A real-time demand response pricing model for the smart grid

Submitted to the University of Bedfordshire, in partial fulfilment of the requirements for the degree of Doctor of Philosophy (PhD)This thesis contributes to a novel model for Real-Time Price Suggestions (RTPS) of the Smart Grid (SG), which is the next generation modern bi-directional grid, particularly with respect to the pricing model. The research employs an experiment-based methodology which includes the use of a simulation technique. The research developed a Demand Response (DR) pricing model. Energy users are keen to reduce their bills, and Energy Providers (EP) is also keen on reducing their industrial costs. The DR model would benefit them both. The model has been tested with the UK-based traditional price value using real-time usage data. Energy users significantly reduced their bill and EP reduced their industrial cost due to load shifting. The Price Control Unit (PCU) and Price Suggestion Unit (PSU) utilise a set of embedded algorithms to vary price based upon demand. This model makes suggestions based on an energy threshold and makes use of Simultaneous Perturbation Stochastic Approximation Methods to produce prices. The results show that bill and peak load reductions benefit both the energy provider and users. The tests on a daily basis and monthly basis both benefit energy users and energy provider. The model has been validated by building a hardware prototype. This model also addresses users’ preferences; if users are non-responsive, they can still reduce their bills. The model contributes significantly to the existing models, and the novel contribution is the PSU which uniquely benefits energy users and provider. Therefore, there is a number of fundamental aspect of contributions to the model RTPS constitutes the final thesis of the PhD. The Real-Time Pricing is a better pricing system, algorithm developed on a daily basis and monthly basis and finally building a hardware prototype

Technoeconomic and whole-energy system analysis of low-carbon heating technologies

Despite developments in renewable electricity production, space heating and hot-water provision still account for a high proportion of the total greenhouse gas emissions in the world. Decarbonising heating requires an in-depth understanding of the candidate technology options. Should investments in energy systems focus on large-scale/centralised options, or small-scale/distributed ones? How should end-users operate their heating systems to maximise economic and environmental benefits? Should manufacturers design high-performance yet high-cost technologies and reduce the transition cost to the wider electricity system infrastructure, or should they promote more affordable, lower-performance end-use alternatives at a cost to the wider system? In this thesis, technoeconomic models that capture the cost and performance characteristics of heating technologies are developed and used to analyse the design and operation of competing solutions from the perspectives of different stakeholders. An extensive analysis of commercially available air-source and ground-source heat pumps, combined heat and power systems, district heating infrastructure and thermal energy storage systems on the UK market is first conducted. Fitting techniques are used to determine relationships arising from the collected data and quantify the related uncertainty in technology characteristics between the data and fitted relationships. Then, thermodynamic and component-costing models are developed for technologies for which there is a substantial spread in the available data, or for which data are not available. These include electricity- and hydrogen-driven heat pumps and involve dedicated compressor efficiency maps, heat exchanger models, and equipment-costing methods. The resulting technoeconomic models are first used to assess the economic and environmental performance of different centralised and distributed low-carbon heat provision pathways, with a London district as a case study. Centralised gas-fired combined heat and power systems are found to be favourable in terms of annual total cost. However, in recent years, the carbon footprint of grid electricity has reduced significantly, meaning that heat pumps installed at household or community level achieve a higher degree of decarbonisation. Furthermore, an uncertainty propagation analysis reveals the significance of properly accounting for technology performance and cost variations when modelling energy systems. In fact, the use of technoeconomic models is shown to reduce the uncertainty in the results by more than 75% compared to the use of black-box approaches. Two different optimisation studies are then conducted to investigate smart operation strategies of heating technologies in the domestic and commercial sectors. First, thermal network models of a domestic electric heat pump coupled to a hot-water cylinder or to two phase-change material thermal stores are developed and used to optimise heat pump operation for different objective functions. As demonstrated, smart heat pump operation can lead to a decrease in operational costs of more than 20% and an increase in self-sufficiency by up to four times. For the commercial sector, a multi-objective control framework is designed and installed on an existing combined heat and power system that provides heat and electricity to a supermarket. By using a stochastic optimisation approach and considering the uncertainty related to the price of exporting electricity, energy savings higher than 35% can be achieved compared to using a typical gas boiler. The integration of technoeconomic models of technologies within whole-energy system models can be used to extend the capabilities of the latter, so that they can, apart from optimising network infrastructures, provide explicit information about future technology design. Thermodynamic and component-costing models of a domestic electric heat pump, a hydrogen boiler and a hydrogen-driven absorption heat pump, as well an existing whole-energy system model of the UK, are used to compare electrification and hydrogen pathways for the domestic sector. The technologies are compared for different weather conditions and fuel-price scenarios, first from a homeowner’s and then from a whole-energy system perspective. It is shown that, in the UK, hydrogen technologies can be economically favourable only if hydrogen is supplied to domestic end-users at a price below half of the electricity price. From a whole-energy system perspective, electric heat pumps are the least-cost decarbonisation pathway under the investigated scenarios. Lastly, this thesis includes an effort to demonstrate how different component choices when designing domestic electric heat pumps can influence the national energy generation mix and heat-decarbonisation transition cost. Using the developed electric heat pump model, a set of optimal heat pump configurations representing competing components is obtained. The size of heat exchangers and the choice of compressor type and working fluid are shown to have a remarkable influence on the technology’s performance and cost. These configurations are integrated into an existing whole-energy system capacity-expansion and unit-dispatch model, to show that, from a UK energy system perspective, although high-performance heat pumps enable a reduction in the required installed electricity generation capacity by up to 50 GW, low-to-medium performance heat pumps can lead to a reduction of more than 10% in the total system transition cost and end-user investment requirements.Open Acces

