Internet of Things (IoT) and the Energy Sector

Integration of renewable energy and optimization of energy use are key enablers of sustainable energy transitions and mitigating climate change. Modern technologies such the Internet of Things (IoT) offer a wide number of applications in the energy sector, i.e, in energy supply, transmission and distribution, and demand. IoT can be employed for improving energy efficiency, increasing the share of renewable energy, and reducing environmental impacts of the energy use. This paper reviews the existing literature on the application of IoT in in energy systems, in general, and in the context of smart grids particularly. Furthermore, we discuss enabling technologies of IoT, including cloud computing and different platforms for data analysis. Furthermore, we review challenges of deploying IoT in the energy sector, including privacy and security, with some solutions to these challenges such as blockchain technology. This survey provides energy policy-makers, energy economists, and managers with an overview of the role of IoT in optimization of energy systems.Peer reviewe

Internet of Things (IoT) and the Energy Sector

Integration of renewable energy and optimization of energy use are key enablers of sustainable energy transitions and mitigating climate change. Modern technologies such the Internet of Things (IoT) offer a wide number of applications in the energy sector, i.e, in energy supply, transmission and distribution, and demand. IoT can be employed for improving energy efficiency, increasing the share of renewable energy, and reducing environmental impacts of the energy use. This paper reviews the existing literature on the application of IoT in in energy systems, in general, and in the context of smart grids particularly. Furthermore, we discuss enabling technologies of IoT, including cloud computing and different platforms for data analysis. Furthermore, we review challenges of deploying IoT in the energy sector, including privacy and security, with some solutions to these challenges such as blockchain technology. This survey provides energy policy-makers, energy economists, and managers with an overview of the role of IoT in optimization of energy systems.Peer reviewe

Short-term load forecasting of microgrid via hybrid support vector regression and long short-term memory algorithms

© 2020 by the authors. Short-Term Load Forecasting (STLF) is the most appropriate type of forecasting for both electricity consumers and generators. In this paper, STLF in a Microgrid (MG) is performed via the hybrid applications of machine learning. The proposed model is a modified Support Vector Regression (SVR) and Long Short-Term Memory (LSTM) called SVR-LSTM. In order to forecast the load, the proposed method is applied to the data related to a rural MG in Africa. Factors influencing the MG load, such as various household types and commercial entities, are selected as input variables and load profiles as target variables. Identifying the behavioral patterns of input variables as well as modeling their behavior in short-term periods of time are the major capabilities of the hybrid SVR-LSTM model. To present the efficiency of the suggested method, the conventional SVR and LSTM models are also applied to the used data. The results of the load forecasts by each network are evaluated using various statistical performance metrics. The obtained results show that the SVR-LSTM model with the highest correlation coefficient, i.e., 0.9901, is able to provide better results than SVR and LSTM, which have the values of 0.9770 and 0.9809, respectively. Finally, the results are compared with the results of other studies in this field, which continued to emphasize the superiority of the SVR-LSTM model

The direct interconnection of the UK and Nordic power market – Impact on social welfare and renewable energy integration

United Kingdom and the Nordic power market have plans to interlink directly through a sub-sea power transmission line in The North Sea. Such power market couplings have complicated implications for the interconnected energy systems and for different agents in the common power market. We analyse this case by modelling the hourly operation of the Nordic-UK power market coupling, considering the local district heating (DH) system in each country as well. According to the results, after the operation of the new interconnection between Norway and the UK (North Sea Link), the overall socio-economic benefits (social welfare) in the region will likely improve by 220–230 million euro per year, without considering the cost of the interconnector itself. The UK-Nordic market coupling enhances the flexibility of the UK power system in wind integration, irrespective of the share of wind in the Nordic countries. However, increasing wind capacity in the UK will diminish the expected economic benefits of the link. The merit order effect of wind integration in the UK will reduce the price gap between UK and Norway, and so the congestion income of the link in many hours a year when the link is congested from Norway towards the UK

Deep Learning in Energy Modeling: Application in Smart Buildings With Distributed Energy Generation

