Sub-agent elements for control methods in multi-agent energy management system

Increased penetration of generation and decentralised control are considered to be feasible and effective solution for reducing cost and emissions and hence efficiency associated with power generation and distribution. Distributed generation in combination with the multi-agent technology are perfect candidates for this solution. Pro-active and autonomous nature of multi-agent systems can provide an effective platform for decentralised control whilst improving reliability and flexibility of the grid

Role of household activities in peak electricity demand and distributional effects of Time-of-Use tariffs

Introduction of Time-of-Use (ToU) tariffs have the potential to motivate consumers to flex their energy use and, by utilising their flexibility, support the reduction in peak electricity demand. In return, lower peak demand could also reduce the system costs due to the reduced need for peaking generation and network reinforcement. By their nature, ToU tariffs would penalise consumers with high consumption during peak periods and who are not able to exercise flexibility. Therefore to ensure the affordability of energy bills it is important to understand the relationship between the timing of activities in the household and socio-demographic properties of the consumers. This paper uses UK Time Use survey data to cluster households by their energy-related activities during the peak electricity demand periods, model the corresponding electricity demand and analyse the impact of ToU tariffs across several socio-demographic parameters. Results show that similar patterns of energy related activities exist for the clusters with different socio-demographic parameters (e.g. family structure or income). Findings also show that there is no single dominant socio-demographic parameter that defines the winners or losers from the introduction of ToU tariff

Exploratory analysis of family-related activities during peak electricity periods

Price-based interventions (such as Time of Use tariffs) are designed to shift the timing of certain everyday activities to mitigate peak electricity demand. On the one hand, it is argued that timing activities outside the peak hours would decrease the demand, easing the stress on the grid. On the other hand, recent literature suggests that householders are more likely to ignore timing of activities - due to convenience or due to activities considered 'non-negotiable' during peak hours. One way to address this conundrum is to investigate how family-related activities during the peak times hang together and the extent to which they are performed together at a specific time of the day. The starting point of this research is that working hours and school times shape the dynamics of peak demand, leaving less time for families to do more during these time periods and also making it difficult to shift activities to other times of the day. We aim to explore the timing and sequences of activities, comparing how they vary at different temporal scales (e.g. workdays vis-à-vis school holidays). In conclusion, we argue that any effective shifting of family-related activities will need to look beyond the meter (such as de-synchronized effects of school holidays), potentially collecting information regarding both energy and non-energy data in order to understand the connection, coordination and organization between activities which constitute electricity demand

Control of Energy Storage

In the attempt to tackle the issue of climate change, governments across the world have agreed to set global carbon reduction targets. [...

Conclusions from the DEePRED project – distributional impacts of flexible electricity tariffs

Whilst flexible electricity tariffs, such as Time-of-Use (ToU) and real-time, play an important role in motivating the shift of electricity demand away from the peak period and progressing towards the Net Zero, their widespread implementation for residential customers may have an adverse effect on some groups of consumers. The objective of research work within the DEePRED (Distributional Effects of dynamic Pricing for Responsive Electricity Demand) project is to evaluate the distributional impact of flexible tariffs and identify groups of consumers who might be advantaged or disadvantaged based on by their socio-demographic parameters. As the project comes to its conclusion, this paper presents key findings, appraises the impact of ToU on smart meter data and explores the application of such findings in the context of transition to Net Zero. The key findings are as follows: (i) bottom-up(clustering) impact analysis method clearly identifies the most affected household groups; (ii) there is no clear set of socio-demographic parameters that can describe these groups; (iii) grouping by household composition demonstrates that the presence of children increases the probability and intensity of energy-related activities at peak-time and hence increases the likelihood of adverse impact of ToU; and (iv) the impact on consumer groups who do not change their behaviour in response to ToU is defined by the peak to off-peak price ratio, which is confirmed by analysis of ToU impact on smart meter profiles

It’s only a matter of time: flexibility, activities and time of use tariffs in the United Kingdom

