (13):3386.
Chicago/Turabian StyleMarko Jelić; Marko Batić; Nikola Tomašević; Andrew Barney; Heracles Polatidis; Tracey Crosbie; Dana Abi Abi Ghanem; Michael Short; Gobind Pillai. 2020. "Towards Self-Sustainable Island Grids through Optimal Utilization of Renewable Energy Potential and Community Engagement." Energies 13, no. 13: 3386.
Capacity markets (CM) are energy markets created to ensure energy supply security. Energy storage devices provide services in the CMs. Li-ion batteries are a popular type of energy storage device used in CMs. The battery lifetime is a key factor in determining the economic viability of Li-ion batteries, and current approaches for estimating this are limited. This paper explores the potential of a lithium-ion battery to provide CM services with four de-rating factors (0.5 h, 1 h, 2 h, and 4 h). During the CM contract, the battery experiences both calendar and cycle degradation, which reduces the overall profit. Physics-based battery and degradation models are used to quantify the degradation costs for batteries in the CM to enhance the previous research results. The degradation model quantifies capacity losses related to the solid–electrolyte interphase (SEI) layer, active material loss, and SEI crack growth. The results show that the physics-based degradation model can accurately predict degradation costs under different operating conditions, and thus can substantiate the business case for the batteries in the CM. The simulated CM profits can be increased by 60% and 75% at 5 °C and 25 °C, respectively, compared to empirical and semiempirical degradation models. A sensitivity analysis for a range of parameters is performed to show the effects on the batteries’ overall profit margins.
Ahmed Gailani; Maher Al-Greer; Michael Short; Tracey Crosbie; Nashwan Dawood. Lifetime Degradation Cost Analysis for Li-Ion Batteries in Capacity Markets using Accurate Physics-Based Models. Energies 2020, 13, 2816 .
AMA StyleAhmed Gailani, Maher Al-Greer, Michael Short, Tracey Crosbie, Nashwan Dawood. Lifetime Degradation Cost Analysis for Li-Ion Batteries in Capacity Markets using Accurate Physics-Based Models. Energies. 2020; 13 (11):2816.
Chicago/Turabian StyleAhmed Gailani; Maher Al-Greer; Michael Short; Tracey Crosbie; Nashwan Dawood. 2020. "Lifetime Degradation Cost Analysis for Li-Ion Batteries in Capacity Markets using Accurate Physics-Based Models." Energies 13, no. 11: 2816.
This paper presents a SWOT analysis of the impact of recent EU regulatory changes on the business case for energy storage (ES) using the UK as a case study. ES technologies (such as batteries) are key enablers for increasing the share of renewable energy generation and hence decarbonising the electricity system. As such, recent regulatory changes seek to improve the business case for ES technologies on national networks. These changes include removing double network charging for ES, defining and classifying ES in relevant legislations, and clarifying ES ownership along with facilitating its grid access. However, most of the current regulations treat storage in a similar way to bulk generators without paying attention to the different sizes and types of ES. As a result, storage with higher capacity receives significantly higher payment in the capacity market and can be exempt from paying renewable energy promotion taxes. Despite the recent regulatory changes, ES is defined as a generation device, which is a barrier to a wide range of revenue streams from demand side services. Also, regulators avoid disrupting the current energy market structure by creating an independent asset class for ES. Instead, they are encouraging changes that co-exist with the current market and regulatory structure. Therefore, although some of the reviewed market and regulatory changes for ES in this paper are positive, it can be concluded that these changes are not likely to allow a level playing field for ES that encourage its increase on energy networks.
Ahmed Gailani; Tracey Crosbie; Maher Al-Greer; Michael Short; Nashwan Dawood. On the Role of Regulatory Policy on the Business Case for Energy Storage in Both EU and UK Energy Systems: Barriers and Enablers. Energies 2020, 13, 1080 .
AMA StyleAhmed Gailani, Tracey Crosbie, Maher Al-Greer, Michael Short, Nashwan Dawood. On the Role of Regulatory Policy on the Business Case for Energy Storage in Both EU and UK Energy Systems: Barriers and Enablers. Energies. 2020; 13 (5):1080.
Chicago/Turabian StyleAhmed Gailani; Tracey Crosbie; Maher Al-Greer; Michael Short; Nashwan Dawood. 2020. "On the Role of Regulatory Policy on the Business Case for Energy Storage in Both EU and UK Energy Systems: Barriers and Enablers." Energies 13, no. 5: 1080.
