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We investigate the power system and climate policy implications of the anticipated expansion in electricity demand from datacentres. We perform a joint optimization of Generation and Transmission Expansion Planning for the island of Ireland considering uncertainty in future datacentre growth under various climate policies. Datacentre expansion imposes significant extra costs on the power system. A renewable energy target is more costly than a technology-neutral carbon reduction policy due to the restriction to invest solely in renewable energy, and the divergence in costs increases non-linearly in electricity demand. A carbon reduction policy is more robust to uncertainties in projected demand than a renewable policy. The increased demand from datacentres, which is relatively constant, is best matched by a power supply with constant output, generally fossil-fuelled power plants, potentially in combination with carbon capture and sequestration technology, than variable supply. Datacentre demand increases power system costs by 6% under a carbon reduction policy, and by 9% to 15% under renewable energy targets. However, the abatement cost of carbon under renewable targets decreases at higher levels of demand, while the opposite is true for the abatement cost under technology-neutral targets. High renewable targets crowd out other low-carbon options, namely CCS, leading to a higher cost solution for the same reduction in emissions, although this result is sensitive to increases in fossil fuel prices. The results suggest that climate-related energy policy should focus on technology-neutral carbon reduction policies. The climate impacts of datacentres on energy demand and thus supply requirements should also feature in climate and energy policy discussions. Climate policy that focuses on renewable energy only is more expensive than a technology-neutral climate policy The costs of a renewable-only focused climate policy increase non-linearly as demand increases [ABSTRACT FROM AUTHOR]

• Model which simulates dairy farm electricity use in 15-min intervals. • Validated on 3 dairy farms of varying size. • Suitable for studying renewable energy integration and demand side management. • Reliable tool for decision support for dairy operations. The objective of this paper was to define, validate and demonstrate a model capable of accurately simulating dairy farm electricity consumption across varying herd and parlour sizes, to facilitate research investigating renewable energy systems (RES) and demand side management (DSM). The Farm Electricity System Simulator (FESS) was developed using grey-box modelling techniques utilizing empirical data for parameter tuning. Empirical data were gathered from nine spring calving, pasture based dairy farms located in the Republic of Ireland. A k-means clustering analysis was conducted, separating the farms into three, near homogenous groups, from which representative farms were selected. FESS was trained using 12 months of data from three representative farms using the repeat hold out method for data partitioning with 75 % of data used for training and 25 % used for validation. An optimisation algorithm was used to minimize the error during model training. Through cross-validation, FESS achieved a root mean squared error (RMSE) of 7.65 kWh, mean absolute percentage error (MAPE) of 7.10 %, mean percentage error (MPE) of −0.86 % and a relative prediction error (RPE) of 7.56 % for total daily electricity consumption. Across the three farms, the simulated outputs of FESS achieved an average R2 value of 0.72, demonstrating good agreement with observed data. FESS's utility was demonstrated by analysing the effects of different electricity pricing structures and on-site solar photovoltaic electricity generation on total farm energy costs. We concluded that FESS simulated on-farm electricity consumption with sufficient accuracy for the intended application. FESS accurately simulated dairy farm electricity consumption across three dairy farms of different herd and parlour sizes while evaluating the effects of demand side management and renewable generation on farm electricity consumption and costs. [ABSTRACT FROM AUTHOR]

A new methodology based on expert knowledge and data mining is proposed to obtain data-driven models that characterize household consumption profiles. These profiles are useful for electricity marketers to understand their customers' consumption. They could then adjust their electricity purchases in the market and provide recommendations to their customers to manage their consumption. The novelty of this research work is proposing a new procedure to determine an adequate number of clusters for a clustering task. Therefore, the proposed new methodology includes this novel procedure to build the models in two phases. In the first phase, clustering algorithms are used to group the data using different numbers of clusters. For the second phase, a new procedure (k-ISAC_TLP) is proposed and used to select the most appropriate number of clusters. This methodology allows the inclusion of domain information. In the case of household electricity consumption, where only groups with a significant number are relevant as long as the error is small, it allows the use of metrics like the mean absolute error and the number of observations (daily electricity consumption profiles). According to experts, the results achieved in two real datasets (from Spain and Ireland), with millions of observations support the methodology and reveal novel knowledge. In both cases, two and a half million observations have been analyzed and around twenty electricity consumption profiles have been detected. The methodology is easily extensible to problems of any domain where clustering algorithms are applicable. A software solution has been implemented and made freely available. [Display omitted] • Data-mining-based methodology has been implemented as an open-source software tool. • The procedure uses expert knowledge for it to be extendible to any domain. • New procedure to automatically determine optimal number of clusters. • A minimal pre-processing of data is advantageous for domain experts. • Real cases using data electricity consumption were tested and validated by experts. [ABSTRACT FROM AUTHOR]

In the effort to achieve carbon neutrality through a decentralized electricity market, accurate short-term load forecasting at low aggregation levels has become increasingly crucial for various market participants' strategies. Accurate probabilistic forecasts at low aggregation levels can improve peer-to-peer energy sharing, demand response, and the operation of reliable distribution networks. However, these applications require not only probabilistic demand forecasts, which involve quantification of the forecast uncertainty, but also determining which consumers to include in the aggregation to meet electricity supply at the forecast lead time. While research papers have been proposed on the supply side, no similar research has been conducted on the demand side. This paper presents a method for creating a portfolio that optimally aggregates demand for a given energy demand, minimizing forecast inaccuracy of overall low-level aggregation. Using probabilistic load forecasts produced by either ARMA-GARCH models or kernel density estimation (KDE), we propose three approaches to creating a portfolio of residential households' demand: Forecast Validated, Seasonal Residual, and Seasonal Similarity. An evaluation of probabilistic load forecasts demonstrates that all three approaches enhance the accuracy of forecasts produced by random portfolios, with the Seasonal Residual approach for Korea and Ireland outperforming the others in terms of accuracy and computational efficiency. • We proposed new low-voltage aggregation techniques based on portfolio theory to improve probabilistic demand forecasting. • The increased volatility at the low voltage level requires probabilistic forecasting to measure uncertainties. • The proposed methods were found to outperform random aggregation with the real-world data from Korea and Ireland. • A simple forecasting approach minimizing the standard deviation of deseasonalized demands was accurate and efficient. • The new method can contribute to achieving carbon neutrality, enable P2P energy sharing, and benefit prosumers. [ABSTRACT FROM AUTHOR]