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The energy management schemes in microgrids enhance the utilization of renewable energy resources and improve the reliability and resilience measures in the distribution networks. While microgrids operate autonomously, the coordination among microgrid and distribution network operators (DNOs) contributes to the improvement in the economics and reliability of serving the demand. Here, a decentralized energy management framework for the networked microgrids is proposed in which the interactions between the microgrid and DNOs are captured using the Benders decomposition technique. The proposed framework limits the information shared among these autonomous operators and facilitates decentralized energy management in the distribution networks. Furthermore, the unbalanced operation of the distribution network and microgrids, as well as the uncertainty in the operating modes of the microgrids, renewable energy resources, and demand, were addressed. Modified IEEE‐34 bus and IEEE‐123 bus distribution networks are used to validate the effectiveness of the proposed algorithm. [ABSTRACT FROM AUTHOR]

This paper proposes an approach to formulate the multiple-period ramping capability of dispatchable generation resources and evaluates the impact of this service on the generation scheduling in day-ahead electricity market. It is discussed that the multiple-period ramping enhances the load following capability of dispatchable generation resources and improves the dispatchability of renewable energy resources in power systems. The presented approach encompasses the uncertainties in the operation scheduling of power systems, using scenario based stochastic security-constrained unit commitment. The presented case studies also highlight the merits of integrating energy storage facilities to reduce the ramping services provided by dispatchable generation resources with respective costs. [ABSTRACT FROM PUBLISHER]

• A data-driven distributionally robust optimization problem is proposed for the unbalanced operation of the distribution network considering the uncertainties in solar photovoltaic generation and electric vehicle interconnection. • A two-stage solution methodology is proposed, where the network switching problem is considered at the first stage while the operation cost minimization is achieved in the second stage. The proposed solution framework leverages the column-and-constraint generation algorithm. • The solution to the formulated problem is compared with the solutions to stochastic programming and robust optimization problems. The in-sample and out-of-sample performance of the proposed formulation are compared with those of stochastic programming. The uncertainties associated with the large-scale integration of electric vehicles (EV) and renewable energy resources introduce new challenges to operating the unbalanced three-phase distribution networks. In this paper, a data-driven distributionally robust optimization framework is proposed for the operation of the distribution network considering the uncertainties associated with the interconnected EV fleets and solar photovoltaic (PV) generation. The proposed framework leverages the column-and-constraint generation (C&CG) approach to minimize the operation cost considering the worst-case probability distributions of PV generation, the available energy in EV fleets, the arrival and departure times of EV fleets as well as their minimum and maximum energy capacities. The proposed approach is applied to the modified three-phase unbalanced IEEE 34-bus and IEEE 123-bus networks. To evaluate the performance of the proposed distributionally robust optimization framework, the results are compared to those procured by solving scenario-based stochastic programming and robust optimization problems. Furthermore, the impact of vehicle-to-grid capability on the operation of the distribution network is investigated and the in-sample and out-of-sample performances of the proposed framework are evaluated. [ABSTRACT FROM AUTHOR]

Demand forecasting faces challenges that include a large volume of data, increasing number of factors that affect the demand profile, uncertainties in the generation profile of the distributed and renewable generation resources and lack of historical data. A hierarchical demand forecasting framework can incorporate the new technologies, customer behaviors and preferences, and environmental factors. [ABSTRACT FROM AUTHOR]

In this paper, the coordinated integration of aggregated plug-in electric vehicle (PEV) fleets and renewable energy sources (wind energy) in power systems is studied by stochastic security-constrained unit commitment (Stochastic SCUC) model, which minimizes the expected grid operation cost while considering the random behavior of the many PEVs. PEVs are mobile and distributed devices with deferrable options for the supply/utilization of energy at various times and locations. The increased utilization of PEVs, which consume electricity rather than fossil fuel for driving, offers unique economic and environmental opportunities, and brings out new challenges to electric power system operation and planning. The storage capability of PEVs could help power systems mitigate the variability of renewable energy sources and reduce grid operation costs. Vehicle-to-grid (V2G) enables PEVs to have bi-directional power flows once they are connected to the grid, i.e., they can either inject power to, and draw power from, the grid which adds further complexity to power system operations. PEVs signify customers' random behavior when considering their driving patterns, locational energy requirements, topological grid interconnections, and other constraints imposed by the consumers. Numerical tests demonstrate the effectiveness of the proposed approach for analyzing the impact of PEVs on the grid operation cost and hourly wind energy dispatch. [ABSTRACT FROM PUBLISHER]

In recent years, sustainable sources of energies attract significant interest due to the serious environmental issues of fossil fuels. Rooftop photovoltaic (PV) panels are among the important power generation technologies exploited in many modern countries. Most PV panels are installed behind-the-meter (BTM), resulting in a lack of observability of actual load and PV power generation in a power distribution system. This paper proposes a novel supervised spatiotemporal approach to accurately disaggregate the net-load data of a set of neighboring residential units. To this end, spatiotemporal correlations of a group of neighboring residential units are modeled using a weighted undirected graph where the nodes store the temporal features. The edges reflect the spatial correlation between neighboring residential units and are determined by the information analysis technique. Afterward, a generative graph attention recurrent neural network (RNN) is devised for capturing highly nonlinear patterns of input graphs using an RNN encoder. In addition, to boost the generalization capacity and robustness of the proposed model, we reconstruct the input graphs using sparse contractive decoders. Finally, an extreme learning machine (ELM) neural network is employed to disaggregate the input net-load time-series of a set of residential units using the extracted complex spatiotemporal patterns. Experimental results on the real-world Pecan Street dataset demonstrate the superiority of the proposed RNN-ELM method over the recent BTM disaggregation techniques. [ABSTRACT FROM AUTHOR]