The world is experiencing a fourth industrial revolution. Rapid development of technologies is advancing smart infrastructure opportunities. Experts observe decarbonisation, digitalisation and decentralisation as the main drivers for change. In electrical power systems a downturn of centralised conventional fossil fuel fired power plants and increased proportion of distributed power generation adds to the already troublesome outlook for operators of low-inertia energy systems. In the absence of reliable real-time demand forecasting measures, effective decentralised demand-side energy planning is often problematic. In this work we formulate a simple yet highly effective lumped model for forecasting the rate at which electricity is consumed. The methodology presented focuses on the potential adoption by a regional electricity network operator with inadequate real-time energy data who requires knowledge of the wider aggregated future rate of energy consumption. Thus, contributing to a reduction in the demand of state-owned generation power plants. The forecasting session is constructed initially through analysis of a chronological sequence of discrete observations. Historical demand data shows behaviour that allows the use of dimensionality reduction techniques. Combined with piecewise interpolation an electricity demand forecasting methodology is formulated. Solutions of short-term forecasting problems provide credible predictions for energy demand. Calculations for medium-term forecasts that extend beyond 6-months are also very promising. The forecasting method provides a way to advance a novel decentralised informatics, optimisation and control framework for small island power systems or distributed grid-edge systems as part of an evolving demand response service.
Sean Williams; Michael Short. Electricity demand forecasting for decentralised energy management. Energy and Built Environment 2020, 1, 178 -186.
AMA StyleSean Williams, Michael Short. Electricity demand forecasting for decentralised energy management. Energy and Built Environment. 2020; 1 (2):178-186.
Chicago/Turabian StyleSean Williams; Michael Short. 2020. "Electricity demand forecasting for decentralised energy management." Energy and Built Environment 1, no. 2: 178-186.
Increased deployment of intermittent renewable energy plants raises concerns about energy security and energy affordability. Capacity markets (CMs) have been implemented to provide investment stability to generators and secure energy generation by reducing the number of shortage hours. The research presented in this paper contributes to answering the question of whether batteries can provide cost effective back up services for one year in this market. The analysis uses an equivalent circuit lithium ion battery model coupled with two degradation models (empirical and semi-empirical) to account for capacity fade during battery lifetime. Depending on the battery’s output power, four de-rating factors of 0.5 h, 1 h, 2 h and 4 h are considered to study which de-rating strategy can result in best economic profit. Two scenarios for the number of shortage hours per year in the CM are predicted based on the energy demand data of Great Britain and recent research. Results show that the estimated battery profit is maximum with 2 h and 1 h de-rating factors and minimum with 4 h and 0.5 h. Depending on the battery degradation model used, battery degradation cost can considerably impact the potential profit if the battery’s temperature is not controlled with adequate thermal management system. The empirical and semi-empirical models predict that the degradation cost is minimum at 5 °C and 25 °C respectively. Moreover, both models predict degradation is minimum at lower battery charge levels. While the battery’s capacity fade can be minimized to make some profits from the CM service, the increased shortage hours can make providing this service not economically viable.
Ahmed Gailani; Maher Al-Greer; Michael Short; Tracey Crosbie. Degradation Cost Analysis of Li-Ion Batteries in the Capacity Market with Different Degradation Models. Electronics 2020, 9, 90 .
AMA StyleAhmed Gailani, Maher Al-Greer, Michael Short, Tracey Crosbie. Degradation Cost Analysis of Li-Ion Batteries in the Capacity Market with Different Degradation Models. Electronics. 2020; 9 (1):90.
Chicago/Turabian StyleAhmed Gailani; Maher Al-Greer; Michael Short; Tracey Crosbie. 2020. "Degradation Cost Analysis of Li-Ion Batteries in the Capacity Market with Different Degradation Models." Electronics 9, no. 1: 90.
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.
Michael Short; Sergio Rodriguez; Richard Charlesworth; Tracey Crosbie; Nashwan Dawood. Optimal Dispatch of Aggregated HVAC Units for Demand Response: An Industry 4.0 Approach. Energies 2019, 12, 4320 .
AMA StyleMichael Short, Sergio Rodriguez, Richard Charlesworth, Tracey Crosbie, Nashwan Dawood. Optimal Dispatch of Aggregated HVAC Units for Demand Response: An Industry 4.0 Approach. Energies. 2019; 12 (22):4320.
Chicago/Turabian StyleMichael Short; Sergio Rodriguez; Richard Charlesworth; Tracey Crosbie; Nashwan Dawood. 2019. "Optimal Dispatch of Aggregated HVAC Units for Demand Response: An Industry 4.0 Approach." Energies 12, no. 22: 4320.
