Battery energy storage systems (BESSs) have been widely used in power grids to improve their flexibility and reliability. However, the inevitable battery life degradation is the main cost in BESS operations. Thus, an accurate estimation of battery aging cost is strongly needed to cover the actual cost of BESSs. The existing models of battery life degradation either are not fully accurate to estimate the actual cost or are not solved easily because of their computation nonlinearity. In this paper, a piece-wise linear battery aging cost model with an accurate estimate of battery life degradation for BESSs is proposed to extend battery life and improve battery profits. In our method, the widely-used Arrhenius law is modified to quantify the battery life degradation affected by the depth of cycle. Further, a nonlinear battery cycle aging cost model is developed by finding the derivative of battery life degradation with respect to discharging power, which indicates the battery life degradation rate due to depth of cycle. To reduce the complexity of computation, a piece-wise linearization method is proposed to simplify the battery cycle aging cost model. Finally, the cycle aging cost model with an accurate estimation of battery life degradation is applied to the optimization dispatch in the day-ahead energy and auxiliary service market. The results show that the error of estimating the battery cycle aging cost of BESSs is less than 5% under proper piece-wise segment numbers. The profits are increased by 27% and the battery life is extended by 11% than the fixed cost method.
Energy management in power systems is a thorny optimization problem. With the sizes of systems rising, centralized optimization methods are restricted by their complexities of communications, while distributed optimization methods have emerged as a powerful tool for dealing with increasingly complex systems. However, convergence rates of some widely used distributed optimization methods, such as the standard alternating direction method of multipliers (ADMM), still have room for improvement. In this paper, a parallel and distributed optimization method for energy management of microgrids (MGs) is proposed to boost the convergence rate without sacrificing the accuracy of the optima, in which agents calculate, exchange and update in parallel. At first, a decomposition method is presented, where the objective functions and constraints of an original optimization problem with separable variables are decomposed into local objective functions and constraints for agents, which is the key to our method. Further, agents solve their local optimization problems independently and then exchange determined optima with their neighbors. Finally, the method is evaluated to solve economic dispatch with demand response for microgrids. The simulation results show that compared to the standard ADMM, for a given accuracy, the number of iterations in our method is only one third or even less than that of ADMM. Furthermore, our method can minimize the cost functions of distributed generation on supply side and maximize the profit functions of flexible loads on the demand side.
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