Application of heat pumps and thermal storage systems for improved control and performance of microgrids

The high penetration of renewable energy sources (RES), in particular, the rooftop photovoltaic (PV) systems in power systems, causes rapid ramps in power generation to supply load during peak-load periods. Residential and commercial buildings have considerable potential for providing load exibility by exploiting energy-e_cient devices like ground source heat pump (GSHP). The proper integration of PV systems with the GSHP could reduce power demand from demand-side. This research provides a practical attempt to integrate PV systems and GSHPs e_ectively into buildings and the grid. The multi-directional approach in this work requires an optimal control strategy to reduce energy cost and provide an opportunity for power trade-o_ or feed-in in the electricity market. In this study, some optimal control models are developed to overcome both the operational and technical constraints of demand-side management (DSM) and for optimum integration of RES. This research focuses on the development of an optimal real-time thermal energy management system for smart homes to respond to DR for peak-load shifting. The intention is to manage the operation of a GSHP to produce the desired amount of thermal energy by controlling the volume and temperature of the stored water in the thermal energy storage (TES) while optimising the operation of the heat distributors to control indoor temperature. This thesis proposes a new framework for optimal sizing design and real-time operation of energy storage systems in a residential building equipped with a PV system, heat pump (HP), and thermal and electrical energy storage systems. The results of this research demonstrate to rooftop PV system owners that investment in combined TSS and battery can be more profitable as this system can minimise life cycle costs. This thesis also presents an analysis of the potential impact of residential HP systems into reserve capacity market. This research presents a business aggregate model for controlling residential HPs (RHPs) of a group of houses that energy aggregators can utilise to earn capacity credits. A control strategy is proposed based on a dynamic aggregate RHPs coupled with TES model and predicting trading intervals capacity requirements through forecasting demand and non-scheduled generation. RHPs coupled with TES are optimised to provide DSM reserve capacity. A rebound effect reduction method is proposed that reduces the peak rebound RHPs power

Modelling an Optimisation Selection Method for Buildings Design Toward Environmental & Economic Objectives

