• Energy Research

The outbreak of SARS-COV-2 disease 2019 (COVID-19) abruptly changed the patterns in electricity consumption, challenging the system operations of forecasting and balancing supply and demand. This is due to the mitigation measures that include lockdown and Work from Home (WFH), which decreased the aggregated demand and remarkably altered its profile. Here, we characterise these changes with various quantitative markers and compare it with pre-COVID-19 business-as-usual data using Great Britain (GB) as a case study. The ripple effects on the generation portfolio, system frequency, forecasting accuracy and imbalance pricing are also analysed. An energy data extraction and pre-processing pipeline that can be used in a variety of similar studies is also presented. Analysis of the GB demand data during the March 2020 lockdown indicates that a shift to WFH will result to a net benefit for flexible stakeholders, such as consumer on variable tariffs. Furthermore, the analysis illustrates a need for faster and more frequent balancing actions, as a result of the increased share of renewable energy in the generation mix. This new equilibrium of energy demand and supply will require a redesign of the existing balancing mechanisms as well as the longer-term power system planning strategies.

The outbreak of SARS-COV-2 disease 2019 (COVID-19) abruptly changed the patterns in electricity consumption, challenging the system operations of forecasting and balancing supply and demand. This is due to the mitigation measures that include lockdown and Work from Home (WFH), which decreased the aggregated demand and remarkably altered its profile. Here, we characterise these changes with various quantitative markers and compare it with pre-COVID-19 business-as-usual data using Great Britain (GB) as a case study. The ripple effects on the generation portfolio, system frequency, forecasting accuracy and imbalance pricing are also analysed. An energy data extraction and pre-processing pipeline that can be used in a variety of similar studies is also presented. Analysis of the GB demand data during the March 2020 lockdown indicates that a shift to WFH will result to a net benefit for flexible stakeholders, such as consumer on variable tariffs. Furthermore, the analysis illustrates a need for faster and more frequent balancing actions, as a result of the increased share of renewable energy in the generation mix. This new equilibrium of energy demand and supply will require a redesign of the existing balancing mechanisms as well as the longer-term power system planning strategies.

More effective energy production requires a greater penetration of storage technologies. This paper takes a looks at and compares the landscape of energy storage devices. Solutions across four categories of storage, namely: mechanical, chemical, electromagnetic and thermal storage are compared on the basis of energy/power density, specific energy/power, efficiency, lifespan, cycle life, self-discharge rates, capital energy/power costs, scale, application, technical maturity as well as environmental impact. It’s noted that virtually every storage technology is seeing improvements. This paper provides an overview of some of the problems with existing storage systems and identifies some key technologies that hold promise.

More effective energy production requires a greater penetration of storage technologies. This paper takes a looks at and compares the landscape of energy storage devices. Solutions across four categories of storage, namely: mechanical, chemical, electromagnetic and thermal storage are compared on the basis of energy/power density, specific energy/power, efficiency, lifespan, cycle life, self-discharge rates, capital energy/power costs, scale, application, technical maturity as well as environmental impact. It’s noted that virtually every storage technology is seeing improvements. This paper provides an overview of some of the problems with existing storage systems and identifies some key technologies that hold promise.

This paper presents a Markov chain approach for developing low-voltage (LV) residential load models for use in the analysis of future smart grids. The motivation is to obtain a better understanding of load use and user behaviour at the LV level which will allow future electricity network performance to be improved by understanding the impact of energy demand transformations through the application of demand-side management (DSM) strategies at the LV side. The developed load models highlight the variations between individual and aggregate load model representation and show that the composition of LV residential users should be carefully considered when analysing LV networks.

This paper presents a Markov chain approach for developing low-voltage (LV) residential load models for use in the analysis of future smart grids. The motivation is to obtain a better understanding of load use and user behaviour at the LV level which will allow future electricity network performance to be improved by understanding the impact of energy demand transformations through the application of demand-side management (DSM) strategies at the LV side. The developed load models highlight the variations between individual and aggregate load model representation and show that the composition of LV residential users should be carefully considered when analysing LV networks.

Domestic heating has a large share in the UK total energy consumption and significant contribution to the greenhouse gas emissions since it is mainly fulfilled by fossil fuels. Therefore, decarbonising the heating system is essential and an option to achieve this is by heating system electrification through heat pumps (HP) installation in combination with renewable power generation. A potential increase in performance and flexibility can be achieved by pairing HP with thermal energy storage (TES), which allows the shifting of heat demand to off peak periods or periods with surplus renewable electricity. We present a design and operational optimisation model which is able to assess the performance of HP–TES relative to conventional heating systems. The optimisation is performed on a synthetic heat demand model which requires only the annual heat demand, temperature and occupancy profiles. The results show that the equipment and operational cost of a HP system without TES are significantly higher than for a conventional system. However, the integration of TES and time-of-use tariffs reduce the operational cost of the HP systems and in combination with the Renewable Heating Incentive make the HP systems cost competitive with conventional systems. The presented demand model and optimisation procedure will enable the design of low carbon district heating systems which integrate the heating system with the variable renewable electricity supply.

Domestic heating has a large share in the UK total energy consumption and significant contribution to the greenhouse gas emissions since it is mainly fulfilled by fossil fuels. Therefore, decarbonising the heating system is essential and an option to achieve this is by heating system electrification through heat pumps (HP) installation in combination with renewable power generation. A potential increase in performance and flexibility can be achieved by pairing HP with thermal energy storage (TES), which allows the shifting of heat demand to off peak periods or periods with surplus renewable electricity. We present a design and operational optimisation model which is able to assess the performance of HP–TES relative to conventional heating systems. The optimisation is performed on a synthetic heat demand model which requires only the annual heat demand, temperature and occupancy profiles. The results show that the equipment and operational cost of a HP system without TES are significantly higher than for a conventional system. However, the integration of TES and time-of-use tariffs reduce the operational cost of the HP systems and in combination with the Renewable Heating Incentive make the HP systems cost competitive with conventional systems. The presented demand model and optimisation procedure will enable the design of low carbon district heating systems which integrate the heating system with the variable renewable electricity supply.