Buildings are responsible for 33% of final energy consumption, and 40% of direct and indirect CO2 emissions globally. While energy consumption is steadily rising globally, managing building energy utilization by on-site renewable energy generation can help responding to this demand. This paper proposes a deep learning method based on a discrete wavelet transformation and long short-term memory method (DWT-LSTM) and a scheduling framework for the integrated modelling and management of energy demand and supply for buildings. This method analyzes several factors including electricity price, uncertainty in climatic factors, availability of renewable energy sources (wind and solar), energy consumption patterns in buildings, and the non-linear relationships between these parameters on hourly, daily, weekly and monthly intervals. The method enables monitoring and controlling renewable energy generation, the share of energy imports from the grid, employment of saving strategy based on the user priority list, and energy storage management to minimize the reliance on the grid and electricity cost, especially during the peak hours. The results demonstrate that the proposed method can forecast building energy demand and energy supply with a high level of accuracy, showing a 3.63-8.57% error range in hourly data prediction for one month ahead. The combination of the deep learning forecasting, energy storage, and scheduling algorithm enables reducing annual energy import from the grid by 84%, which offers electricity cost savings by 87%. Finally, two smart active buildings configurations are financially analyzed for the next thirty years. Based on the results, the proposed smart building with solar Photo-Voltaic (PV), wind turbine, inverter, and 40.5 kWh energy storage has a financial breakeven point after 9 years with wind turbine and 8 years without it. This implies that implementing wind turbines in the proposed building is not financially beneficial.Peer reviewe

The role of natural gas in setting electricity prices in Europe

The EU energy and climate policy revolves around enhancing energy security and affordability, while reducing the environmental impacts of energy use. The European energy transition has been at the centre of debate following the post-pandemic surge in power prices in 2021 and the energy crisis following the 2022 Russia-Ukraine war. Understanding the extent to which electricity prices depend on fossil fuel prices (specifically natural gas) is key to guiding the future of energy policy in Europe. To this end, we quantify the role of fossil-fuelled vs. low-carbon electricity generation in setting wholesale electricity prices in each EU-27 country plus Great Britain (GB) and Norway during 2015-2021. We apply econometric analysis and use sub/hourly power system data to estimate the marginal share of each electricity generation type. The results show that fossil fuel-based power plants set electricity prices in Europe at approximately 58% of the time (natural gas 39%) while generating only 34% of electricity (natural gas 18%) a year. The energy transition has made natural gas the main electricity price setter in Europe, with gas determining electricity prices for more than 80% of the hours in 2021 in several countries such as Belgium, GB, Greece, Italy, and the Netherlands. Hence, Europe’s electricity markets are highly exposed to the geopolitical risk of gas supply and natural gas price volatility, and the economic risk of currency exchange

Buoyancy Energy Storage Technology:An energy storage solution for islands, coastal regions, offshore wind power and hydrogen compression

The world is undergoing a substantial energy transition with an increasing share of intermittent sources of energy on the grid such as wind and solar. These variable renewable energy sources require an energy storage solution to allow a smooth integration of these sources. Batteries can provide short-term storage solutions. However, there is still a need for technologies that can provide weekly energy storage at locations without potential for pumped hydro storage. This paper presents innovative solutions for energy storage based on “buoyancy energy storage” in the deep ocean. The ocean has large depths where potential energy can be stored in gravitational based energy storage systems. The deeper the system, the greater the amount of stored energy. The cost of Buoyancy Energy Storage Technology (BEST) is estimated to vary from 50 to 100 USD/kWh of stored electric energy and 4,000 to 8,000 USD/kW of installed capacity. BES could be a feasible option to complement batteries, providing weekly storage cycles. As well as from storing energy, the system can also be used to compress hydrogen efficiently

Buoyancy energy storage technology : an energy storage solution for islands, coastal regions, offshore wind power and hydrogen compression

The world is undergoing a substantial energy transition with an increasing share of intermittent sources of energy on the grid such as wind and solar. These variable renewable energy sources require an energy storage solution to allow a smooth integration of these sources. Batteries can provide short-term storage solutions. However, there is still a need for technologies that can provide weekly energy storage at locations without potential for pumped hydro storage. This paper presents innovative solutions for energy storage based on “buoyancy energy storage” in the deep ocean. The ocean has large depths where potential energy can be stored in gravitational based energy storage systems. The deeper the system, the greater the amount of stored energy. The cost of Buoyancy Energy Storage Technology (BEST) is estimated to vary from 50 to 100 USD/kWh of stored electric energy and 4,000 to 8,000 USD/kW of installed capacity. BES could be a feasible option to complement batteries, providing weekly storage cycles. As well as from storing energy, the system can also be used to compress hydrogen efficiently