This paper is concerned with the implementation and field-testing of an edge device for real-time condition monitoring and fault detection for large-scale rotating equipment in the UK water industry. The edge device implements a local digital twin, processing information from low-cost transducers mounted on the equipment in real-time. Condition monitoring is achieved with sliding-mode observers employed as soft sensors to estimate critical internal pump parameters to help detect equipment weasr before damage occurs. The paper describes the implementation of the edge system on a prototype microcontroller-based embedded platform, which supports the Modbus protocol; IP/GSM communication gateways provide remote connectivity to the network core, allowing further detailed analytics for predictive maintenance to take place. The paper first describes validation testing of the edge device using Hardware-In-The-Loop techniques, followed by trials on large-scale pumping equipment in the field. The paper concludes that the proposed system potentially delivers a flexible and low-cost industrial digitalization platform for condition monitoring and predictive maintenance applications in the water industry.
Michael Short; John Twiddle. An Industrial Digitalization Platform for Condition Monitoring and Predictive Maintenance of Pumping Equipment. Sensors 2019, 19, 3781 .
AMA StyleMichael Short, John Twiddle. An Industrial Digitalization Platform for Condition Monitoring and Predictive Maintenance of Pumping Equipment. Sensors. 2019; 19 (17):3781.
Chicago/Turabian StyleMichael Short; John Twiddle. 2019. "An Industrial Digitalization Platform for Condition Monitoring and Predictive Maintenance of Pumping Equipment." Sensors 19, no. 17: 3781.
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.
Sean Williams; Michael Short; Tracey Crosbie. On the use of thermal inertia in building stock to leverage decentralised demand side frequency regulation services. Applied Thermal Engineering 2018, 133, 97 -106.
AMA StyleSean Williams, Michael Short, Tracey Crosbie. On the use of thermal inertia in building stock to leverage decentralised demand side frequency regulation services. Applied Thermal Engineering. 2018; 133 ():97-106.
Chicago/Turabian StyleSean Williams; Michael Short; Tracey Crosbie. 2018. "On the use of thermal inertia in building stock to leverage decentralised demand side frequency regulation services." Applied Thermal Engineering 133, no. : 97-106.
Fossil fuels deliver most of the flexibility in contemporary electricity systems. The pressing need to reduce CO2 emissions requires new methods to provide this flexibility. Demand response (DR) offers consumers a significant role in the delivery of flexibility by reducing or shifting their electricity usage during periods of stress or constraint. Blocks of buildings offer more flexibility in the timing and use of energy than single buildings, however, and a lack of relevant scalable ICT tools hampers DR in blocks of buildings. To ameliorate this problem, a current innovation project called “Demand Response in Blocks of Buildings” (DR-BoB: www.dr-bob.eu) has integrated existing technologies into a scalable cloud-based solution for DR in blocks of buildings. The degree to which the DR-BoB energy management solution can increase the ability of any given site to participate in DR is dependent upon its current energy systems, i.e., the energy metering, the telemetry and control technologies in building management systems, and the existence/capacity of local power generation and storage plants. To encourage the owners and managers of blocks of buildings to participate in DR, a method of assessing and validating the technological readiness to participate in DR energy management solutions at any given site is required. This paper describes the DR-BoB energy management solution and outlines what we have called the demand response technology readiness levels (DRTRLs) for the implementation of such a solution in blocks of buildings.
Tracey Crosbie; John Broderick; Michael Short; Richard Charlesworth; Muneeb Dawood. Demand Response Technology Readiness Levels for Energy Management in Blocks of Buildings. Buildings 2018, 8, 13 .
AMA StyleTracey Crosbie, John Broderick, Michael Short, Richard Charlesworth, Muneeb Dawood. Demand Response Technology Readiness Levels for Energy Management in Blocks of Buildings. Buildings. 2018; 8 (2):13.
Chicago/Turabian StyleTracey Crosbie; John Broderick; Michael Short; Richard Charlesworth; Muneeb Dawood. 2018. "Demand Response Technology Readiness Levels for Energy Management in Blocks of Buildings." Buildings 8, no. 2: 13.
