A majority of remote power systems are going to be supplied by diesel-renewable resources such as wind and photovoltaic energy in the future. However, the unpredictable nature of wind generation increases the concern about the reliable operation of these isolated microgrids. Using energy storage systems (ESSs) is recently accepted as an efficient solution to the volatility and intermittency of renewable energy sources. In this paper, a stochastic programming based on the Monte Carlo approach is introduced for optimal planning of remote systems. So far, most literatures have focused exclusively on the energy storage initial sizing. However, capacity expansion of ESS through the time span can result in significant cost saving and will be illustrated in this paper. Factors such as reliability criteria together with the investment and the operation costs are taken into account in the proposed methodology. This method utilizes practical operational constraints of ESS including efficiency and life cycle. Considering life cycle constraint reinforces the proposed method to completely investigate the difference between ESS technologies. The results of case study demonstrate that the proposed capacity expansion algorithm could lead to about 10% more profit over the traditional energy storage sizing. Index Terms-Energy storage system (ESS), MonteCarlo, planning, wind generation. NOMENCLATURE A. Variables e s ess (t) Stored energy of energy storage system (ESS) at time t. E y ess Energy capacity of ESS in year y. I y Investment cost in year y. IC s y Interruption cost in year y for scenario s. LS s (t) Load shedding at time t for scenario s. LSINDX s (t) Load shedding index at time t for scenario s. M s y Maintenance cost in year y for scenario s. O s y Operation cost in year y for scenario s. OC W Operation cost of wind units. OMC ESS Operation and maintenance cost of ESS. P s ch (t) ESS power charging at time t for scenario s. P s dch (t) ESS power discharging at time t for scenario s. ). M. Bozorg is with the Power

Abstract-Inter-zonal trading in multi-area power system (MAPS) improves the market efficiency and the system reliability by sharing the resources (energy and reserve services) across zonal boundaries. Actually, each area can operate with less reserve resources than would normally be required for isolated operation. The aim of this work is to propose a model that includes the problem of optimal spinning reserve (SR) provision into the security constraint unit commitment (SCUC) formulation based on the reliability criteria for a MAPS. The loss of load probability (LOLP) and the expected load not served (ELNS) are evaluated as probabilistic metrics in the case of a multi-control zone power system. Moreover, we demonstrate how these criteria can be explicitly incorporated into the market-clearing formulation. The non-coincidental nature of spinning reserve requirement across the zonal boundary is effectively modeled. Two system cases including a small-scale (six-bus) test system and the IEEE reliability test system (IEEE-RTS) are used to demonstrate the effectiveness of the presented model. Index Terms-Multi-control zone, probabilistic approach, reliability metrics, spinning reserve, unit commitment.

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Voltage stability imposes important limitations on the power systems operation. Adequate voltage stability margin needs to be obtained through the appropriate scheduling of the reactive power resources. The main countermeasures against voltage instability could be distinctly classified into preventive and corrective control actions. This paper proposes a preventive countermeasure to improve the voltage stability margin through the management of the reactive power and its reserve. The voltage and reactive power management is studied from the generator's point of view to maximize effective generator reactive power reserve (EGRPR). Detailed model of the generators including the armature and field current limits, as well as the switch mode between the voltage control and the reactive power limitations are considered to maximize the reactive power capability of the generators in emergency states. One-stage and two-stage optimization approaches are utilized to find the optimum solution. The proposed optimization procedure is applied on a 6-bus system and the New England 39-bus system to illustrate the effectiveness of the method.

In this paper, we propose a distributionally robust chance constrained (DRCC) optimization problem for the operation of an active distribution network (ADN). The ADN's operator uses the proposed problem to centrally optimize the dispatch plan of his resources, namely photovoltaic (PV) and battery energy storage (BES) systems, and to participate in wholesale real/reactive power and flexibility markets. We model the uncertainties in the problem by knowing a set of probability distributions, i.e., an ambiguity set. The uncertainties include production capability of PV systems, end-users' consumption, requested flexibility by the external network's operator, and voltage magnitude at the point of common coupling (PCC). The resulting formulation is a DRCC optimization problem for which a solution methodology based on freely available solvers is presented. We evaluate the performance of proposed solution in the numerical results section by comparing it with two benchmark models based on stochastic and chance constrained (CC) optimization.

Photovoltaic (PV) systems are widely spread across MV and LV distribution systems and the penetration of PV generation is solidly growing. Because of the uncertain nature of the solar energy resource, PV power forecasting models are crucial in any energy management system for smart distribution networks. Although point forecasts can suit many scopes, probabilistic forecasts add further flexibility to an energy management system and are recommended to enable a wider range of decision making and optimization strategies. This paper proposes methodology towards probabilistic PV power forecasting based on a Bayesian bootstrap quantile regression model, in which a Bayesian bootstrap is applied to estimate the parameters of a quantile regression model. A novel procedure is presented to optimize the extraction of the predictive quantiles from the bootstrapped estimation of the related coefficients, raising the predictive ability of the final forecasts. Numerical experiments based on actual data quantify an enhancement of the performance of up to 2.2% when compared to relevant benchmarks.