Constructing microgrids with renewable energy systems could be one feasible solution to increase the penetration of renewable energy. With proper control of the battery energy storage system (BESS) and thermostatically controlled loads (TCLs) in such microgrids, the variable and intermittent energy can be smoothed and utilized without the interference of the main power grid. In this paper, a decentralized control strategy for a microgrid consisting of a distributed generator (DG), a battery energy storage system, a solar photovoltaic (PV) system and thermostatically controlled loads is proposed. The control objective is to maintain the desired temperature in local buildings at a minimum cost. Decentralized control algorithm involving variable structure controller and dynamic programming is used to determine suitable control inputs of the distributed generator and the battery energy storage system. The model predictive control approach is utilized for long-term operation with predicted data on solar power and outdoor temperature updated at each control step.

In this paper, an optimal control strategy is presented for grid-connected microgrids with renewable generation and battery energy storage systems (BESSs). In order to optimize the energy cost, the proposed approach utilizes predicted data on renewable power, electricity price, and load demand within a future period, and determines the appropriate actions of BESSs to control the actual power dispatched to the utility grid. We formulate the optimization problem as a Markov decision process and solve it with a dynamic programming algorithm under the receding horizon approach. The main contribution in this paper is a novel cost model of batteries derived from their life cycle model, which correlates the charge/discharge actions of batteries with the cost of battery life loss. Most cost models of batteries are constructed based on identifying charge–discharge cycles of batteries on different operating conditions, and the cycle counting methods used are analytical, so cannot be expressed mathematically and used in an optimization problem. As a result, the cost model proposed in this paper is a recursive and additive function over control steps that will be compatible with dynamic programming and can be included in the objective function. We test the proposed approach with actual data from a wind farm and an energy market operator.

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Dried ginger is a commonly used stomachic. Dried ginger is often used as a gastric protector to treat stomach-related diseases. However, the effect of dried ginger on energy metabolism in stomach tissue of rats under physiological condition has not been studied. In this study, different doses of water extract of dried ginger were given to rats for 4 weeks. The activity of Na + -K + -ATPase, Ca 2 + -Mg 2 + -ATPase, SDH (succinate dehydrogenase) enzyme, ATP content, mitochondrial metabolic rate and mitochondrial number in stomach tissue of rats were measured. Analysis of potential biomarkers related to the effect of dried ginger on energy metabolism in stomach tissue of rats by metabonomics, and their metabolic pathways were also analyzed. The results revealed that there was no significant difference in Na + -K + -ATPase in high-dose group (GJH), medium-dose group (GJM) and low-dose group (GJL) compared to the Control group. The Ca 2 + -Mg 2 + -ATPase activity was significantly increased in stomach tissue of GJH group and GJM group, but there were no significant changes in stomach tissue of GJL group. The SDH activity and the ATP levels were significantly increased in stomach tissue of GJH group, GJM group and GJL group. The mitochondrial metabolic rate was significantly increased in GJL group, but there was no significant change in GJM group and was inhibited in GJH group. These effects might be mediated by arginine biosynthesis, glutathione metabolism, arachidonic acid metabolism, glycerophospholipid metabolism, arginine and proline metabolism, purine metabolism pathway.