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Global growth in variable renewable generation has brought significant attention to the challenge of balancing electricity supply and demand. However, inter-annual variability of energy resources has only recently begun to feature in energy system assessments and receives limited recognition in policy discussion, let alone policy design. Meteorological reanalysis datasets that blend modern modelling techniques with historic weather records are seeing increased application in energy system studies. This practice offers insights for market and policy design implications as governments seek to manage the changing energy landscape, as seen with the UK's introduction of the Electricity Market Reform policy package. Here we apply a concise, Load Duration Curve based approach to consider the market and policy implications of increasing variability in the Great Britain (GB) energy system. Our findings emphasise the growing inter-annual variability in operating opportunity for residual mid-merit and even baseload generation, alongside implications for capacity assurance approaches. The growth in wind generation is seen to bring an accompanying opportunity for increased solar generation, with its lower inter-annual variability and largely uncorrelated annual characteristic. The results underscore the need for an increased recognition of inter-annual variability when addressing market design and incentive mechanisms. • Meteorological reanalysis datasets benefit energy system studies. • Load duration curve approach complements sophisticated system models. • Inter-annual variability has implications for energy and capacity (power) based markets. • Blended renewables solutions help mitigate inter-annual variability. [ABSTRACT FROM AUTHOR]

Abstract 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 non-synchronous 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. Highlights • Derived long term time series of wind, solar and demand. • Determined hourly proportion of demand from renewables for a 36-year period. • Sunny, windy weekend days have highest penetration of renewables. • Derived volume of curtailment for range of penetration limits. • Volume of curtailment varies with blend of wind and solar capacity. [ABSTRACT FROM AUTHOR]

• Assesses grid impact of EV charging under diverse tariff and control strategies. • Stepwise Time of Use tariffs cause higher peak loads than on-demand EV charging. • Smart tariffs reduce grid carbon emissions with dynamic tariffs most effective. • Diversity of tariffs should be encouraged to mitigate the risk from demand peaks. • Smart tariffs fail to avoid local EV peak loads with capacity management necessary. With the rapid increase in ownership of Electric Vehicles (EVs), widespread concern has been raised regarding the potential for EV charging demand to overload electricity grids. Smart control of charging is advocated as a solution, gaining attention from business and support from policymakers. However, the ultimate grid benefits (or disbenefits) of smart charging will follow from a combination of user behaviour and pricing arrangements / tariffs. Local clustering of vehicle uptake can lead to unintended consequences as national incentives fail to align with local pressures. In this paper, we describe a simulation of the dynamic electricity demand pattern arising from a fleet of grid connected EVs. The model developed for this study combines stochastic sampling of data from a UK-based smart charging trial (Western Power Distribution's Electric Nation project) with a set of plausible tariffs, including a strategy which specifically seeks to minimize grid carbon emissions. This provides insights into the potential impacts of EV charging by encompassing a wider range of tariffs than previously assessed, while also separating the control actions of optimising cost and managing capacity. We examine the carbon implications of tariff choice and introduce a range of grid overload metrics that reveal nuances in the tariff implications and evolution of impacts as EV penetration increases. The results show that smart charging is not necessarily a better solution for the grid compared to on-demand charging. Stepwise tariffs, currently favoured by UK energy suppliers, present a particular risk. Such tariffs can tend to increase load synchronization by shifting load towards periods where more cars are connected and awaiting charge. This can lead to an increased peak load even at moderate EV uptake levels. Dynamic tariffs proved preferable but still increase peak demand at higher vehicle uptakes. All smart tariffs offer a strong carbon benefit, but, again, current stepwise tariffs are failing to realise the full potential that could be realized by targeting low carbon time periods. Separate local capacity management was able to eliminate overload at the secondary substation, even with very high EV uptake, with only rare, very small levels of unserved demand. [ABSTRACT FROM AUTHOR]

Recently there has been a significant change in the distribution of wind farms in Great Britain with the construction of clusters of large offshore wind farms. These clusters can produce large ramping events (i.e. changes in power output) on temporal scales which are critical for managing the power system (30 min, 60 min and 4 h). This study analyses generation data from the Thames Estuary cluster in conjunction with meteorological observations to determine the magnitude and frequency of ramping events and the meteorological mechanism. Over a 4 h time window, the extreme ramping events of the Thames Estuary cluster were caused by the passage of a cyclone and associated weather fronts. On shorter time scales, the largest ramping events over 30 min and 60 min time windows are not associated with the passage of fronts. They are caused by three main meteorological mechanisms; (1) very high wind speeds associated with a cyclone causing turbine cut-out (2) gusts associated with thunderstorms and (3) organised band of convection following a front. Despite clustering offshore capacity, the addition of offshore wind farms has increased the mean separation between capacity and therefore reduced the variability in nationally aggregated generation on high frequency time scales. [ABSTRACT FROM AUTHOR]

In recent years there has been a significant change in the distribution of wind farms in Great Britain, with a trend towards very large offshore farms clustered together in zones. However, there are concerns these clusters could produce large ramping events on time scales of less than 6 h as local meteorological phenomena simultaneously impact the production of several farms. This paper presents generation data from the wind farms in the Thames Estuary (the largest cluster in the world) for 2014 and quantifies the high frequency power ramps. Based on a case study of a ramping event which occurred on 3rd November 2014, we show that due to the large capacity of the cluster, a localised ramp can have a significant impact on the cost of balancing the power system on a national level if it is not captured by the forecast of the system operator. The planned construction of larger offshore wind zones will exacerbate this problem. Consequently, there is a need for accurate regional wind power forecasts to minimise the costs of managing the system. This study shows that state-of-the-art high resolution forecast models have capacity to provide valuable information to mitigate this impact. [ABSTRACT FROM AUTHOR]