298 research outputs found
Large-scale unit commitment under uncertainty: an updated literature survey
The Unit Commitment problem in energy management aims at finding the optimal production schedule of a set of generation units, while meeting various system-wide constraints. It has always been a large-scale, non-convex, difficult problem, especially in view of the fact that, due to operational requirements, it has to be solved in an unreasonably small time for its size. Recently, growing renewable energy shares have strongly increased the level of uncertainty in the system, making the (ideal) Unit Commitment model a large-scale, non-convex and uncertain (stochastic, robust, chance-constrained) program. We provide a survey of the literature on methods for the Uncertain Unit Commitment problem, in all its variants. We start with a review of the main contributions on solution methods for the deterministic versions of the problem, focussing on those based on mathematical programming techniques that are more relevant for the uncertain versions of the problem. We then present and categorize the approaches to the latter, while providing entry points to the relevant literature on optimization under uncertainty. This is an updated version of the paper "Large-scale Unit Commitment under uncertainty: a literature survey" that appeared in 4OR 13(2), 115--171 (2015); this version has over 170 more citations, most of which appeared in the last three years, proving how fast the literature on uncertain Unit Commitment evolves, and therefore the interest in this subject
Adaptive Robust Optimization with Dynamic Uncertainty Sets for Multi-Period Economic Dispatch under Significant Wind
The exceptional benefits of wind power as an environmentally responsible
renewable energy resource have led to an increasing penetration of wind energy
in today's power systems. This trend has started to reshape the paradigms of
power system operations, as dealing with uncertainty caused by the highly
intermittent and uncertain wind power becomes a significant issue. Motivated by
this, we present a new framework using adaptive robust optimization for the
economic dispatch of power systems with high level of wind penetration. In
particular, we propose an adaptive robust optimization model for multi-period
economic dispatch, and introduce the concept of dynamic uncertainty sets and
methods to construct such sets to model temporal and spatial correlations of
uncertainty. We also develop a simulation platform which combines the proposed
robust economic dispatch model with statistical prediction tools in a rolling
horizon framework. We have conducted extensive computational experiments on
this platform using real wind data. The results are promising and demonstrate
the benefits of our approach in terms of cost and reliability over existing
robust optimization models as well as recent look-ahead dispatch models.Comment: Accepted for publication at IEEE Transactions on Power System
Reliability-Constrained Economic Dispatch with Analytical Formulation of Operational Risk Evaluation
Operational reliability and the decision-making process of economic dispatch (ED) are closely related and important for power system operation. Consideration of reliability indices and reliability constraints together in the operation problem is very challenging due to the problem size and tight reliability constraints. In this paper, a comprehensive reliability-constrained economic dispatch model with analytical formulation of operational risk evaluation (RCED-AF) is proposed to tackle the operational risk problem of power systems. An operational reliability evaluation model considering the ED decision is designed to accurately assess the system behavior. A computation scheme is also developed to achieve efficient update of risk indices for each ED decision by approximating the reliability evaluation procedure with an analytical polynomial function. The RCED-AF model can be constructed with decision-dependent reliability constraints expressed by the sparse polynomial chaos expansion. Case studies demonstrate that the proposed RCED-AF model is effective and accurate in the optimization of the reliability and the cost for day-ahead economic dispatch
Evaluating Stochastic Methods in Power System Operations with Wind Power
Wind power is playing an increasingly important role in electricity markets.
However, it's inherent variability and uncertainty cause operational challenges
and costs as more operating reserves are needed to maintain system reliability.
Several operational strategies have been proposed to address these challenges,
including advanced probabilistic wind forecasting techniques, dynamic operating
reserves, and various unit commitment (UC) and economic dispatch (ED)
strategies under uncertainty. This paper presents a consistent framework to
evaluate different operational strategies in power system operations with
renewable energy. We use conditional Kernel Density Estimation (KDE) for
probabilistic wind power forecasting. Forecast scenarios are generated
considering spatio-temporal correlations, and further reduced to lower the
computational burden. Scenario-based stochastic programming with different
decomposition techniques and interval optimization are tested to examine
economic, reliability, and computational performance compared to deterministic
UC/ED benchmarks. We present numerical results for a modified IEEE-118 bus
system with realistic system load and wind data
Stochastic Optimization for Integrated Energy System with Reliability Improvement Using Decomposition Algorithm
As energy demands increase and energy resources change, the traditional energy system has been upgraded and reconstructed for human society development and sustainability. Considerable studies have been conducted in energy expansion planning and electricity generation operations by mainly considering the integration of traditional fossil fuel generation with renewable generation. Because the energy market is full of uncertainty, we realize that these uncertainties have continuously challenged market design and operations, even a national energy policy. In fact, only a few considerations were given to the optimization of energy expansion and generation taking into account the variability and uncertainty of energy supply and demand in energy markets. This usually causes an energy system unreliable to cope with unexpected changes, such as a surge in fuel price, a sudden drop of demand, or a large renewable supply fluctuation. Thus, for an overall energy system, optimizing a long-term expansion planning and market operation in a stochastic environment are crucial to improve the system\u27s reliability and robustness. As little consideration was paid to imposing risk measure on the power management system, this dissertation discusses applying risk-constrained stochastic programming to improve the efficiency, reliability and economics of energy expansion and electric power generation, respectively. Considering the supply-demand uncertainties affecting the energy system stability, three different optimization strategies are proposed to enhance the overall reliability and sustainability of an energy system. The first strategy is to optimize the regional energy expansion planning which focuses on capacity expansion of natural gas system, power generation system and renewable energy system, in addition to transmission network. With strong support of NG and electric facilities, the second strategy provides an optimal day-ahead scheduling for electric power generation system incorporating with non-generation resources, i.e. demand response and energy storage. Because of risk aversion, this generation scheduling enables a power system qualified with higher reliability and promotes non-generation resources in smart grid. To take advantage of power generation sources, the third strategy strengthens the change of the traditional energy reserve requirements to risk constraints but ensuring the same level of systems reliability In this way we can maximize the use of existing resources to accommodate internal or/and external changes in a power system. All problems are formulated by stochastic mixed integer programming, particularly considering the uncertainties from fuel price, renewable energy output and electricity demand over time. Taking the benefit of models structure, new decomposition strategies are proposed to decompose the stochastic unit commitment problems which are then solved by an enhanced Benders Decomposition algorithm. Compared to the classic Benders Decomposition, this proposed solution approach is able to increase convergence speed and thus reduce 25% of computation times on the same cases
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