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Power system operation has been a very complex and interesting problem, and currently has gathered more attention because of new elements in the network. New elements such as renewable sources, demand response, electric vehicles, and energy storage systems, are crucial because of their stochastic behavior. Stochastic variables present new challenges for the system operator, who must prevent future changes in the network status by taking efficient decisions with available information and guarantee the system security. In this document we show that Model Predictive Control (MPC) might be appropriate to solve the economic dispatch problem when feasible future events are considered. A case study is solved from different perspectives of the system operator, which is able to consider or neglect future events. Results show that MPC efficiently solves the economic dispatch problem when considering future events and ramp rate constraints. [ABSTRACT FROM PUBLISHER]

Planning of protection systems plays a significant role to achieve an efficient behavior (or performance) of distribution systems. Feeders that offer high robustness and reliability are essential to ensure continuous energy delivering to all customer loads. Since distributed generation (DG) has increased its penetration in distribution systems, it has become necessary to place protective devices (such as reclosers) in order to allow DG islanded operation, which decreases the energy not supplied. This paper addresses efficient placement of protective switches, which mainly comprises circuit breakers and reclosers. Two optimization approaches are proposed to enhance reliability indices. First, a single objective optimization problem is defined to maximize the profit of the distribution system operator. Such problem is solved through implementing the differential evolution algorithm (DE). Second, a multi-objective optimization problem is defined to reduce the system average interruption duration index (SAIDI), the system average interruption frequency index (SAIFI), and system costs. This approach yields optimal planning of switches by applying the non-dominated sorting differential evolution algorithm (NSDE). The heuristic algorithms are implemented in Matlab, while the power system is modeled in DIgSILENT in order to use a co-simulation environment between these two tools. Simulations are developed on a real test feeder for two cases: (i) without DG; and (ii) with 6 MW penetration of distributed sources. Results show the importance of both DG and protective devices to enhance reliability, and the effect of enabling intentional islanded operation. [ABSTRACT FROM AUTHOR]

• Stochastic distributed economic dispatch methods based on MPC are proposed. • Hourly and ultra-short term dispatches integration boost the power system behavior. • A data-driven scenario generation is applied to consider stochastic variables. • Computational and operation cost analyses show the advantages of the proposed method. Power systems operation has been traditionally addressed by deterministic and centralized approaches because of their low-variation behavior. However, current tendencies have introduced variability and stochasticity as a result of including renewable energy sources, active demand participation, and short-term market clearing. Thereby, operators are looking for utilizing available forecast information to enhance the system operation response to unpredictable changes from the uncertainty sources. This paper considers two distributed techniques that solve the economic dispatch problem in an hourly basis and for the ultra-short term, and a data-driven scenario generation method that reduces uncertainty impacts on operation costs. At first, the hourly and ultra-short term dispatches are presented as stochastic programming problems by relying on model predictive control (MPC), which also address the concern of variability and uncertainty. Second, since ultra-short term dispatch does not optimize the social benefit, we provide a hierarchical configuration that allows operators to efficiently coordinate it with the hourly approach to obtain enhanced operation costs. The simulation results validate the advantages of using stochastic programming instead of deterministic approaches under smart grids framework and show how a hierarchical coordination of both methods provides enhanced results. Additionally, computational time has been tested and it has been successfully shown that the proposed methods maintain a reasonable computational burden even for high complexity cases. [ABSTRACT FROM AUTHOR]