Abstract:We present a simulation and multi-objective optimization framework for the integration of renewable generators and storage devices into an electrical distribution network. The framework searches for the optimal size and location of the distributed renewable generation units (DG). Uncertainties in renewable resources availability, components failure and repair events, loads and grid power supply are incorporated. A Monte Carlo simulation -optimal power flow (MCS-OPF) computational model is used to generate scenarios of the uncertain variables and evaluate the network electric performance. As a response to the need of monitoring and controlling the risk associated to the performance of the optimal DG-integrated network, we introduce the conditional valueat-risk (CVaR) measure into the framework. Multi-objective optimization (MOO) is done with respect to the minimization of the expectations of the global cost (C g ) and energy not supplied (ENS) combined with their respective CVaR values. The multi-objective optimization is performed by the fast non-dominated sorting genetic algorithm NSGA-II. For exemplification, the framework is applied to a distribution network derived from the IEEE 13 nodes test feeder. The results show that the MOO MCS-OPF framework is effective in finding an optimal DG-integrated network considering multiple sources of uncertainties. In addition, from the perspective of decision making, introducing the CVaR as a measure of risk enables the evaluation of trade-offs between optimal expected performances and risks.

Abstract-A key assumption made in this paper is that electric vehicle (EV) battery charging profiles are rectangular. This requires a specific and new formulation of the charging problem, involving discrete action sets for the EVs in particular. The considered cost function comprises of three components: the distribution transformer aging, the distribution energy losses, and a component inherent to the EV itself (e.g., the battery charging monetary cost). Charging start times are determined by the proposed distributed algorithm, whose analysis is conducted by using game-theoretic tools such as ordinal potential games. Convergence of the proposed algorithm is shown to be guaranteed for some important special cases. Remarkably, the performance loss w.r.t. the centralized solution is shown to be small. Simulations, based on realistic public data, allow one to gain further insights on the issues of convergence and optimality loss and provide clear messages about the tradeoff associated with the presence of the three components in the considered cost function. While simulations show that the proposed charging policy performs quite similarly to existing (continuous) charging policies such as valley-filling-type solutions when the non-EV demand forecast is perfect, they reveal an additional asset of rectangular profiles in presence of forecasting errors.

Abstract-This paper investigates a decentralized optimization methodology to coordinate Electric Vehicles (EV) charging in order to contribute to the voltage control on a residential electrical distribution feeder. This aims to maintain the voltage level in function of the EV's power injection using the sensitivity matrix approach. The decentralized optimization is tested with two different methods, respectively global and local, when EV take into account their impact on all the nodes of the network or only on a local neighborhood of their connection point. EV can also update their decisions asynchronously or synchronously. While only the global approach with asynchronous update is theoretically proven to converge, using results from game theory, simulations show the potential of other algorithms for which fewer iterations or fewer informations are necessary. Finally, using Monte Carlo simulations over a wide range of EV localization configurations, the first analysis have also shown a promising performance in comparison with uncoordinated charging or with a "voltage droop charging control" recently proposed in the literature.

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