Evenly convex sets, and evenly quasiconvex functions, revisited

Since its appearance, even convexity has become a remarkable notion in convex analysis. In the fifties, W. Fenchel introduced the evenly convex sets as those sets solving linear systems containing strict inequalities. Later on, in the eighties, evenly quasiconvex functions were introduced as those whose sublevel sets are evenly convex. The significance of even convexity relies on the different areas where it enjoys applications, ranging from convex optimization to microeconomics. In this paper, we review some of the main properties of evenly convex sets and evenly quasiconvex functions, provide further characterizations of evenly convex sets, and present some new results for evenly quasiconvex functions.This research has been partially supported by MINECO of Spain and ERDF of EU, Grants PGC2018-097960-B-C22 and ECO2016-77200-P

準凸計画問題に対する劣微分を用いた最適性条件 (非線形解析学と凸解析学の研究)

本講究録では, 準凸計画問題に対する劣微分を用いた最適性条件について述べる. 特に近年筆者等によって示された, essentially quasiconvex programmingに対する最適性の必要十分条件, 一般の準凸計画問題に対する最適性の必要十分条件, 逆準凸制約を持つ準凸計画問題に対する最適性の必要条件について述べる

準凸計画間題に対するKKT条件と制約想定 (非線形解析学と凸解析学の研究)

本講究録では，準凸計画問題に対するKKT条件と制約想定について述べる．特に近年筆者によって示された， essentially quasiconvex programmingに対するGP劣微分と生成集合を用いた最適性の必要十分条件，一般の準凸計画問題に対するM劣微分と生成集合を用いた最適性の必要十分条件について述べる．特に既存の研究との関連や証明のアイディア等について詳細に述べる

Stochastic Optimal Control of Grid-Level Storage

The primary focus of this dissertation is the design, analysis and implementation of stochastic optimal control of grid-level storage. It provides stochastic, quantitative models to aid decision-makers with rigorous, analytical tools that capture high uncertainty of storage control problems. The first part of the dissertation presents a $p$-periodic Markov Decision Process (MDP) model, which is suitable for mitigating end-of-horizon effects. This is an extension of basic MDP, where the process follows the same pattern every $p$ time periods. We establish improved near-optimality bounds for a class of greedy policies, and derive a corresponding value-iteration algorithm suitable for periodic problems. A parallel implementation of the algorithm is provided on a grid-level storage control problem that involves stochastic electricity prices following a daily cycle. Additional analysis shows that the optimal policy is threshold policy. The second part of the dissertation is concerned with grid-level battery storage operations, taking battery aging phenomenon (battery degradation) into consideration. We still model the storage control problem as a MDP with an extra state variable indicating the aging status of the battery. An algorithm that takes advantage of the problem structure and works directly on the continuous state space is developed to maximize the expected cumulated discounted rewards over the life of the battery. The algorithm determines an optimal policy by solving a sequence of quasiconvex problems indexed by a battery-life state. Computational results are presented to compare the proposed approach to a standard dynamic programming method, and to evaluate the impact of refinements in the battery model. Error bounds for the proposed algorithm are established to demonstrate its accuracy. A generalization of price model to a class of Markovian regime-switching processes is also provided. The last part of this dissertation is concerned with how the ownership of energy storage make an impact on the price. Instead of one player in most storage control problems, we consider two players (consumer and supplier) in this market. Energy storage operations are modeled as an infinite-horizon Markov Game with random demand to maximize the expected discounted cumulated welfare of different players. A value iteration framework with bimatrix game embedded is provided to find equilibrium policies for players. Computational results show that the gap between optimal policies and obtained policies can be ignored. The assumption that storage levels are common knowledge is made without much loss of generality, because a learning algorithm is proposed that allows a player to ultimately identify the storage level of the other player. The expected value improvement from keeping the storage information private at the beginning of the game is then shown to be insignificant