Improving the performance of buildings based on their energy consumption is a challenging task. The main contributing factor to the amount of energy a building consumes is associated with maintaining the satisfaction of the building’s users, by controlling the conditions within a building’s envelope. Two main design factors control the overall buildings energy performance, the Heating Ventilation and Air Conditioning (HVAC) system design and building envelope design. There are several studies aimed at finding optimum solutions, evaluating these factors individually. The researches focused on the HVAC system design, has limited number of variables going into it, comparing different systems, operation set ups and fuels. As for the researches focusing on the buildings’ envelop design, a large number of envelope’s design variables can influence the building’s energy consumption, such as its shape, geometry, material composition, elevation, and location, lead to different energy consumption rates. This research systematically investigates how three main building envelop design variables (Orientation, Aspect ratio/compactness and Window to Wall ratio) impact the overall building’s energy performance, including the potential of integrating sustainable energy generation systems, in search for optimum buildings designs than can achieve an environmental and economic balance. The first component is specific to the analyses of buildings’ energy performance/consumption, based on the three building’s envelop design variables. The energy performance considers different building geometries (from a square to a rectangular aspect ratio that is of length twice the width). Then, orienting those different forms at different directions. Further, varying the external walls composition at different window to wall ratios. The results are used to calculate the net yearly energy consumption rates and understand the patterns of energy consumption influenced by those three variables. All simulations are specific to the climate condition of Kuwait’s geolocation, to develop an informed perspective of the climate influence on energy patterns. The results obtained have unique patterns that do not particularly agree with the general conclusions cited by other researches, specific to the relationship between buildings compactness and the energy consumption. With the growing concerns of climate change effects on the environment, it’s no longer enough to aim for passive mitigation solutions by reducing the energy consumption. The goal is to push for active ways to generate energy using sustainable resources, when possible, in the most economically feasible way. Hence, the second component of this research, focused on the opportunities to utilise the envelope for energy generation. By integrating sustainable energy generation systems within the buildings’ façade, the dependency on the power from the grid, that is 10 mostly generated using fossil fuels, can be reduced. The climate characteristics of the Gulf Cooperation Council (GCC) countries impose specific challenges on buildings’ energy performance as well as the efficiency of sustainable energy generation systems. Specific challenges such as the effect of dust on the most productive sustainable source for energy generation, solar photovoltaic systems, must be considered. Accordingly, a prediction model is created to quantify the regional effect of dust on the productivity of PV systems. Then, given the specific building variables used in the buildings’ energy consumption calculations, the energy generation potentials are calculated. The last component of this research aims to optimise the objectives of lower energy consumption rates, higher energy generation potentials (Lower emissions), and lower investment costs. A model is created to find optimum solutions that can balance those contradicting objectives. The results are obtained to provide guidance to the designers toward environmental and economic decisions, through a set of different possible design combinations

Development of the Next Generation of Water Distribution Network Modelling Tools Using Inverse Methods

