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Abstract While it is generally accepted that our fossil fuel-dominated energy systems must undergo a sustainable transition, researchers have often neglected the potential impacts of this on water and land systems. However, if unintended environmental impacts from this process are to be avoided, understanding its implications for land use and water demand is of crucial importance. Moreover, developed countries may induce environmental stress beyond their own borders, for instance through extensive imports of bioenergy. In this paper, Germany serves as an example of a developed country with ambitious energy transformation targets. Results show that in particular, the politically-driven aspiration for more organic farming in Germany results in a higher import quota of biomass, especially biofuels. These imports translate into land demand, which will exceed the area available in Germany for bioenergy by a factor of 3–6.5 by 2050. As this will likely bring about land stress in the respective exporting countries, this effect of the German energy transformation ought to be limited as much as possible. In contrast, domestic water demand for the German energy system is expected to decrease by over 80% through 2050 due to declining numbers of fossil-fuelled power plants. However, possible future irrigation needs for bioenergy may reduce or even counterbalance this decreasing effect. In addition, energy policy targets specific to the transport sector show a high sensitivity to biomass imports. In particular, the sector-specific target for greenhouse gas reductions will seemingly promote biomass imports, leading to the above-described challenges in the pursuit of sustainability.

Abstract While it is generally accepted that our fossil fuel-dominated energy systems must undergo a sustainable transition, researchers have often neglected the potential impacts of this on water and land systems. However, if unintended environmental impacts from this process are to be avoided, understanding its implications for land use and water demand is of crucial importance. Moreover, developed countries may induce environmental stress beyond their own borders, for instance through extensive imports of bioenergy. In this paper, Germany serves as an example of a developed country with ambitious energy transformation targets. Results show that in particular, the politically-driven aspiration for more organic farming in Germany results in a higher import quota of biomass, especially biofuels. These imports translate into land demand, which will exceed the area available in Germany for bioenergy by a factor of 3–6.5 by 2050. As this will likely bring about land stress in the respective exporting countries, this effect of the German energy transformation ought to be limited as much as possible. In contrast, domestic water demand for the German energy system is expected to decrease by over 80% through 2050 due to declining numbers of fossil-fuelled power plants. However, possible future irrigation needs for bioenergy may reduce or even counterbalance this decreasing effect. In addition, energy policy targets specific to the transport sector show a high sensitivity to biomass imports. In particular, the sector-specific target for greenhouse gas reductions will seemingly promote biomass imports, leading to the above-described challenges in the pursuit of sustainability.

Electricity load forecasting is a key aspect for power producers to maximize their economic efficiency in deregulated markets. So far, many solutions have been employed to forecast the consumption load in power grids. However, most of these methods have suffered in modeling the time-series state of data and removing noise from real-world data. Thus, the forecasting results in most cases did not have acceptable accuracy due to the mentioned problems. In this paper, in order to short-term electricity load forecast in Tabriz, Iran, a hybrid technique based on deep learning applications called Variational Autoencoder Bidirectional Long Short-Term Memory (VAEBiLSTM) is presented. Pre-processing, noise cancellation, and time-series state modeling of the data are prominent features of the developed load forecasting model. In addition, in order to prevent overfitting problems in the process of training large amounts of data, the training process is developed in the form of batch training. Load forecasting is done using meteorological and environmental data of Tabriz city as well as historical information and days of the week as input variables. In the hybrid method structure, the Variational Autoencoders are applied to the data for data preprocessing and reconstruction. Then, the normalized, noise-free data is utilized as a dataset for training the Bidirectional Long Short-Term Memory (BiLSTM) network. The proposed training method for BiLSTM is based on batch training. To present the effectiveness of the proposed technique in a comparative approach, the conventional LSTM and Support Vector Regression (SVR) algorithms are also applied to the data. Each network is trained with input data related to the years of 2017 and 2018 to predict the electricity load of the Tabriz city separately for each of the four seasons of the 2019 year. The forecasting results obtained from each method are evaluated by different statistical performance indicators. It can be seen that the proposed model forecasts the load with the correlation coefficients (R) of 99.78%, 99.57%, 99.33%, and 99.76% for spring, summer, autumn, and winter, respectively. The presented results show that the proposed VAEBiLSTM method with the highest R values and minimum forecasting errors compared to the LSTM and SVR methods has high effectiveness and performance.

Electricity load forecasting is a key aspect for power producers to maximize their economic efficiency in deregulated markets. So far, many solutions have been employed to forecast the consumption load in power grids. However, most of these methods have suffered in modeling the time-series state of data and removing noise from real-world data. Thus, the forecasting results in most cases did not have acceptable accuracy due to the mentioned problems. In this paper, in order to short-term electricity load forecast in Tabriz, Iran, a hybrid technique based on deep learning applications called Variational Autoencoder Bidirectional Long Short-Term Memory (VAEBiLSTM) is presented. Pre-processing, noise cancellation, and time-series state modeling of the data are prominent features of the developed load forecasting model. In addition, in order to prevent overfitting problems in the process of training large amounts of data, the training process is developed in the form of batch training. Load forecasting is done using meteorological and environmental data of Tabriz city as well as historical information and days of the week as input variables. In the hybrid method structure, the Variational Autoencoders are applied to the data for data preprocessing and reconstruction. Then, the normalized, noise-free data is utilized as a dataset for training the Bidirectional Long Short-Term Memory (BiLSTM) network. The proposed training method for BiLSTM is based on batch training. To present the effectiveness of the proposed technique in a comparative approach, the conventional LSTM and Support Vector Regression (SVR) algorithms are also applied to the data. Each network is trained with input data related to the years of 2017 and 2018 to predict the electricity load of the Tabriz city separately for each of the four seasons of the 2019 year. The forecasting results obtained from each method are evaluated by different statistical performance indicators. It can be seen that the proposed model forecasts the load with the correlation coefficients (R) of 99.78%, 99.57%, 99.33%, and 99.76% for spring, summer, autumn, and winter, respectively. The presented results show that the proposed VAEBiLSTM method with the highest R values and minimum forecasting errors compared to the LSTM and SVR methods has high effectiveness and performance.

