Power systems have been undergoing significant restructuring as a result of increasing independently operated local resources. Consequently, new entities, i.e. microgrids (MGs), are developed, which facilitate the integration of local resources, specifically renewable energy sources (RESs), into the operation of power systems. Despite many benefits, the integration of RESs could cause severe rampings in the net-load, which would challenge the reliable operation of the system. Therefore, it seems essential that flexible local resources in an MG should be employed to provide flexibility services to the main grid, thus ensuring that ramping in the MG's net-load would meet the ramping capability in the upper-level system. Accordingly, this study aims to develop an energy management framework to schedule local resources in an MG considering system's ramping limits. In this regard, the interaction between electricity and gas grid as a potential future flexible resource for energy systems is considered in the developed scheme. Moreover, the chance-constrained methodology is employed to address the uncertainty associated with the operational management of RESs. Finally, the proposed energy management model is analysed from different perspectives to show the importance of the contribution of flexible local resources to the efficient improvement of the power system flexibility.

Summary Demand forecasting plays an important role as a decision support tool in power system management, especially in smart grid and liberalized power market. In this paper, a demand forecasting method is presented by using support vector regression (SVR). The proposed method is applied to practical hourly data of the Greater Tehran Electricity Distribution Company. The SVR parameters are selected by using a grid optimization process and an investigation on different kernel functions. Moreover, correlation analysis is used to find exogenous variables. Acceptable accuracy of load prediction is shown by comparing the result of SVR model to that of the artificial neural networks and the actual data, concluding that the method is applicable to day‐ahead spot pricing of electricity in the liberalized power market. Copyright © 2013 John Wiley & Sons, Ltd.

Restructuring and privatization in power systems have resulted in a fundamental transition of conventional distribution systems into modern multi-agent systems. In these structures, each agent of the distribution system would independently operate its local resources. In this regard, uncertainties associated with load demands and renewable energy sources could challenge the operational scheduling conducted by each agent. Therefore, this paper aims to develop a distributed operational management for multi-agent distribution systems taking into account the uncertainties of each agent. The developed framework relies on alternating direction method of multipliers (ADMM) to coordinate the operational scheduling of the agents in a distributed manner. Moreover, a robust optimization technique is employed to consider the worst-case realization associated with the operation of each agent. Finally, the proposed framework is implemented on IEEE 37-bus network to analyze its efficacy in distributed robust operational management of distribution systems with multi-agent structures.Index Terms-Energy Management, active distribution systems, distributed management, alternating direction method of multipliers (ADMM), robust optimization.