Optimising energy demands for new housing development in Cambridge – Milton Keynes – Oxford Arc

The Oxford – Milton Keynes – Cambridge (OMC) arc is one of the fastest growing regions of the United Kingdom. The connection of OMC cities via new infrastructure services is seen vital for long-term economic growth of the arc. This growth is expected to increase the arc’s population by 1.9 million and create 23,000 new jobs by 2050. With world-class universities, research locations and high-tech firms, the arc’s future economic growth is threatened by the absence of affordable housing and appropriate connective infrastructures. Since residential and commercial buildings account for half of UK energy use, it is important to plan new housing development in a smart way by including low carbon technologies so as to reduce demands for energy. Therefore, this thesis studies the relationship between the urban development and energy and investigates the potential of low carbon technologies and associated grid impacts for the arc’s new housing development. The study considers PV panels with storage systems such as lithium nickel-cobalt-aluminium and lead-acid batteries as low carbon technologies and analyses their potential to reduce demand for energy from new housing development. Additionally, the growing use of electrical vehicles (EVs) and their impact on the grid has also been included in the investigation. The study calculates and compares the energy demand for the new housing development with and without the low carbon technologies under alternative scenarios which has been characterised as ‘degree of smartness’. The results show that installing PV panels coupled with energy storage systems reduce the dwellings’ demand from the grid as well as it is economically advantageous. Particular considerations about smart EV charging along with load shifting of appliances are highlighted to reduce the number of PV panels and the size of batteries to be installed.Outgoin

Activity-Based Recommendations for Demand Response in Smart Sustainable Buildings

The energy consumption of private households amounts to approximately 30% of the total global energy consumption, causing a large share of the CO2 emissions through energy production. An intelligent demand response via load shifting increases the energy efficiency of residential buildings by nudging residents to change their energy consumption behavior. This paper introduces an activity prediction-based framework for the utility-based context-aware multi-agent recommendation system that generates an activity shifting schedule for a 24-hour time horizon to either focus on CO2 emissions or energy cost savings. In particular, we design and implement an Activity Agent that uses hourly energy consumption data. It does not require further sensorial data or activity labels which reduces implementation costs and the need for extensive user input. Moreover, the system enhances the utility option of saving energy costs by saving CO2 emissions and provides the possibility to focus on both dimensions. The empirical results show that while setting the focus on CO2 emissions savings, the system provides an average of 12% of emissions savings and 7% of cost savings. When focusing on energy cost savings, 20% of energy costs and 6% of emissions savings are possible for the studied households in case of accepting all recommendations. Recommending an activity schedule, the system uses the same terms residents describe their domestic life. Therefore, recommendations can be more easily integrated into daily life supporting the acceptance of the system in a long-term perspective

Integrated decarbonisation strategies for the electricity, heat, and transport sectors