The application of optimisation to Water Distribution Network (WDN) Modelling involves the use of computer-based techniques to many different problems, such as leakage detection and localisation. The success in the application of any model-based methodology for finding leaks highly depends on the availability of a well-calibrated model. Both leak detection and localisation, as well as model calibration are procedures that constitute the field of inverse problems in WDN modelling. The procedures are interlinked and dependent as when a leak is found and the model is updated its quality improves, while when a model is calibrated its ability to detect and localise leaks also improves. This is because both inverse problems are solved with the aim to mimic the behaviour of the real system as closely as possible using field measurements. In this research, both inverse problems are formulated as constrained optimisation problems. Evolutionary Optimisation techniques, of which Genetic Algorithms are the best-known examples, are search methods that are increasingly applied in WDN modelling with the aim to improve the quality of a solution for a given problem. This, ultimately, aids practitioners in these facets of management and operation of WDNs. Evolutionary Optimisation employs processes that mimic the biological process of natural selection and “survival of the fittest” in an artificial framework. Based on this philosophy a population of individual solutions to the problem is manipulated and, over time, “evolves” towards optimal solutions. However, such algorithms are characterised by large numbers of function evaluations. This, coupled with the computational complexity associated with the hydraulic simulation of WDNs incurs significant computational burden, can limit the applicability and scalability of this technology across the Water Industry. In addition, the inverse problem is often “ill-posed”. In practice, the ill-posed condition is typically manifested by the non-uniqueness of the problem solution and it is usually a consequence of inadequate quantity and/or quality of field observations. Accordingly, this thesis presents a methodology for applying Genetic Algorithms to solve leakage related inverse problems in WDN Modelling. A number of new procedures are presented for improving the performance of such algorithms when applied to the complex inverse problems of leak detection and localisation, as well as model calibration. A novel reformulation of the inverse problem is developed as part of a decision support framework that minimizes the impact of the inherent computational complexity and dimensionality of these problems. A search space reduction technique is proposed, i.e., a reduction in the number of possible solution combinations to the inverse problem, to improve its condition considering the accuracy of the available measurements. Eventually, this corresponds to a targeted starting point for initiating the search process and therefore more robust stochastic optimisations. The ultimate purpose is to increase the reliability of the WDN hydraulic model in localising leaks in real District Metered Areas, i.e., to reduce the number false positives. In addition, to speed up the leak search process (both computationally and physically) and, improve the overall model accuracy. A calibrated model of the WDN is not always available for supporting work at distribution mains level. Consequently, two separate problem-specific methods are proposed to meet the abovementioned purpose: (a) a Leak Inspection Method used for the detection and localisation of leaks and; (b) a Calibration Method for producing an accurate average day model that is fit for the purpose of leak detection and localisation. Both methods integrate a three-step Search Space Reduction stage, which is implemented before solving the inverse problem. The aim is to minimize the number of decision variables and the range of possible values, while trying to preserve the optimum solution, i.e., reduce the inverse problem dimensionality. The search space reduction technique is established to generate a reduced set of highly sensitive decision variables. Eventually this is done to provide a viable, scalable technique for accelerating evolutionary optimisation applications in inverse problems being worthwhile on both academic and practical grounds. The novel methodologies presented here for leak detection and localisation, as well as for model calibration are verified successfully on four case studies. The case studies include two real WDN examples with artificially generated data, which investigate the limits of each method separately. The other two case studies implement both methods on real District Metered Areas in the United Kingdom, firstly to calibrate the hydraulic network model and, then, to detect and localise a single leak event that has actually happened. The research results suggest that leaks and unknown closed or open throttle valves that cause a hydraulic impact larger than the sensor data error can be detected and localised with the proposed framework which solves the inverse problem after search space reduction. Moreover, the quality of solutions can dramatically improve for given runtime of the algorithm, as 99.99% of infeasible solution combinations are removed, compared to the case where no search space reduction is performed. The outcomes of the real cases show that the presented search space reduction technique can reduce the search area for finding the leak to within 10% of the WDN (by length). The framework can also contribute to more timely detection and localisation of leakage hotspots, thus reducing economic and environmental impacts. The optimisation model for predicting leakage hotspots can be effective despite the recognized challenges of model calibration and the physical measurement limitations from the pressure and flow field tests

Energy management and guidelines to digitalisation of integrated natural gas distribution systems equipped with expander technology

In a swirling dynamic interaction, digital innovation, environment and anthropological evolution are swiftly shaping the smart grid scenario. Integration and flexibility are the keywords in this emergent picture characterised by a low carbon footprint. Digitalisation, within the natural limits imposed by the thermodynamics, seems to offer excellent opportunities for these purposes. Of course, here starts a new challenge: how digital technologies should be employed to achieve these objectives? How would we ensure a digital retrofit does not lead to a carbon emission increase? In author opinion, as long as it remains a generalised question, none answer exists: the need to contextualise the issue emerges from the variety of the characteristics of the energy systems and from their interactions with external processes. To address these points, in the first part of this research, the author presented a collection of his research contributions to the topic related to the energy management in natural gas pressure reduction station equipped with turbo expander technology. Furthermore, starting from the state of the art and the author's previous research contributions, the guidelines for the digital retrofit for a specific kind of distributed energy system, were outlined. Finally, a possible configuration of the ideal ICT architecture is extracted. This aims to achieve a higher level of coordination involving, natural gas distribution and transportation, local energy production, thermal user integration and electric vehicles charging. Finally, the barriers and the risks of a digitalisation process are critically analysed outlining in this way future research needs