Industrial automation and control systems are increasingly deployed using wireless networks in master-slave, star-type configurations that employ a slotted timeline schedule. In this paper, the scheduling of (re)transmissions to meet real-time constraints in the presence of non-uniform interference in such networks is considered. As packet losses often occur in correlated bursts, it is often useful to insert gaps before attempting retransmissions. In this paper, a quantum Earliest Deadline First (EDF) scheduling framework entitled ‘Eligible EDF’ is suggested for assigning (re)transmissions to available timeline slots by the master node. A simple but effective server strategy is introduced to reclaim unused channel utilization and replenish failed slave transmissions, a strategy which prevents cascading failures and naturally introduces retransmission gaps. Analysis and examples illustrate the effectiveness of the proposed method. Specifically, the proposed framework gives a timely throughput of 99.81% of the timely throughput that is optimally achievable using a clairvoyant scheduler
Michael Short. Eligible earliest deadline first: Server-based scheduling for master-slave industrial wireless networks. Computers & Electrical Engineering 2017, 64, 305 -321.
AMA StyleMichael Short. Eligible earliest deadline first: Server-based scheduling for master-slave industrial wireless networks. Computers & Electrical Engineering. 2017; 64 ():305-321.
Chicago/Turabian StyleMichael Short. 2017. "Eligible earliest deadline first: Server-based scheduling for master-slave industrial wireless networks." Computers & Electrical Engineering 64, no. : 305-321.
Model predictive control (MPC) schemes employ dynamic models of a process within a receding horizon framework to optimize the behavior of a process. Although MPC has many benefits, a significant drawback is the large computational burden, especially in adaptive and constrained situations. In this paper, a computationally efficient self-tuning/adaptive MPC scheme for a simple industrial process plant with rate and amplitude constraints on the plant input is developed. The scheme has been optimized for real-time implementation on small, low-cost embedded processors. It employs a short (2-step) control horizon with an adjustable prediction horizon, automatically tunes the move suppression (regularization) parameter to achieve well-conditioned control, and presents a new technique for generating the reference trajectory that is robust to changes in the process time delay and in the presence of any inverse response. In addition, the need for a full quadratic programming procedure to handle input constraints is avoided by employing a quasi-analytical solution that optimally fathoms the constraints. Preliminary hardware-in-the-loop (HIL) test results indicate that the resulting scheme performs well and has low implementation overhead.
Michael Short; Fathi Abugchem. A Microcontroller-Based Adaptive Model Predictive Control Platform for Process Control Applications. Electronics 2017, 6, 88 .
AMA StyleMichael Short, Fathi Abugchem. A Microcontroller-Based Adaptive Model Predictive Control Platform for Process Control Applications. Electronics. 2017; 6 (4):88.
Chicago/Turabian StyleMichael Short; Fathi Abugchem. 2017. "A Microcontroller-Based Adaptive Model Predictive Control Platform for Process Control Applications." Electronics 6, no. 4: 88.
Tracey Crosbie; John Broderick; Muneeb Dawood; Richard Charlesworth; Vladimir Vukovic; Michael Short; Nashwan Dawood. Integrating Technologies for Demand Response in Blocks of Buildings - A UK Case Study. Lean and Computing in Construction Congress - Volume 1: Proceedings of the Joint Conference on Computing in Construction 2017, 1 .
AMA StyleTracey Crosbie, John Broderick, Muneeb Dawood, Richard Charlesworth, Vladimir Vukovic, Michael Short, Nashwan Dawood. Integrating Technologies for Demand Response in Blocks of Buildings - A UK Case Study. Lean and Computing in Construction Congress - Volume 1: Proceedings of the Joint Conference on Computing in Construction. 2017; ():1.
Chicago/Turabian StyleTracey Crosbie; John Broderick; Muneeb Dawood; Richard Charlesworth; Vladimir Vukovic; Michael Short; Nashwan Dawood. 2017. "Integrating Technologies for Demand Response in Blocks of Buildings - A UK Case Study." Lean and Computing in Construction Congress - Volume 1: Proceedings of the Joint Conference on Computing in Construction , no. : 1.
Michael Short. Timing analysis for embedded systems using non-preemptive EDF scheduling under bounded error arrivals. Applied Computing and Informatics 2017, 13, 130 -139.
AMA StyleMichael Short. Timing analysis for embedded systems using non-preemptive EDF scheduling under bounded error arrivals. Applied Computing and Informatics. 2017; 13 (2):130-139.
Chicago/Turabian StyleMichael Short. 2017. "Timing analysis for embedded systems using non-preemptive EDF scheduling under bounded error arrivals." Applied Computing and Informatics 13, no. 2: 130-139.
