Energy and exergy analysis of a micro-compressed air energy storage and air cycle heating and cooling system

Energy storage systems are becoming more important for load leveling, especially for widespread use of intermittent renewable energy. Compressed air energy storage (CAES) is a promising method for energy storage, but large scale CAES is dependent on suitable underground geology. Micro-CAES with man-made air vessels is a more adaptable solution for distributed future power networks. In this paper, energy and exergy analyses of a micro-CAES system are performed, and, to improve the efﬁciency of the system, some innovative ideas are introduced. The results show that a micro-CAES system could be a very effective system for distributed power networks as a combination that provides energy storage, generation with various heat sources, and an air-cycle heating and cooling system, with a energy density feasible for distributed energy storage and a good efﬁciency due to the multipurpose system. Especially, quasi-isothermal compression and expansion concepts result in the best exergy efﬁciencies

Design optimization of electrical hubs using hybrid evolutionary algorithm

Dasaraden Mauree, Vahid Moussavi Nik, Amarasinghage Tharindu Dasun Perera, Jean-Louis Scartezzini

Integration of non-dispatchable renewable energy sources such as wind and solar into the grid is challenging due to the stochastic nature of energy sources. Hence, electrical hubs (EH) and virtual power plants that combine non-dispatchable energy sources, energy storage and dispatchable energy sources such as internal combustion generators and micro gas turbines are getting popular. However, designing such energy systems considering the electricity demand of a neighborhood, curtailments for grid interactions and real time pricing (RTP) of the main utility grid (MUG) is a difficult exercise. Seasonal and hourly variation of electricity demand, potential for each nondispatchable energy source and RTP of MUG needs to be considered when designing the energy system. Representation of dispatch strategy plays a major role in this process where simultaneous optimization of system design and dispatch strategy is required. This study presents a bi-level dispatch strategy based on reinforced learning for simultaneous optimization of system design and operation strategy of an EH. Artificial Neural Network (ANN) was combined with a finite state controller to obtain the operating state of the system. Pareto optimization is conducted considering, lifecycle cost and system autonomy to obtain optimum system design using evolutionary algorithm.

Amer Soc Mechanical Engineers2016

Evaluating the need for energy storage to enhance autonomy of neighborhoods

Dasaraden Mauree, Amarasinghage Tharindu Dasun Perera, Jean-Louis Scartezzini, Karni Siraganyan

Energy storage is generally considered as a means to bridge a period between when/where energy is available and when/where it is in demand. Storage plays an important role by providing flexibility to energy systems, increasing the potential to accommodate variable renewables generation and improving management of electricity networks. However, currently it remains unclear when and under which conditions energy storage can be profitably operated at a district level. The present study aims to quantify the level of integration of solar energy and storage in the Junction district of Geneva. A simulation tool is developed to investigate the techno-economical and environmental assessment under different scenarios. For a given investment over 20 years, the model calculates the levelized cost of electricity (LCOE), the autonomy level as well as the CO2 emissions. Given the assumptions of the model, four scenarios are analysed based on the combination of solar PV, storage, solar thermal and heat pump to find out an economically optimal configuration in terms of system size. A comparison with the Homer software is performed to test the robustness of the solar PV and battery model. The economic profitability of solar PV and battery system is in very good agreement with Homer and the autonomy level is validated by using a simulation tool created by SI-REN (Services Industriels des Energies Renouvelables de Lausanne). However, combining solar PV with battery system doesn’t bring additional autonomy to the model for Geneva study case. Under the assumptions of the model, to foster investments in solar PV and battery installations, falling investments costs seem necessary for the future. A reduction gap between buying and selling price in grid for solar panel is recommended to increase solar installations. A validated simulation tool has been developed in this work and provide a reliable based that will be extended in the future to include the thermal demand and production. The availability of thermal storage at a large scale as well as the production over a district should further increase the autonomy of the district.

Elsevier2017

Optimum dispatch of a multi-storage and multi-energy hub with demand response and restricted grid interactions

Timothée Chatelain, Dasaraden Mauree, Amarasinghage Tharindu Dasun Perera, Jean-Louis Scartezzini

Integrated energy systems such as multi energy hubs which combines different energy conversion technologies and storage is recently getting popular. These systems possess the potential to integrate distributed renewable energy sources with a minimum impact to the grid. Hence, a number of recent studies have focused on optimizing the operation of energy hubs with simple energy systems. This study focuses on optimizing the dispatch of a multi-energy hub model which includes solar PV (SPV) panels, wind turbines, boiler, Internal Combustion Generator (ICG), Cogeneration plant (CHP), energy storage (heat and electricity). The energy hub is considered to maintain limited interactions with the grid. Interactions with e-mobility is considered as a flexible demand. A detailed energy hub model is developed considering energy interactions among all the aforementioned components. Dispatch strategy is optimized using evolutionary algorithm. Results obtained from the evolutionary algorithm is compared with Particle Swarm Optimization (PSO) algorithm. A detailed analysis is conducted in order to assess the energy interactions within the system. Sensitivity of system components such as storage size, renewable energy capacity etc., grid interactions, and time horizon considered for optimization is subsequently assessed and reported concisely. Results obtained clearly shows choice of dispatch strategy is considerably volatile which makes the operation of the energy hub challenging. This can be mitigated up to a certain level by increasing the capacity of battery bank.

2017

Evaluating the need for energy storage to enhance autonomy of neighborhoods

Dasaraden Mauree, Amarasinghage Tharindu Dasun Perera, Jean-Louis Scartezzini, Karni Siraganyan

Elsevier2017