A stochastic mixed integer linear optimization scheduling model minimizing system operation costs and treating load and wind power production as stochastic inputs is presented. The schedules are updated in a rolling manner as more up-to-date information becomes available. This is a fundamental change relative to day-ahead unit commitment approaches. The need for reserves dependent on forecast horizon and share of wind power has been estimated with a statistical model combining load and wind power forecast errors with scenarios of forced outages. The model is used to study operational impacts of future high wind penetrations for the island of Ireland. Results show that at least 6000 MW of wind (34% of energy demand) can be integrated into the island of Ireland without significant curtailment and reliability problems.

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Stochastic Optimization Model to Study the Operational Impacts of High Wind Penetrations in Ireland

In this paper, we study an optimal day-ahead price-based power scheduling problem for a community-scale microgrid (MG). The proposed optimization framework aims to balance between maximizing the expected benefit of the MG in the deregulated electricity market and minimizing the MG operation cost considering users' thermal comfort requirements and other system constraints. The power scheduling and bidding problem is formulated as a two-stage stochastic program where various system uncertainties are captured by using the Monte Carlo simulation approach. Our formulation is novel in that it can exploit the thermal dynamic characteristics of buildings to compensate for the variable and intermittent nature of renewable energy resources and enables us to achieve desirable tradeoffs for different conflicting design objectives. Extensive numerical results are presented to demonstrate the great benefits in exploiting the building thermal dynamics and the flexibility of the proposed scheduling method in achieving different practical design tradeoffs. We also investigate the impacts of different design and system parameters on the curtailment of renewable energy resources and the optimal expected profit of the MG.

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Optimal Bidding Strategy for Microgrids Considering Renewable Energy and Building Thermal Dynamics

https://re.public.polimi.it/bitstream/11311/1045961/2/Optimal%20design_main%20article-Martelli_11311-1045961.pdf

Optimal design of multi-energy systems with seasonal storage

Review of real-time electricity markets for integrating Distributed Energy Resources and Demand Response

This paper addresses the optimal bidding strategy problem of a commercial virtual power plant (CVPP), which comprises of distributed energy resources (DERs), battery storage systems (BSS), and electricity consumers, and participates in the day-ahead (DA) electricity market. The ultimate goal of the CVPP is the maximization of the DA profit in conjunction with the minimization of the anticipated real-time production and the consumption of imbalance charges. A three-stage stochastic bi-level optimization model is formulated, where the uncertainty lies in the DA CVPP DER production and load consumption, as well as in the rivals’ offer curves and real-time balancing prices. Demand response schemes are also incorporated into the virtual power plant (VPP) portfolio. The proposed bi-level model consists of an upper level that represents the VPP profit maximization problem and a lower level that represents the independent system operator (ISO) DA market-clearing problem. This bi-level optimization problem is converted into a mixed-integer linear programing model using the Karush–Kuhn–Tucker optimality conditions and the strong duality theory. Finally, the risk associated with the VPP profit variability is explicitly taken into account through the incorporation of the conditional value-at-risk metric. Simulations on the Greek power system demonstrate the applicability and effectiveness of the proposed model.

Optimal Offering Strategy of a Virtual Power Plant: A Stochastic Bi-Level Approach

This paper considers stochastic programming models for decision-making under uncertainty in the context of electricity markets. It provides a brief overview of modeling and solution techniques within a mathematical programming framework. Tutorial as well as recent references are provided. This paper provides the guidelines for discussion in a panel session organized by the authors on "Decision Making under Uncertainty in Electricity Markets", scheduled for the IEEE PES 2006 General Meeting.

/pdf/decision-making-under-uncertainty-in-electricity-markets-jv50g8qcut.pdf

L.A. Barroso

Papers

Decision Making Under Uncertainty in Electricity Markets