multi-stage stochastic programming

Optimal participation of energy communities in electricity markets under uncertainty. A multi-stage stochastic programming approach

  • Albert Solà Vilalta
  • Ignasi Mañé Bosch
  • F.-Javier Heredia
    • Categories Energy, Stochastic Programming Tags , , ,

    We propose a multi-stage stochastic programming model for the optimal participation of energy communities in electricity markets. The multi-stage aspect captures the different times at which variable renewable generation and electricity prices are observed. This results in large-scale optimization problem instances containing large scenario trees with 34 stages, to which scenario reduction techniques are applied. … Read more

    MDP modeling for multi-stage stochastic programs

  • David P. Morton
  • Oscar Dowson
  • Bernardo K. Pagnoncelli
    • Categories Stochastic Programming Tags , , , ,

    We study a class of multi-stage stochastic programs, which incorporate modeling features from Markov decision processes (MDPs). This class includes structured MDPs with continuous state and action spaces. We extend policy graphs to include decision-dependent uncertainty for one-step transition probabilities as well as a limited form of statistical learning. We focus on the expressiveness of … Read more

    A Dynamic Strategic Plan for Transition to Campus-Scale Clean Electricity Using Multi-Stage Stochastic Programming

  • Ahmet Emir Şener
  • Burak Kocuk
  • Tuğçe Yüksel
    • Categories Energy, Stochastic Programming Tags , , ,

    The decarbonization of energy systems at energy-intensive sites is an essential component of global climate mitigation, yet such transitions involve substantial capital requirements, ongoing technological progress, and the operational complexities of renewable integration. This study presents a dynamic strategic planning framework that applies multi-stage stochastic programming to guide clean electricity transitions at the campus level. … Read more

    A Dynamic Strategic Plan for the Transition to a Clean Bus Fleet using Multi-Stage Stochastic Programming with a Case Study in Istanbul

  • Neman Karimi
  • Burak Kocuk
  • Tuğçe Yüksel
    • Categories Energy, Stochastic Programming, Transportation Tags , , ,

    In recent years, the transition to clean bus fleets has accelerated. Although this transition might bring environmental and economic benefits, it requires a long-term strategic plan due to the large investment costs involved. This paper proposes a multi-stage stochastic program to optimize strategic plans for the clean bus fleet transition that explicitly considers the uncertainty … Read more

    Sampling-based Decomposition Algorithms for Multistage Stochastic Programming

  • Harsha Gangammanavar
    • Categories Stochastic Programming Tags , , ,

    Sampling-based algorithms provide a practical approach to solving large-scale multistage stochastic programs. This chapter presents two alternative approaches to incorporating sampling within multistage stochastic linear programming algorithms. In the first approach, sampling is used to construct a sample average approximation (SAA) of the true multistage program. Subsequently, an optimization step is undertaken using deterministic decomposition … Read more

    Compromise Policy for Multi-stage Stochastic Linear Programming: Variance and Bias Reduction

  • Jiajun Xu
  • Suvrajeet Sen
    • Categories Applications - Science and Engineering, Dynamic Programming, Stochastic Programming Tags , , , , ,

    This paper focuses on algorithms for multi-stage stochastic linear programming (MSLP). We propose an ensemble method named the “compromise policy”, which not only reduces the variance of the function approximation but also reduces the bias of the estimated optimal value. It provides a tight lower bound estimate with a confidence interval. By exploiting parallel computing, … Read more

    Markov Chain-based Policies for Multi-stage Stochastic Integer Linear Programming with an Application to Disaster Relief Logistics

  • Margarita P Castro
  • Merve Bodur
  • Yongjia Song
    • Categories (Mixed) Integer Linear Programming, Stochastic Programming Tags , , , ,

    We introduce an aggregation framework to address multi-stage stochastic programs with mixed-integer state variables and continuous local variables (MSILPs). Our aggregation framework imposes additional structure to the integer state variables by leveraging the information of the underlying stochastic process, which is modeled as a Markov chain (MC). We demonstrate that the aggregated MSILP can be … Read more

    Risk-averse Regret Minimization in Multi-stage Stochastic Programs

  • Mehran Poursoltani
  • Erick Delage
  • Angelos Georghiou
    • Categories Applications - OR and Management Sciences, Robust Optimization, Stochastic Programming Tags , , ,

    Within the context of optimization under uncertainty, a well-known alternative to minimizing expected value or the worst-case scenario consists in minimizing regret. In a multi-stage stochastic programming setting with a discrete probability distribution, we explore the idea of risk-averse regret minimization, where the benchmark policy can only benefit from foreseeing Delta steps into the future. … Read more

    The Value of Coordination in Multi-Market Bidding of Grid Energy Storage

  • Nils Löhndorf
  • David Wozabal
    • Categories Applications - OR and Management Sciences, Stochastic Programming Tags , , , , ,

    We consider the problem of a storage owner who trades in a multi-settlement electricity market comprising an auction-based day-ahead market and a continuous intraday market. We show in a stylized model that a coordinated policy that reserves capacity for the intraday market is optimal and that the gap to a sequential policy increases with intraday … Read more

    Confidence Interval Software for Multi-stage Stochastic Programs

  • Xiaotie Chen
  • Sylvain Cazaux
  • Brian Knight
  • David L. Woodruff
    • Categories Stochastic Programming Tags , ,

    When the uncertainty is explicitly modeled in an optimization problem, it is often necessary to use samples to compute a solution, which gives rise to a need to compute confidence intervals around the objective function value that is obtained. In this paper we describe software that implements well-known methods for two stage problems and we … Read more