Modeling, scientific computing and optimal control for renewable energy systems with storage (original) (raw)
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International Journal of Modeling and Optimization, 2015
The variability of solar and wind energy generation presents significant grid integration challenges especially in the operation of power systems. The optimal use of energy storage system may be a viable solution to control and match the power generation to the load profile. This paper proposes a method to identify the optimum daily operational strategy of a storage system. An hourly optimization problem was formulated for this purpose to maximize the economic gain of a wind farm considering the market price and the main operational restrictions of the hybrid energy system. This method is also suitable for other energy resources and energy storage technologies. Another prominent issue facing renewable energy farms is the proper sizing of the energy storage system. A method to optimize the size of the storage system with respect to the net present cost (NPC), while meeting the service requirement is also proposed. Case studies are presented to verify the effectiveness of these models.
Journal of Simulation, 2015
It has been widely recognised that renewable energy, such as wind, provides valuable benefits for the environment, human health, and the economy. Nevertheless, renewable energy has several drawbacks: high variability in its availability, uncertainty in its forecast, and difficulty in matching production to demand. The storage of energy would enable solving of most of these problems. In this paper, we obtain operative management policies for energy storage under two criteria: maximising the profit of selling the energy and maximising the reliability of the system as a provider of committed energy. Decisions take into account data concerning the structure of selling prices and penalties, as well as updated probabilistic wind speed forecasts. We use a sequence of rolling horizon stochastic optimisation problems to determine the parameters of the proposed management strategies. To solve these problems we propose a simulation-based optimisation methodology.
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This work presents a thermo-economic simulation model of a hybrid renewable power plant based on wind turbine and photovoltaic technologies, coupled to an energy storage system. The total plant capacity is 200 kW (190 kW and 10 kW, for photovoltaic and wind turbine, respectively), whereas the energy storage capacity is 400 kWh. Aim of this work is to design a renewable power plant showing limited fluctuations (with respect to the ones typically achieved in case of solar systems) with marginal amounts of electricity purchased or sold to the grid, maximizing the electricity selfconsumption. The thermo-economic model, developed in TRNSYS environment, allows one to determine the best system configuration and maximize the economic profitability by considering the time-dependent tariffs applied to the electricity exchanged with the grid and the possibility to store electricity. Different system layouts with or without the storage system and for different users are considered. Results show negative profit indexes of the layouts including the storage system (-0.27 in the worst case vs. 0.61 in the best case without the storage), due to its lower efficiency and its higher capital cost, although a remarkable reduction of the operating costs and an enhancing of the self-consumed energy.
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Since the discovery of different forms of energy and their integration into industrial or residential systems, various possibilities to store and realize energy reserves have been considered. Due to the volatility in the input of the renewable energy sources and also the variation of the demand, energy storage systems are needed. In this paper Li-Ion batteries are considered in a power system that contains thermal generating units, hydro generating units and wind generating units. A mathematical model is presented that determines the optimal operation of a power system similar to a real one that is located in the south-eastern part of Romania.
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