The rapid climate change experienced at the beginning of the twenty-first century is intimately entwined with the increase in anthropogenic greenhouse gas (GHG) emissions resulting from the growth of fossil fuel consumption in all energy sectors. By 2050, not only these energy sectors must eliminate GHG emissions: electricity, heat, transport, but also those sectors should be closely coupled to achieve maximum synergy effects and efficiency. In this context, this thesis develops integrated models to assess decarbonisation strategies for a variety of complex energy system transitions, including the electricity, heat and transport sectors. Firstly, the thesis proposes a novel single-year, integrated electricity, heat and transport sectors model that considers integrating the hydrogen supply chain while optimising the system’s investment and operation costs and covers both local and national levels. A series of studies are then carried out to evaluate different integrated decarbonisation strategies for the future low-carbon energy system based on the single-year integrated multi-energy optimisation model. Secondly, this thesis evaluates the economic performance and system implications of different road-transport decarbonisation strategies and analyses the electricity sector decarbonisation synergy. Great Britain (GB) case study suggests that transport electrification should be carried out with smart charging to reduce the additional cost on the electricity sector expansion. Hydrogen fuel cell vehicle (HFCV) can be combined with electric vehicle (EV) to reduce the system of increased peak demand due to road transport’s electrification. However, when EV enables smart charging, the case for HFCV becomes less compelling from a system perspective. Their penetration is limited by their higher capital costs and lower efficiency compared to EV. The results also clearly demonstrate a synergy between the hydrogen used in the electricity and transport sector. The integration of hydrogen-fuelled generation can reduce the overall system cost by enabling more investment in renewable energy and reduce the need for the firm but high-cost low-carbon generation technologies, particularly nuclear and gas with carbon capture and storage (CCS). The integration of power-to-gas (P2G) facilities can increase the integration of wind power capacity. Additionally, the heat sector’s decarbonisation is one of the key challenges in achieving the net-zero target by 2050. This thesis evaluates the integrated decarbonisation strategies for the electricity, heat and transport sectors involving hydrogen integration. A study compares the economic advantages under the deployments of P2G hydrogen production and gas-to-gas (G2G) hydrogen production and the associated implications for overall system planning and operation. The results demonstrate that hydrogen integration through the G2G process brings more economic benefits than the P2G process; combining P2G with G2G can yield further cost savings. The results also clearly show the changes in the electricity side driven by the different hydrogen integration strategies. The integration of hydrogen will promote hydrogen boiler (HB) deployment, which will dominate the heating market, combined with the heat pump (HP). From the perspective of the transport sector, the development of HFCV is positively related to the integration cost of the hydrogen system, especially in the demanding carbon scenario. Going further, the single-year, multi-energy integrated optimisation model has limitations, focusing only on short-term investment operations and unable to deal with the long-term system planning problem. Therefore, this thesis presents a novel transition model for the electricity, heat and transport sectors, operating in full hourly resolution and taking into account sectoral coupling, simulating future energy systems’ transition to low-carbon energy production. Finally, considering the different difficulties and speeds of transition in the different energy sectors and the complementary effects between energy sectors, designing individual sector transition cannot provide a systematic view, as the most valuable sector coupling effects are overlooked, and sector separation consideration underestimates the complexity of the optimal transition pathway. This thesis designs three integrated energy system transition pathways based on the multi-year transition model, placing sector coupling and considering a full range of low-carbon technologies, enabling fundamental insights into the optimal energy system transition pathway to achieve the net-zero target by 2050. The GB case study results demonstrate that electrification combined with hydrogen integration will be the most cost-effective pathway. Hybrid heating technologies and EV will be the leading options in the heat and transport sector for decarbonisation. Bioenergy will play an essential role to offset carbon emissions from the other energy sectors. Cross-energy flexibility is vital to achieving a cost-effective transition pathway. Based on the above results, the policy recommendations for the net-zero target achieving can be made for policymakers.Open Acces

Demand response in low-carbon power systems: a review of residential electrical demand response projects

The transition to a future low-carbon power system will increase the need for and value of demand response – where demand can be curtailed or shifted in time according to the network’s requirements. The electricity supply industry is investing heavily in ‘smart’ technologies, partly based on the assumption that demand response will be available when it is needed, yet this is an unfamiliar concept to most consumers, who still view electricity as a resource that can be consumed as and when they want it. That such a gap exists between the reality on the ground and the requirements of the future is a cause for concern, yet the methods proposed today to achieve demand response are based predominantly on assumptions that people will accept and respond to variations in the price of electricity. There is however growing evidence that the ‘people are economic actors’ approach is inadequate when dealing with the complexities of energy-use within the home. This paper reviews existing residential demand response projects, and supports the growing realisation that the principal challenge in demand response is no longer the technology itself but rather its acceptance and use by the consumer. In order to deal with this challenge, a more holistic approach to demand response is needed, one that can better deal with both the ‘hard’ and ‘soft’ sides of the system

Assessing the benefits of demand-side flexibility in residential and transport sectors from an integrated energy systems perspective

Demand-side flexibility from smart appliances and passenger electric vehicles has been increasingly regarded in recent years as an effective measure to reduce peak loads and to aid system balancing. While numerous studies have been undertaken to investigate the benefits of demand-side flexibility, most have either focused only on the power sector or provided a snapshot for a future year or day. The influence of interactions between sectors in the long-term under energy transition pathways has therefore been under explored. This paper presents a novel modelling approach in a whole energy systems model, UK TIMES, to investigate the benefits of demand-side flexibility from smart appliances and passenger electric vehicles, including the reduction in the costs of moving to a low carbon economy. This analysis shows that demand-side control increases system flexibility, enabling the integration of high levels of low carbon power, such as nuclear and wind, whilst reducing the requirements for storage. By 2050, the peak load is reduced by around 7 GW (9%), and cumulatively about 30.9 billion GBP saved with the help of this demand-side flexibility. This approach could be integrated into other energy systems models to improve the representation of this important flexibility mechanism

