In this paper, we study the long-term power generation investment expansion planing problem under uncertainty. We propose a bilevel optimization model that includes an upper-level multistage stochastic expansion planning problem and a collection of lower-level economic dispatch problem. This model seeks for the optimal sizing and siting for both thermal and wind power units to be built to maxim...

The transportation network design problem (NDP) with multiple objectives and demand uncertainty was originally formulated as a spectrum of stochastic multi-objective programming models in a bi-level programming framework. Solving these stochastic multi-objective NDP (SMONDP) models directly requires generating a family of optimal solutions known as the Pareto-optimal set. For practical implemen...

Microgrid is commonly regarded as an efficient way for integration of distributed generation (DG) in low voltage network. However, the integration method of microgrid in power system for maximum benefit needs to be further promoted. In this paper, a stochastic bidding strategy of microgrid in a joint dayahead market of energy and spinning reserve service is proposed taking into account of uncer...

In this paper we study the problem of simultaneous minimization of risks, and maximization of the terminal value of expected funds assets in a stochastic defined benefit aggregated pension plan. The risks considered are the solvency risk, measured as the variance of the terminal fund’s level, and the contribution risk, in the form of a running cost associated to deviations from the evolution of...

A novel data-driven stochastic robust optimization (DDSRO) framework is proposed for optimization under uncertainty leveraging labeled multi-class uncertainty data. Uncertainty data in large datasets are often collected from various conditions, which are encoded by class labels. Machine learning methods including Dirichlet process mixture model and maximum likelihood estimation are employed for...

This paper proposes a bi-level stochastic optimization problem (a Stackelberg game) to generate optimal bids for a profit maximizing hydropower producer and presents a mathematical approach to solve it. The first level represents the strategically acting hydropower producer also called a Stackelberg leader, while the second level represents the transmission system operator (TSO) also called a S...

The outbreak of COVID-19 disrupted our everyday life. Many local authorities enforced a cordon sanitaire for the protection sensitive areas. Travellers can only pass after tested. This paper aims to propose method design an on-ramp control scheme maximise urban freeway network throughput with predetermined queuing delay constraint at all off-ramps around sanitaire. A bi-level programming model ...

Due to continuous growth in electric demand and increasing connection of renewable energy, the power systems are being operated with smaller stability margins. Therefore, a sufficient loading margin is essential maintain system secure ensure voltage stability. To this end, paper studies joint optimization transmission capacity wind investment problem, unique feature which incorporating (VSM) pl...

Bi-level programming techniques are developed to handle decentralized problems with two-level decision makers, leaders and followers, who may have more than one objective to achieve. This paper proposes a λ-cut and goal programming based algorithm to solve fuzzy linear multiple objective bi-level (FLMOB) decision problems. First, based on the definition of a distance measure between two fuzzy v...