• Energy Research

Abstract Storage technologies such as the battery energy storage system are expected to play a critical role in micro-grids. However, current simulation tools underestimate their operating costs, which jeopardises their efficient use and deployment. This paper proposes a strategy based on artificial intelligence and time series prediction for the planning and real-time management of a battery energy storage system in a micro-grid acting as a virtual power plant. An economic analysis was performed, and the findings suggested that the state of charge should be managed to avoid economic losses due to cycle ageing. In addition, the battery should be sized correctly to ensure its economic viability, which indicates that it should be optimised according to the load profile of the micro-grid. The proposed management strategy was demonstrated to avoid the economic losses observed with a non-managed storage system. In addition, ensuring that the micro-grid did not deviate from the load profile agreed upon with the transmission system operator was found to increase economic returns, reduce battery degradation and increase self-consumption with the co-benefit of reducing micro-grid fluctuations.

Abstract Storage technologies such as the battery energy storage system are expected to play a critical role in micro-grids. However, current simulation tools underestimate their operating costs, which jeopardises their efficient use and deployment. This paper proposes a strategy based on artificial intelligence and time series prediction for the planning and real-time management of a battery energy storage system in a micro-grid acting as a virtual power plant. An economic analysis was performed, and the findings suggested that the state of charge should be managed to avoid economic losses due to cycle ageing. In addition, the battery should be sized correctly to ensure its economic viability, which indicates that it should be optimised according to the load profile of the micro-grid. The proposed management strategy was demonstrated to avoid the economic losses observed with a non-managed storage system. In addition, ensuring that the micro-grid did not deviate from the load profile agreed upon with the transmission system operator was found to increase economic returns, reduce battery degradation and increase self-consumption with the co-benefit of reducing micro-grid fluctuations.

The increasing attention to environmental issues is forcing the implementation of novel energy models based on renewable sources, fundamentally changing the configuration of energy management and introducing new criticalities that are only partly understood. In particular, renewable energies introduce fluctuations causing an increased request of conventional energy sources oriented to balance energy requests on short notices. In order to develop an effective usage of low-carbon sources, such fluctuations must be understood and tamed. In this paper we present a microscopic model for the description and the forecast of short time fluctuations related to renewable sources and to their effects on the electricity market. To account for the inter-dependencies among the energy market and the physical power dispatch network, we use a statistical mechanics approach to sample stochastic perturbations on the power system and an agent based approach for the prediction of the market players behavior. Our model is a data-driven; it builds on one day ahead real market transactions to train agents behaviour and allows to infer the market share of different energy sources. We benchmark our approach on the Italian market finding a good accordance with real data.

The increasing attention to environmental issues is forcing the implementation of novel energy models based on renewable sources, fundamentally changing the configuration of energy management and introducing new criticalities that are only partly understood. In particular, renewable energies introduce fluctuations causing an increased request of conventional energy sources oriented to balance energy requests on short notices. In order to develop an effective usage of low-carbon sources, such fluctuations must be understood and tamed. In this paper we present a microscopic model for the description and the forecast of short time fluctuations related to renewable sources and to their effects on the electricity market. To account for the inter-dependencies among the energy market and the physical power dispatch network, we use a statistical mechanics approach to sample stochastic perturbations on the power system and an agent based approach for the prediction of the market players behavior. Our model is a data-driven; it builds on one day ahead real market transactions to train agents behaviour and allows to infer the market share of different energy sources. We benchmark our approach on the Italian market finding a good accordance with real data.

AbstractThis paper presents the main features and the expected performance of the pilot solar power plant under construction in Ottana (Sardinia-Italy). The facility is based on a 600 kWe concentrating solar power (CSP) plant with thermal energy storage, and a 400 kWe concentrating photovoltaic (CPV) plant with electrochemical storage. The CSP plant uses linear Fresnel collectors, thermal oil as heat transfer fluid, a two-tank direct storage system and an ORC module. The CPV plant consists of 37 dual-axis trackers integrated with Sodium-Nickel batteries. The facility is characterised by the integration of different concentrating solar and storage technologies. The pilot power plant has been designed in order to produce electricity with scheduled profiles according to weather forecast.

AbstractThis paper presents the main features and the expected performance of the pilot solar power plant under construction in Ottana (Sardinia-Italy). The facility is based on a 600 kWe concentrating solar power (CSP) plant with thermal energy storage, and a 400 kWe concentrating photovoltaic (CPV) plant with electrochemical storage. The CSP plant uses linear Fresnel collectors, thermal oil as heat transfer fluid, a two-tank direct storage system and an ORC module. The CPV plant consists of 37 dual-axis trackers integrated with Sodium-Nickel batteries. The facility is characterised by the integration of different concentrating solar and storage technologies. The pilot power plant has been designed in order to produce electricity with scheduled profiles according to weather forecast.