Abstract Peer-to-peer (P2P) energy sharing is a fast-emerging concept that concerns electricity exchanges between grid-connected end-users. This innovative user-centric market model incentivises the creation of more distributed, collaborative, and democratised energy networks compared to traditional ones. Current research on P2P energy sharing has essentially focused on the definition of its techno-economic attributes, hence sound research on end-user engagement in this context is still lacking in the scientific literature. This paper addresses this knowledge gap by proposing a novel end-user engagement framework constructed around the P2P energy sharing context that was trialled in 3 different pilots in Portugal. These pilots represent the first Portuguese testing grounds where this concept was demonstrated under real market conditions. Findings based on a sample of 123 participants suggest that the proposed framework was effective in raising awareness and empowering unmotivated, passive end-users in an initial phase of the project implementation, as well as in retaining the interest of motivated end-users during a later phase. Furthermore, the empirical analysis allowed to conclude that participation in the project was predominantly voluntary rather than coerced. The proposed end-user engagement framework contributes to the scientific literature since it facilitates and guides the design of future P2P energy sharing initiatives.

Abstract Peer-to-peer (P2P) energy sharing is a fast-emerging concept that concerns electricity exchanges between grid-connected end-users. This innovative user-centric market model incentivises the creation of more distributed, collaborative, and democratised energy networks compared to traditional ones. Current research on P2P energy sharing has essentially focused on the definition of its techno-economic attributes, hence sound research on end-user engagement in this context is still lacking in the scientific literature. This paper addresses this knowledge gap by proposing a novel end-user engagement framework constructed around the P2P energy sharing context that was trialled in 3 different pilots in Portugal. These pilots represent the first Portuguese testing grounds where this concept was demonstrated under real market conditions. Findings based on a sample of 123 participants suggest that the proposed framework was effective in raising awareness and empowering unmotivated, passive end-users in an initial phase of the project implementation, as well as in retaining the interest of motivated end-users during a later phase. Furthermore, the empirical analysis allowed to conclude that participation in the project was predominantly voluntary rather than coerced. The proposed end-user engagement framework contributes to the scientific literature since it facilitates and guides the design of future P2P energy sharing initiatives.

Abstract: This paper proposes a holistic definition of airport sustainability comprising social, economic, operational, and environmental dimensions. Methodologically, a composite indicator approach is applied to build the Airport Sustainability Evaluation Index (ASEI), which aims to benchmark airports' sustainability performance across the four dimensions. To justify the issue of subjectivity in composite indicator building, two different methods are used in each of the normalization, weighting, and aggregation processes. Consequently, this forms eight composite indicator building schemes. Then, a variance-based sensitivity analysis, average shift in ranking (ASR), and Cronbach's alpha test are performed to examine the sensitivity and reliability among the eight schemes. Schiphol airport is selected as a demonstration to validate the ASEI with its data from 2012 to 2021 as inputs. The results reveal a significant consensus among the eight schemes in identifying the outstanding and bottom performers across the analyzed period. Additionally, weighting is found to be the most influential composite indicator building process. Further, the scheme with the most significant contribution to the result reliability found in this paper is re-scaling as the normalization method, Benefit-of-the-doubt (BoD) as the weighting method, and Non-compensatory multi-criteria approach (NCMC) as the aggregation method.

Abstract: This paper proposes a holistic definition of airport sustainability comprising social, economic, operational, and environmental dimensions. Methodologically, a composite indicator approach is applied to build the Airport Sustainability Evaluation Index (ASEI), which aims to benchmark airports' sustainability performance across the four dimensions. To justify the issue of subjectivity in composite indicator building, two different methods are used in each of the normalization, weighting, and aggregation processes. Consequently, this forms eight composite indicator building schemes. Then, a variance-based sensitivity analysis, average shift in ranking (ASR), and Cronbach's alpha test are performed to examine the sensitivity and reliability among the eight schemes. Schiphol airport is selected as a demonstration to validate the ASEI with its data from 2012 to 2021 as inputs. The results reveal a significant consensus among the eight schemes in identifying the outstanding and bottom performers across the analyzed period. Additionally, weighting is found to be the most influential composite indicator building process. Further, the scheme with the most significant contribution to the result reliability found in this paper is re-scaling as the normalization method, Benefit-of-the-doubt (BoD) as the weighting method, and Non-compensatory multi-criteria approach (NCMC) as the aggregation method.

This paper estimates the price impacts of the unanticipated closure of Hazelwood, a large brown coal power plant (1600 MW) in Victoria, Australia. We measure the total impact of the closure on prices in Australia's National Electricity Market for each half-hour interval, and for each state, 12 months from closure. We also break down the impact into direct and indirect effects and find that the total impact of the closure on prices varies considerably across half-hours. The results vary not only in magnitude and across time, but also in statistical significance. Our estimates suggest an upper bound for the impact on the average half-hourly price of $24.02/MWh 12 months from closure, with a total market impact of $6,527.4 million. When we break down the total impact into direct and indirect effects, we find the latter to be the main driver of our results, through a reduction in the merit-order effect of wind generation.