This paper focuses on the problems of optimal operation of battery energy storage systems (BESSs) in distributed networks from a nonlinearprogramming (NLP) point of view. This proposal contains the active and reactiv...
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This paper focuses on the problems of optimal operation of battery energy storage systems (BESSs) in distributed networks from a nonlinearprogramming (NLP) point of view. This proposal contains the active and reactive power capabilities of the voltage source converters that interface with the BESS independently, which implies that the BESSs have the possibility to work as dynamic energy compensators over all the four quadrants. Two radial distribution networks have been used to validate the proposed NLP model. This model has been arrived at by using the general algebraic modeling system (GAMS) optimization package. Artificial neural networks (ANN) are also employed for short-term forecasting of renewable generation based on wind and photo-voltaic sources. Numerical results show that the inclusion of reactive power capabilities of batteries reduces the daily operational cost of the network as compared to the classical unity power factor operation approach. All the simulations are made considering Colombian utility typical curves. (c) 2020 Elsevier Ltd. All rights reserved.
Recently, as the problem of "peak load" in supply systems of electricity and town-gas is getting quite serious in urbanized areas of Japan, the necessity of "load management" is increasing. In thes...
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Recently, as the problem of "peak load" in supply systems of electricity and town-gas is getting quite serious in urbanized areas of Japan, the necessity of "load management" is increasing. In these areas, the peak load of the electric power system is brought about by demand for air conditioning in summer. In contrast, the peak load of the town-gas system which is brought about by demand for space heating exists in winter. Therefore mutual substitution of the demands between these two energy utilities, by some means for a cooperative supply, can be an effective way for load leveling of the both systems. One of the promising means to achieve this cooperative supply is use of strategic incentive rates such as time-of-use pricing and seasonal pricing instead of the existing constant pricing In the present paper, an energy model of a nonlinearprogramming type, is developed to investigate the issue. Due to simulation studies based on the model, the effects of these new pricing schemes on the cooperative supply of the energy utilities are made clear quantitatively
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