Digital Twins in Civil Infrastructure Systems

This research explores the existing definitions, concepts and applications surrounding the efficient implementation and use of digital twins (DTs) within civil infrastructure systems (CISs). The CISs within the scope of this research are as follows: transportation, energy, telecommunications, water and waste, as well as Smart Cities, which encompasses all of the previous. The research methodology consists of a review of current literature, a series of semi-structured interviews and a detailed survey. The outcome of this work is a refined definition of DTs within CISs, in addition to a set of recommendations for both future academic research and industry best practice

Asset management in urban water utilities: Case study in India

Access to safe and sufficient drinking water and adequate sanitation are now recognized as basic human rights. One Millennium Development Goal is to reduce by half the proportion of people without access to safe drinking water and basic sanitation by 2015. However, ensuring sustainability of existing and new services is considered to be one of the major challenges for the water sector in the years to come. In India, in addition to service expansion, existing water service quality has been observed to be deteriorating over recent years. There is therefore an equally urgent need to address sustainability and improvement of service quality to the presently served population. In this low-income country, where water utilities are unable to recover even the service costs of operations and minor maintenance through user charges, there is a need to determine ways and means to be able to maintain a cost-effective service to consumers. For such a capital intensive service these ways have to include not only the introduction of efficiency measures but also the long-term planning of capital maintenance, that is the maintenance of the fixed assets upon which services depend. Water utilities in high-income countries have been using various fixed asset management techniques to improve asset operational efficiency, to plan capital maintenance and to demonstrate their ability to maintain and improve service to their customers. This study explores the viability of the application of asset management techniques and their potential contribution towards improving water service provision in urban centres in India. Following a literature review, a generic asset management model for a low-income country water utility was developed and then applied in the water utility serving Jaipur, Rajasthan to assess the viability of this adaptation. Having identified strengths and weaknesses during this fieldwork a revised model was proposed, including distinct phases of asset management/data intensity, which could be used as a generic approach in large urban centres in India. Following consultations with prospective users in six States, the study showed that it is feasible to take a first step towards asset management at low cost but this will require a change in the management approach. The study identified lack of relevant data as a key factor influencing an effective and comprehensive application of a generic asset management model. The study concludes that the proposed phased asset management models can contribute to improving serviceability for customers; however the concern that remains is the willingness of the organisation to adapt to the necessary changes

PREdictive model for DISaster response configuration (PREDIS decision platform)

This thesis was submitted for the award of Doctor of Philosophy and was awarded by Brunel University LondonThe extraordinary conditions of a disaster, require the mobilisation of all available resources, inducing the rush of humanitarian partners into the affected area. This phenomenon called the proliferation of actors, causes serious problems during the disaster response phase including the oversupply, duplicated efforts, lack of planning. The aim of this research is to provide a solution to reduce the partner proliferation problem. To that end the main research question is put forward as “How to reduce the proliferation of partners in a disaster response”? Panel analysis of the historic record of 4,252 natural onset disasters between 1980 to 2013 via regression analysis, MA and AHP gives rise to the formation of a predictive decision-making platform called PREDIS. It is capable of predicting the human impact of the disaster (fatality, injured, homeless) of up to 3% of errors and enables the decision makers to estimate the required needs for each disaster and prioritises them based on the disaster type and socio-economics of the affected country. It further renders it possible to rank and optimise the desired partners based on the decision maker’s preferences. Verification of the PREDIS through a simulation game design using a sample group of decision makers, show that this technique enables the user to decide within one hour after the disaster strike using the widely available data at the time of the disaster. It also enables non-experts to decide almost identically to experts in terms of the similarity of the choices and the speed of the decision.The lack of an extensive database for the potential humanitarian partners from which to choose, is the limitation of this research in addition to the lack of standardised set of minimum requirements for the suitable partners.The model is also as strong as its data feed which is inconsistent in various humanitarian sources