In current academic, policy and industry debates there is significant emphasis on the importance of enhancing the level of flexibility of electricity demand. Flexibility is considered critical in order to improve balancing with renewables, reduce costs of electricity generation and make the most of smart systems and battery storage. There remain questions around how flexibility is delivered, and which portions of demand will take part in different aspects of flexibility markets. The aim of the paper is to identify activities in the home for which people may either gain or lose following the introduction of Time of Use (ToU) tariffs. It uses 2014–2015 UK Time Use Survey data to cluster households in terms of similarities in activities at peak time and identify households differently affected by ToU tariffs across several socio-demographic parameters (i.e. work status, income, family structure). Findings show that sociodemographic distribution did not demonstrate any significant dominant parameter. Instead, clustering based on similarities in the timing of activities has provided distinctive patterns and can shed light on groups of people who might be either advantaged or disadvantaged from the introduction of ToU tariffs

On-line adjustment of battery schedules for supporting LV distribution network operation

Increasing domestic demand for electric energy is expected to put significant strain on the existing power distribution networks. In order to delay or prevent costly network reinforcement, some UK Distribution Network Operators (DNOs) are investigating the use of Battery Energy Storage Solutions (BESS), or other demand response systems, in the Low-Voltage (LV) power distribution networks to reduce peak demand. In most cases the control strategies, and metrics of success, are evaluated on a half-hourly basis and so sub-half-hourly (i.e. minute by minute) variations in demand are not effectively addressed. In this work, a closed-loop optimisation methodology is proposed that adjusts the pre-scheduled charging profile of a BESS in a sub-half-hourly manner in order to improve network operation whilst maintain the same average net energy flow over the half-hour period. This new approach guarantees that the BESS follows its predetermined half-hourly schedule, yet voltage and power imbalance, network losses, and feeder overloading are additionally mitigated through sub-half-hourly control actions. For validation, this paper presents a case study based on the real BESS installed in Bracknell as part of Thames Valley Vision project with Scottish and Southern Energy Power Distribution (SSE-PD) evaluated on the IEEE LV test case feeder model

Exploring socioeconomic and temporal characteristics of British and German residential energy demand

The British and German residential sectors account for similar fractions of national energy demand and carbon emissions. They also exhibit underlying differences in the building stock, fuel split, tenure and household load profiles. The temporal habits in British and German households are also quite different, which is challenging to measure due to the paucity of German smart meter data. This contribution takes this background as a starting point to explore some of the temporal and socioeconomic characteristics of residential energy demand in Britain and Germany. The Centre for Renewable Energy Systems Technology (CREST) residential load profile generator is updated for the UK and extended to the German context and validated with standard load profiles, providing high levels of accuracy according standard normalized root-mean-squared error (NRMSE) measures. The paper then analyzes the energy-related activities of different socioeconomic household groups based on with National Time Use Survey data from both countries. The analysis showed some clear differences between groups and countries, which are a reminder of the importance of non-energy policy (e.g. school hours) in determining peaks. As well as encountering useful insights into international differences in energy related behaviour, the results showed some key differences within specific socioeconomic groups, such as single persons, families with children, and pensioners. Further work will focus on extending the German CREST model to include a German appliance stock, as well as allocating these appliances according to households’ socioeconomic characteristics. The definition of the groups themselves needs to be refined, perhaps to include multiple variables and based on clustering or similar techniques, and validation with smart meter data

Online control algorithm for sub-half-hourly operation of LV-connected energy storage device owned by DNO

Change in consumer behaviour through uptake of Low Carbon Technologies is likely to put existing distribution networks under strain and worsen the operational requirements of the network. Deployment of energy storage and power electronics is a feasible alternative to traditional network reinforcement. This paper presents two control algorithms used with an energy storage device deployed as part of New Thames Valley Vision Project. The two algorithms are aimed at 1) equalising phase loading with correction of power factor and 2) providing voltage support with Additive Increase Multiplicative Decrease algorithm for active and reactive power control