Forecast-based Energy Management Systems

The high integration of distributed energy resources into the domestic level has led to an increase in the number of consumers becoming prosumers (producer + customer), which creates several challenges for network operators, such as controlling renewable energy sources over-generation. Recently, self-consumption as a new approach is encouraged by several countries to reduce the dependency on the national grid. This work presents two different Energy Management System (EMS) algorithms for a domestic Photovoltaic (PV) system: (a) real-time Fuzzy Logic-based EMS (FL-EMS) and (b) day-ahead Mixed Integer Linear Programming-based EMS (MILP-EMS). Both methods are tested using the data from the Active Office Building (AOB) located in Swansea University, Bay Campus, UK, as a case study to demonstrate the developed EMSs. AOB comprises a PV system and a Li-ion Battery Storage System (BSS) connected to the grid. The MILP-EMS is used to develop a Community Energy Management System (CEMS) to facilitate local energy exchange. CEMS is tested using the data from six houses located in London, UK, to form a community. Each household comprises a PV system and BSS connected to the grid. It is assumed that all six households use an EV and are equipped with a bidirectional charger to facilitate the Vehicle to House (V2H) mode. In addition, two shiftable appliances are considered to shift the demand to the times when PV generation is maximum to maximise community local consumption. MATLAB software is used to code the proposed systems. The FL-EMS exploits day-ahead energy forecast (assumed it is available from a third party) to control the BSS with the aim of reducing the net energy exchange with the grid by enhancing PV self-consumption. The FL-EMS determines the optimal settings for the BSS, taking into consideration the BSS's state of health to maximise its lifetime. The results are compared with recently published works to demonstrate the effectiveness of the proposed method. The proposed FL-EMS saves 18% on total energy costs in six months compared to a similar system that utilises a day-ahead energy forecast. In addition, the method shows a considerable reduction in the net energy exchanged between the AOB and the grid. The main objective of the MILP-EMS is to reduce the net energy exchange with the grid by including a two days-ahead energy forecast in the optimisation process. The proposed method reduces the total operating costs (energy cost + BSS degradation cost) by up to 35% over six months and reduces net energy exchanged with the grid compared to similar energy optimisation technique. The proposed cost function in MILP-EMS shows that it can outperform the performance of alternative cost function that directly reduce the net energy exchange. CEMS uses two days-ahead energy forecast to reduce the net energy exchange with the grid by coordinating the distributed BSSs. The proposed CEMS reduces the total operating costs (energy costs + BSSs degradation costs) of the community by 7.6% when compared to the six houses being operated individually. In addition, the proposed CEMS enhances community self-consumption by reducing the net energy exchange with the grid by 25.3% over four months compared to similar community energy optimisation technique. A further reduction in operating costs is achieved using V2H mode and including shiftable appliances. Results show that introducing the V2H mode reduces both the total operating costs of the community and the net energy exchange with the grid

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

Benefits of smart control of hybrid heat pumps: an analysis of field trial data

Smart hybrid heat pumps have the capability to perform smart switching between electricity and gas by employing a fully-optimized control technology with predictive demand-side management to automatically use the most cost-effective heating mode across time. This enables a mechanism for delivering flexible demand-side response in a domestic setting. This paper conducts a comprehensive analysis of the fine-grained data collected during the world’s first sizable field trial of smart hybrid heat pumps to present the benefits of the smart control technology. More specifically, a novel flexibility quantification framework is proposed to estimate the capability of heat pump demand shifting based on preheating. Within the proposed framework, accurate estimation of baseline heat demand during the days with interventions is fundamentally critical for understanding the effectiveness of smart control. Furthermore, diversity of heat pump demand is quantified across different numbers of households as an important input into electricity distribution network planning. Finally, the observed values of the Coefficient of Performance (COP) have been analyzed to demonstrate that the smart control can optimize the heat pump operation while taking into account a variety of parameters including the heat pump output water temperature, therefore delivering higher average COP values by maximizing the operating efficiency of the heat pump. Finally, the results of the whole-system assessment of smart hybrid heat pumps demonstrate that the system value of smart control is between 2.1 and 5.3 £ bn/year