Tracey Crosbie; Michael Short; Muneeb Dawood; Richard Charlesworth. Demand response in blocks of buildings: opportunities and requirements. Entrepreneurship and Sustainability Issues 2017, 4, 271 -281.
AMA StyleTracey Crosbie, Michael Short, Muneeb Dawood, Richard Charlesworth. Demand response in blocks of buildings: opportunities and requirements. Entrepreneurship and Sustainability Issues. 2017; 4 (3):271-281.
Chicago/Turabian StyleTracey Crosbie; Michael Short; Muneeb Dawood; Richard Charlesworth. 2017. "Demand response in blocks of buildings: opportunities and requirements." Entrepreneurship and Sustainability Issues 4, no. 3: 271-281.
Several popular tuning strategies applicable to Model Predictive Control (MPC) schemes such as GPC and DMC have previously been developed. Many of these tuning strategies require an approximate model of the controlled process to be obtained, typically of the First Order Plus Dead Time type. One popular method uses such a model to analytically calculate an approximate value of the move suppression coefficient to achieve a desired condition number for the regularized system dynamic matrix; however it is not always accurate and tends to under-estimate the required value. In this paper an off-line method is presented to exactly calculate the move suppression coefficient required to achieve a desired condition number directly from the unregularized system dynamic matrix. This method involves an Eigendecomposition of the system dynamic matrix - which may be too unwieldy in some cases -and a simpler analytical expression is also derived. This analytical expression provides a guaranteed tight upper bound on the required move suppression coefficient yielding a tuning formula which is easy to apply, even in on-line situations. Both methods do not require the use of approximate or reduced order process models for their application. Simulation examples and perturbation studies illustrate the effectiveness of the methods in both off-line and on-line MPC configurations. It is shown that accurate conditioning and improved closed loop robustness can be achieved.
Michael Short. Move Suppression Calculations for Well-Conditioned MPC. ISA Transactions 2017, 67, 371 -381.
AMA StyleMichael Short. Move Suppression Calculations for Well-Conditioned MPC. ISA Transactions. 2017; 67 ():371-381.
Chicago/Turabian StyleMichael Short. 2017. "Move Suppression Calculations for Well-Conditioned MPC." ISA Transactions 67, no. : 371-381.
Environmental concerns combined with the liberalisation of the energy markets has led to the emergence of small to medium-scale decentralised generation equipment embedded within transmission and distribution networks. Commonly, such plant is operated by small to medium private enterprises and dispatched independently from centralised resources. The liberalisation of energy markets has also brought about the rise of variable wholesale electricity markets, in the form of the spot (day-ahead) market and the balancing (intra-day) markets across the EU and beyond. As such, there is much interest in how decentralised generation equipment can be most profitably operated in this context. This paper focuses on short-term forecasting of both heat and electrical loads, along with unit commitment scheduling and economic dispatch optimisation, for a small/medium scale decentralised combined heat and power (CHP) plant. In the work presented the plant is assumed to be equipped with local heat and electricity storage and operating in the presence of fluctuating wholesale energy prices and local loads. The approach adopted builds on recent research employing Mixed Integer Linear Programming (MILP) models and non-linear boiler efficiency curves, and extends this work into a rolling horizon context. Results are presented which demonstrate the efficiency of the proposed approach and investigate the sensitivity of the results with respect to CHP model accuracy and load prediction accuracy. The results indicated that profit is much more sensitive to the accuracy of load predictions than indicated by previous work in the area. The findings also challenge those of recent work in the field, which suggest that a strategy of interacting with the spot (day-ahead) market only is the most profitable for small/medium scale decentralised energy producers. The results presented in this paper indicate that when load prediction inaccuracies are also considered in the CHP optimisation framework, a strategy interacting with both the spot (day-ahead) market and the balancing (intra-day) market is significantly more profitable than a strategy interacting with the spot market only
Michael Short; Tracey Crosbie; Muneeb Dawood; Nashwan Dawood. Load forecasting and dispatch optimisation for decentralised co-generation plant with dual energy storage. Applied Energy 2017, 186, 304 -320.
AMA StyleMichael Short, Tracey Crosbie, Muneeb Dawood, Nashwan Dawood. Load forecasting and dispatch optimisation for decentralised co-generation plant with dual energy storage. Applied Energy. 2017; 186 ():304-320.
Chicago/Turabian StyleMichael Short; Tracey Crosbie; Muneeb Dawood; Nashwan Dawood. 2017. "Load forecasting and dispatch optimisation for decentralised co-generation plant with dual energy storage." Applied Energy 186, no. : 304-320.