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The task of electric power companies is to achieve their economic targets and social responsibilities. As one of the promising methods to make these targets come true, asset management is attracting more and more attention, and the life cycle cost management (LLCM) is an effective tool of asset evaluation. In order to optimize the electrical equipment life cycle, and aiming at some problems in the current equipment maintenance research, which includes ignoring the influence of maintenance on equipment failure rate, ignoring the trend of continued deterioration during the operation of equipment and so on, this paper introduces the deterioration theory in the field of mechanical engineering. This essence of this method can be regarded as searching the balance between the value of equipment themselves and the benefits brought by the power systems, between LCCM and equipment maintenance, and finally, between technologies and management of power system. Based on this method, this article deduces the relationship of the reliability of equipment in each maintenance circle, and constructs the maintenance strategy optimization model of equipment based on risk combined with the definition of risk and service age reduction factor. In the end, this paper employs the theory into a practical example and calculates the optimal maintenance interval, and finally compares it with traditional preventive maintenance.

Stepwise power tariff (SPT), which has been put into practice, is a crucial way for energy saving and environment protecting. In this paper, a new optimal model of SPT based on residential demand response model is presented. The optimal decision is proposed to restrain high electricity consumption as well as safeguard benefits of both supply and demand sides. As a result, the objective is designed to minimize electricity consumption and constraints are taken into consideration thoroughly, including acceptable index of consumers, average price, sales profit of power providers and basic electricity demand, which serves as a foundation for smooth implement of SPT. To solve the constrained optimal problem, genetic algorithm (GA) is employed. The effectiveness of the model and algorithm is investigated and demonstrated based on real data of 300 residents by a numerical example. The study shows that the method can reduce power consumption obviously with little sacrifice of the benefits of consumers and power providers.

Large-scale deployment of electric vehicles (EVs) is anticipated in the foreseeable future. Heavy intermittent charging load of EVs will create bottlenecks in supplying capacity and expose power system to severe security risks. In this paper, we propose an intelligent method to control EV charging loads in response to time-of-use (TOU) price in a regulated market. First, an optimized charging model is formulated to minimize the charging cost. Then, a heuristic method is implemented to minimize the charging cost considering the relation between the acceptable charging power of EV battery and the state of charge (SOC). Finally, the charging cost and energy demand in different time intervals are compared for both typical charging pattern and optimized charging pattern. Results show that the optimized charging pattern has great benefit in reducing cost and flatting the load curve if the peak and valley time periods are partitioned appropriately.

Objective To evaluate the clinical effects of combined acupuncture and ear-puncture in treating renal colic.