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As the penetration of renewable energy sources into a power system increases, the significance of precise short-term forecasts for wind power generation becomes paramount. However, the erratic and non-periodic nature of wind poses challenges in accurately predicting the output. This paper presents a comprehensive investigation into forecasting wind power generation for the following day, using three machine learning models: long short-term memory (LSTM), convolutional neural network-bidirectional LSTM (CNN-biLSTM), and light gradient boosting machine (LGBM). In addition, this paper proposes a method to improve the prediction performance of LGBM by separating data according to the distribution of features, and training and testing each separated dataset with a distinct model. This study includes a comparative analysis of the performance of the proposed models in predicting wind turbine output, offering valuable insights into their respective efficiencies. The results of this investigation were analyzed for two geographically distinct wind farms (Korea and the UK). The findings of this study are expected to facilitate the selection of efficient prediction models within the forecast accuracy auxiliary service market and assist grid operators in ensuring reliable power supply for the grid. [ABSTRACT FROM AUTHOR]

Given the fundamental role of renewable energy assets in achieving global temperature control targets, new energy management methods are required to efficiently match intermittent renewable generation and demand. Based on analysing various designed cases, this paper explores a number of heuristics for a smart battery scheduling algorithm that efficiently matches available power supply and demand. The core of improvement of the proposed smart battery scheduling algorithm is exploiting future knowledge, which can be realized by current state-of-the-art forecasting techniques, to effectively store and trade energy. The performance of the developed heuristic battery scheduling algorithm using forecast data of demands, generation, and energy prices is compared to a heuristic baseline algorithm, where decisions are made solely on the current state of the battery, demand, and generation. The battery scheduling algorithms are tested using real data from two large-scale smart energy trials in the UK, in addition to various types and levels of simulated uncertainty in forecasts. The results show that when using a battery to store generated energy, on average, the newly proposed algorithm outperforms the baseline algorithm, obtaining up to 20–60% more profit for the prosumer from their energy assets, in cases where the battery is optimally sized and high-quality forecasts are available. Crucially, the proposed algorithm generates greater profit than the baseline method even with large uncertainty on the forecast, showing the robustness of the proposed solution. On average, only 2–12% of profit is lost on generation and demand uncertainty compared to perfect forecasts. Furthermore, the performance of the proposed algorithm increases as the uncertainty decreases, showing great promise for the algorithm as the quality of forecasting keeps improving. [ABSTRACT FROM AUTHOR]

Renewable energy (RE) is one of the most popular public policy orientations worldwide. Compared to some other countries and continents, Europe has gained an early awareness of energy and environmental problems in general. At the theoretical level, free-market environmentalism indicates that based on the principle of private property rights, with fewer state interventionist and regulation policies, entrepreneurs, as the driving force of the market economy, can provide better services to meet the necessity of offering RE to protect the environment more effectively. Previous studies have revealed that Germany, Denmark, and the United Kingdom have made some progress in using the market to develop RE. However, this research did not analyze the three countries' RE conditions from the perspective of free-market environmentalism. Based on our review of the principles of free-market environmentalism, this paper originally provides an empirical study of how Germany, Denmark, and the United Kingdom have partly conducted free-market-oriented policies to successfully achieve their policy goal of RE since the 1990s on a practical level. In particular, compared with Germany and Denmark, the UK has maintained a relatively low energy tax rate and opted for more pro-market measures since the Hayekian-Thatcherism free-market reform of 1979. The paper also discovers that Fredrich A. Hayek's theories have strongly impacted its energy liberalization reform agenda since then. Low taxes on the energy industry and electricity have alleviated the burden on the electricity enterprises and consumers in the UK. Moreover, the empirical results above show that the energy enterprises play essential roles in providing better and more affordable RE for household and industrial users in the three sampled countries. Based on the above results, the paper also warns that state intervention policies such as taxation, state subsidies, and industrial access restrictions can impede these three countries' RE targets. Additionally, our research provides reform agendas and policy suggestions to policymakers on the importance of implementing free-market environmentalism to provide more efficient RE in the post-COVID-19 era. [ABSTRACT FROM AUTHOR]

Grid-connected photovoltaic (PV) capacity is increasing and is currently estimated to account for 3.0% of worldwide energy generation. One strategy to balance fluctuating PV power is to incentivize self-consumption by shifting certain loads. The potential improvement in the amount of self-consumption is usually estimated using smart meter and PV production data. Smart meter data are usually available only at sampling frequences far below the Nyquist limit. In this paper we investigate how this insufficient sampling rate affects the estimated self-consumption potential of shiftable household appliances (washing machines, tumble dryers and dishwashers). We base our analyses on measured consumption data from 16 households in the UK and corresponding PV data. We found that the simulated results have a marked dependence on the data sampling rate. The amount of self-consumed energy estimated with data sampled every 10 min was overestimated by 30–40% compared to estimations using data with 1 min sampling rate. We therefore recommend to take this factor into account when making predictions on the impact of appliance load shifting on the rate of self-consumption. [ABSTRACT FROM AUTHOR]

Renewable energy sources (RES) are gradually becoming one of the key elements in the process of achieving energy efficiency worldwide. This trend can be observed in many developed Western economies—for example, in the United States, as well as in the United Kingdom. Hence, the role of innovative policies for promoting energy efficiency is becoming crucial in transition to the post-carbon economy. The shift to the carbon-free future make all actors to face forgoing commitments Nevertheless, customers and residential households are the first and the most important players in the pursuit of the energy-efficient future. Without them, carbon-free economy based on RES would never take the shape as envisaged. Our paper focuses on the innovative strategies and policies studying the effect and the scope of RES penetration into the households. We employ and empirical analysis of the effects from using RES in households using an example of the residential households in the northwest region of the United Kingdom (UK) with and without solar photovoltaic (PV) panels and electric vehicles (EV). We analyse the four scenarios that are aimed at analysing the system dynamics and providing differentiation between systems in terms of the varying values of the gross demand, tariffs, metered import, and the total revenue. Our results demonstrate that the solar PV leads to the transfer of costs and wealth regardless of the ownership of PV and EVs. Solar energy generation reduces the share of UK solar PV households per kWh costs of the distribution system which causes the augmenting of the per unit charges as well as to the changes in payments for the electricity that impoverishes less wealthy customer groups. It also becomes clear that with the increase of EV penetration, the existing energy efficiency schemes would have to be revised. [ABSTRACT FROM AUTHOR]

Ammonia is gaining popularity around the world due to its advantages as an energy vector over other zero-carbon fuels and has the potential to be a key component for deep decarbonisation of energy systems. However, little is known about public attitudes and concerns about the technology, one of the factors that will determine its successful implementation. This research examined public perception of green ammonia technologies through online surveys conducted in Mexico (n = 563) and the UK (n = 357). The results suggest that most of the participants in the two countries support the development of these technologies, with men being more likely to show support than women. Participants in Mexico and the UK had both negative and neutral associations of ammonia as a chemical, but overall perceptions of green ammonia were surprisingly positive. A multiple regression analysis indicates that support for this technology is highly dependent on the way participants perceive the benefits and the risks of implementing green ammonia as a fuel or for storage in their countries. Perceptions of green ammonia presented in this study must be interpreted cautiously because of the novelty of the concept. Therefore, additional research should be carried out to determine the positive opinion of green ammonia obtained in the research. [ABSTRACT FROM AUTHOR]