Sunny windy Sundays

Rapid expansion of wind and solar capacity in Great Britain presents challenges for managing electricity systems. One concern is the reduction in system inertia during periods where renewables provide a high proportion of demand which has led to some networks imposing system nonsynchronous penetration limits. However, given the lack of operational data, the relationship between renewable generation and demand for the full range of meteorological conditions experienced in Great Britain is poorly understood. This study uses reanalysis datasets to determine the proportion of demand from renewable generation on an hourly resolution for a 36-year period. The days with highest penetration of renewables tend to be sunny, windy weekend days between May and September, when there is a significant contribution of both wind and solar generation and demand is suppressed due to human behaviour. Based on the current distribution of wind and solar capacity, there is very little curtailment for all system non-synchronous penetration limits considered. However, as installed capacity of renewables grows the volume of generation curtailed also increases with a disproportionate volume occurring at weekends. The total volume of curtailment is highly dependent on ratio of wind and solar capacity, with the current blend close to the optimum level

The value of stochastic programming in day-ahead and intra-day generation unit commitment

The recent expansion of renewable energy supplies has prompted the development of a variety of efficient stochastic optimization models and solution techniques for hydro-thermal scheduling. However, little has been published about the added value of stochastic models over deterministic ones. In the context of day-ahead and intra-day unit commitment under wind uncertainty, we compare two-stage and multi-stage stochastic models to deterministic ones and quantify their added value. We present a modification of the WILMAR scenario generation technique designed to match the properties of the errors in our wind forecasts, and show that this is needed to make the stochastic approach worthwhile. Our evaluation is done in a rolling horizon fashion over the course of two years, using a 2020 central scheduling model based on the British power system, with transmission constraints and a detailed model of pump storage operation and system-wide reserve and response provision. We show that in day-ahead scheduling the stochastic approach saves 0.3% of generation costs compared to the best deterministic approach, but the savings are less in intra-day scheduling

Improving the feasibility of household and community energy storage : a techno-enviro-economic study for the UK

Rooftop photovoltaics (PV) have become widely adopted by domestic customers in tandem with energy storage systems to generate clean energy and limit import from the grid, however most applications struggle to achieve profitability. The level at which energy storage is deployed, be it household energy storage (HES), or as a community energy storage (CES) system, can potentially increase the economic feasibility. Furthermore, the introduction of a Time-of-Use (TOU) tariff enables households to further reduce their energy costs through demand side management (DSM). Here we investigate and compare the performance of HES and CES with DSM. The results suggest that TOU tariffs can effectively shave peak demand by up to 30% and lower energy bills by at least 20%, but do not improve self-consumption or selfsufficiency rate. This study indicates that all cases considered are environmentally friendly and can pay back the total CO2 emissions associated with the manufacturing within 8 years. However, the levelised cost of storage (LCOS) is still beyond a household’s affordability, ranging from £0.4 to £2.03 kWh-1, though CES is proven more effective at improving self-consumption for consumers and shaving peak demand for network operators. The feasibility can be improved by 1) combining different services and tariffs to obtain more revenues for households; 2) more legislative and financial support to reduce system costs; and 3) more innovative business models and policies to optimise revenues with existing resourc

An estimate of the value of lost load for Ireland

This paper estimates the value of short term lost load in the all island electricity market, which includes the Republic of Ireland and Northern Ireland. The value of lost load (VoLL) is the average willingness of electricity consumers to pay to avoid an additional period without power. VoLL is also known as the value of security of electricity supply and is inferred using a production function approach. Detailed electricity use data for the Republic of Ireland allows us to estimate the value of lost load by time of day, time of week and type of user. We find that the value of lost load is highest in the residential sector in both the Republic of Ireland and Northern Ireland. Our results can be used to advise policy decisions in the case of supply outages and to encourage optimum supply security. In the context of this study short term is taken to be a matter of hours rather than days or weeks

Optimisation of costs and carbon savings in relation to the economic dispatch problem as associated with power system operation

In this paper, the costs and carbon savings in the economic dispatch (ED) problem of the power system operation are optimised. Energy demands and generation are forecast and assimilated using ensemble Kalman filter (EnKF). Optimisation is performed using the ensemble-based closed-loop production optimisation scheme (EnOpt). The real energy parameters of thermal units with green generators (wind farm) are used to test the methodology. The ability of the EnKF to predict, and the robustness of the EnOpt to optimise costs and the resultant carbon emissions are demonstrated. The proposed approach addresses the complexity and diversity of the power system and may be implemented in operational conditions of energy suppliers