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Operation planning models for hydro-dominated power systems usually use low temporal resolutions due to the excessive computational burden, thus ignoring short-term characteristics of such systems. As a result, in systems coupled with wind energy, such models may fail to accurately capture wind variability, and may not appropriately take into account potential consequences of uncertainty on the system operation. This paper addresses this drawback by (i) “controlling” the cost associated with the operation of a hydro-dominated power system equipped with wind power and batteries via a risk-measure and (ii) formulating the short-term load balance as probabilistic constraints in order to hedge against potential extreme wind power scenarios. The risk-averse scheme is embedded in the stochastic dual dynamic programming framework. Simulation results for a case study on a real industrial setting show that hedging the system against the short-term volatility of wind power contributes to mitigating the risk of excessive operations costs or load curtailments, and that the consideration of the decision maker risk profile contributes to decreasing the variability of the solutions. In addition, the results of the application also illustrate the potential of the scheme to assess the energy situation of a country or a region under the penetration of wind energy and batteries deployment. © 2019 Elsevier Ltd

The efficient planning of radial distribution feeders is important to ensure energy supply to load demands as suitable quality of the service is provided. This planning is usually separated into two decision-making stages, i.e., design of topology infrastructure and protection system shaping. Moreover, these stages are performed pursuing the achievement of multiple objectives such as costs minimization and reliability maximization. In consequence, a multi-stage and multi-objective optimization problem arises. In this paper, an efficient Pareto-based approach is proposed to solve the aforementioned kind of optimization. Infrastructure preparation considers possible interconnections between loads and installation of Distributed Generation (DG). Furthermore, the shaping of protection system considers the amount of devices as their efficient placement in the feeder. DG have been largely considered as an alternative to increase the operation performance of distribution systems (DSs), besides its merely function to provide energy. On the other hand, Non-Dominated Sorting Genetic Algorithm (NSGA-II) is implemented to solve each stage of the planning. Finally, simulations are applied on a 33-nodes test feeder with available sources to install DG

Planning the expansion of distribution systems (DS) is a nonlinear and combinatorial problem that combines technical and regulatory constraints. Commonly, the planning of DS is intended to achieve a radial topology to reduce its complexity. However, in a smart-grid context, distribution feeders are subject to reconfigurations to transfer load between feeders in failure events, or to compensate for voltage profile and power quality issues caused by distributed generation (DG). In this paper we propose a novel methodology for the planning of primary feeders, which considers the DS performance in both open and closedloop arrangements. To this end we introduce the concept of reach current, which we use to define and solve a minimum power-loss flow problem. We solve this problem for each type of conductor considered to find a set of primary-feeder candidates. Additionally, an efficiency evaluation is performed to select the best among the candidate primary feeders. Simulation results on a test system show how this method is able to capture open and closed-loop operations, explicitly considering DG in the DS expansion planning within a smart-grid scheme.

The evolution of distribution systems (DS) towards the smart-grid concept posses new challenges, triggered by the integration of distributed generation (DG) and the installation of new devices. These challenges raise the need to reconsider the traditional network operation during the planning stage, enabling the DS to be flexible to operate under different network configuration scenarios. In this paper we propose a DS planning methodology for the connection of support feeders in radial networks, explicitly considering reconfiguration options with open-and closed-loop operation. To this end, we propose an efficiency evaluation, based on Data Envelopment Analysis, to assess candidate feeders in terms of expansion costs, energy losses, and lines' chargeability, under a range of demand scenarios that include GD penetration. Additionally, we have developed a method to identify the main feeder in a radial system, obtaining a simplified version of the DS, better suited for analysis. Simulation results on a real urban DS show the effectiveness of the method to identify the best nodes in a main feeder to connect support feeders, further indicating how to divide the network into operation areas for an improved network performance.

Currently, the microgrid scheme is considered as the future of distribution feeders operation. Nevertheless, there are several impacts associated to its implementation, and the distribution system operator (DSO) requires a planning signal to decide whether to integrate these schemes. This paper proposes a methodology to quantify the technical impacts of the integration of some elements of a microgrid, i.e., distributed generation (DG) power plants and electric vehicles (EVs) charging stations. To that end, a methodology based on steady state and dynamic simulations is proposed. This methodology compares the performance of the business as usual (BAU) case with different microgrid scenarios. The output of this comparison is a global index that contains information related to voltage profiles, elements loading, power losses, short circuit currents, and transient stability. Simulations are applied on a real test feeder, considering BAU and three microgrid scenarios, obtaining the global index for each configuration.