Search

Abstract Energy sources, like hydro and wind power, present uncertain nature, characteristic that affects the electric power systems operation planning. Such factors require using methodologies capable to handle these uncertainties in interconnected hydro-thermal-wind power systems operation. This work proposes a computational model for the day-ahead unit commitment problem for interconnected hydro-thermal-wind power systems, considering the uncertainties related to wind power, water inflow, and energy demand, employing the Robust Optimization (RO). The optimization model consists in a three-level mixed integer linear programming problem, which is resolved using a two-stage decomposition approach, solved using the column-and-constraint generation algorithm. The model is validated using the 30 nodes IEEE and the 33 nodes Brazilian test system. The model proposed proved to have an operating cost proportional to the level of uncertainty and the methodology employed provides a preventive view of what may occur during the hydro-thermal-wind systems operation, allowing the central system operator to take certain actions in order to ensure reliable operating conditions. In addition, the model allowed the identification of important aspects related to computational costs, such as the verification that constraints that couple the problem in time and space significantly impact the simulation times of the problem.

Abstract The insertion of new distributed energy resources, such as distributed generation (DG), energy storage systems (ESS), demand response (DR), and microgrids (MG), is emerging, bringing new challenges to the current distribution network. In this regard, the active distribution networks (I) with multi-microgrids concept appears. The present paper proposes a hierarchical (master-slave problem) computational model to achieve optimal coordinated operation of multi-microgrids connected to an I. Day-ahead operation planning of an I was formulated as a multiperiod non-linear optimal power flow model, resulting in a non-linear optimization problem, additionally, the day-ahead operation planning of MGs was formulated as a multiperiod linearized optimal power flow resulting in a mixed-integer linear optimization problem. Numerical results on four different test-system microgrids connected to a 359-nodes ADNs test-system belonging to a Brazilian distribution company show the effectiveness of the proposed model and solution strategy. Three cases have been tested: with a maximum load-shedding restriction, without this restriction, and considering insertion of DG. Besides, the hierarchical model can evaluate how much losses and load shedding take effect without integrated operation and expansion planning of emerging distributed networks. This study showed the importance of analyzing the systemic impact of integrating multi-MGs and I synergistic operation interactions, resulting in improvements in the voltage quality levels, operation costs, and power losses. The results showed that, including DG in the system, the costs were reduced by 13,48% compared to the case base.

Abstract The optimization of microgrids present challenges such as managing distributed energy resources (DERs) and the high reliance on intermittent generation such as PV and wind turbines, which present an aleatory behavior. The most popular techniques to deal with the uncertainties are stochastic optimization, which comes with a high computational burden, and adaptive robust optimization (ARO), which is often criticized for the conservativeness of its solutions. In response to these drawbacks, this work proposes a mixed-integer linear programming (MILP) model using a data-driven robust optimization approach (DDRO) solved by a two-stage decomposition using the column-and-constraint generation algorithm (C&CG). The DDRO model uses historic data to create the bounds of its uncertainty set, eliminating the conservativeness created by the arbitrary definition of the uncertainty set that is seen in ARO while maintaining a low computational burden. The DDRO model applied was not previously utilized in MGs, only in bulk power systems. A benchmark MG system was simulated for a 24-hour period without uncertainties, with uncertainties using ARO (15% uncertainty budget) and with uncertainties and DDRO. The operational costs without uncertainty were $124,600,60, while the ARO approach rose those costs by 32.5% ($ 165,137.18). Finally, the DDRO approach managed to keep the costs in $ 126,934.54, a mere 1.8% increase from the base case without uncertainty. All simulations were performed in less than 1 minute. The results confirm a) the advantages of bounding the uncertainty set with historical data instead of an arbitrary definition of bounds and b) the fast-converging times of DDRO.

Abstract: Although nonlinear Alternating Current (AC) Optimal Power Flow (OPF) may provide more accurate results compared to linearized OPF models, many ACOPF problems are complex and may require high, sometimes infeasible, computational time. The linear Direct Current OPF (DCOPF) has been widely used in power system analysis; however, it has limitations regarding reactive power analysis, and it may cause significant error in distribution networks with a high R/X ratio of feeders. Moreover, most of the existing formulations for the linear ACOPF do not consider mutual impedance for the lines and simplify the network as a single-phase system, which does not significantly represent unbalanced loads. This paper proposes a novel linear AC three-phase OPF formulation for unbalanced ZIP loads using current injection and considering delta and wye loads. The formulation is extended to include shunt capacitors, voltage regulators, and mutual impedance. The proposed methodology is illustrated using the IEEE 123-bus test system in case studies including/not-including voltage regulators, distributed generation, high R/X ratio, and mutual impedance. The results are very accurate compared with a nonlinear three-phase ACOPF model. The high computational performance and the accuracy of the proposed model considering distributed generation and high R/X ratio show its effectiveness in active distribution networks.