Search

The power industry has introduced flexible ramping products (FRPs) to improve the operational flexibility that high penetration of renewables requires. However, FRPs are mainly provided by natural-gas-fired power generators, whose capability to provide ramping service is determined by the operational conditions of the natural gas system. We develop a power-gas coordination model that takes into account FRP requirement. Specifically, the power market represents the required energy-FRP co-optimization, while the natural-gas market is settled considering FRP-deployment gas constraints. A Lagrangian coordination scheme is used to identify a market equilibrium involving the two markets that are settled separately. Computations results on two test systems examine the impact of gas-system operating constraints on the delivery of FRPs. Additionally, sensitivity analyses with respect to gas-network bottlenecks, FRP price caps, and renewable penetration levels are presented.

DC microgrids have promising applications in renewable integration due to their better energy efficiency when connecting DC components. However, they might be unstable since many loads in a DC microgrid are regulated as constant power loads (CPLs) that have a destabilizing negative impedance effect. As a result, the state trajectory displacement caused by abrupt load changes or contingencies can easily lead to instability. Many existing works have been devoted to studying the region of attraction (ROA) of a DC microgrid, in which the system is guaranteed to be asymptotically stable. Nevertheless, existing work either focuses on using numerical methods for ROA approximations that generally have no performance guarantees or cannot ensure a desired ROA for a general DC microgrid. To close this gap, this paper develops an innovative control synthesis algorithm to make a general DC microgrid have a theoretically guaranteed ROA, for example, to cover the entirety of its operating range regarding state trajectories. We first study the nonlinear dynamics of a DC microgrid to derive a novel transient stability condition to rigorously certify whether a given operating range is a subset of the ROA; then, we formulate a control synthesis optimization problem to guarantee the condition’s satisfaction. This condition is a linear constraint, and the optimization problem resembles an optimal power flow problem and has a good computational behavior. Simulation case studies verify the validity of the proposed work.

This paper presents a hybrid column-and-constraint-generation augmented-Lagrangian algorithm to efficiently solve the robust security-constrained dynamic transmission expansion planning (TEP) problem. The column-and-constraint generation algorithm separates the TEP problem into a master problem and a set of subproblems decomposable by time period. Additionally, the computationally expensive master problem is decomposed into three computationally efficient sub-master problems: an upper-master quadratic problem, a middle-master quadratic unconstrained binary problem, and a lower-master quadratic unconstrained problem. A set of auxiliary variables are used to relax as real ones the binary variables corresponding to the status of candidate transmission lines enabling the master problem decomposition. The solutions of the three sub-master problems are coordinated using a three-block alternating direction method of multipliers algorithm to enforce binary variables to be binary. An initialization strategy based on load shedding is used to enhance the performance of the proposed algorithm. Simulation results on the IEEE 118-bus test system show the efficient performance of the proposed algorithm for solving security-constrained dynamic TEP problems.

Driven by the goals of clean energy and zero carbon emissions, the power industry is undergoing significant transformations. The rapid growth of diverse distributed energy resources (DERs) at grid edge such as rooftop photovoltaics (PVs) and electric vehicles is transforming the traditional centralized power grid management to a decentralized, bottom-up, and localized control paradigm. Establishing local and distribution-level electricity markets provides an effective solution to managing large amounts of small-scale DERs. New regulations such as the recent FERC Order 2222 in the U.S. open the door to DERs in the wholesale markets. Through coordinating the local and distribution-level markets with the transmission-level wholesale market, the DERs and prosumers can trade energy and flexibility locally with each other and meanwhile provide energy, flexibility and ancillary services to the bulk power grid. During this transition, there are many new technical challenges to address, calling for innovative ideas and interdisciplinary research in this promising direction. Advanced information and communication technologies (ICT) are needed, as a key enabler, for the development and practical implementation of local and distribution electricity markets. Research into local and distribution markets is strongly interdisciplinary, involving the state of the art in power engineering, economics, and digital/information technology. A broad spectrum of contributors from universities, industry, research laboratories and policy makers is sought to develop and present solutions and technologies that will facilitate and advance practical applications and implementations of local and distribution-level electricity markets to uncover the values of DERs.

We consider a distribution system including a supplying utility, prosumers, and conventional consumers, interacting through a distribution market. In such a market, each prosumer, seeking its maximum profit, uses an optimization problem to determine its hourly production/consumption, and the distribution market operator uses a minimum social-cost problem from which hourly locational marginal prices (LMPs) are derived. Conventional consumers accept these prices and the supplying utility sell energy at given prices at the substation bus. Since these problems (those of the prosumers and the one of the operator) are interdependent, they need to be jointly considered and solved, which constitutes an equilibrium problem. We show that such equilibrium problem is equivalent to an optimization one, solve it, and analyze outcomes using a realistic distribution system. We extract conclusions of the potential impact of this distribution market on the financial outcomes of the utility, the prosumers and the conventional consumers.

To link to full-text access for this article, visit this link: http://dx.doi.org/10.1016/j.cor.2005.05.035 Byline: Enrique Castillo (a), Antonio J. Conejo (b), Rosa Eva Pruneda (c), Cristina Solares (c) Abstract: This paper deals with the problem of observability of linear systems of equations and inequalities in which some of the unknowns and some of the right-hand side constants are known and one seeks the subset of the remaining unknowns and constants that can be uniquely determined. A general methodology for solving this problem, first in the case of linear equations and finally the case of inequalities, is presented. The proposed methods are illustrated by their application to the telephone network problem. Author Affiliation: (a) Department of Applied Mathematics and Computational Sciences, University of Cantabria, 39005 Santander, Spain (b) Department of Electrical Engineering, University of Castilla-La Mancha, 13071 Ciudad Real, Spain (c) Department of Applied Mathematics, University of Castilla-La Mancha, 13071 Ciudad Real, Spain

In this article, we discuss how energy storage (behind the meter or otherwise) improves the performance of industrial and public distribution systems in various ways. We focus on large industrial or commercial electricity consumers and the corresponding supplying utility. Note that this article does not represent any guidance, policy, or preference from any specific electric utility [i.e., American Electric Power (AEP)].

We propose a novel hybrid method to solve the network-constrained stochastic unit commitment problem. We target realistic large-scale instances including hundreds of thermal generation units, thousands of transmission lines and nodes, and a large number of stochastic renewable generation units. This scheduling problem is formulated as a two-stage stochastic programming problem with continuous and binary variables in the first stage and only continuous variables in the second stage. We develop a hybrid solution method that decomposes the original problem into a master problem including unit commitment and dispatch decisions, and decomposed subproblems representing dispatch with transmission constraints per scenario. The proposed decomposition embeds a column-and-constraint generation step within the traditional Benders decomposition framework. The performance of the proposed decomposition technique is contrasted with the solution of the extensive form via branch-and-cut and Benders decomposition available in commercial solvers, and with conventional Benders decomposition variants. Our computational experiments show that the proposed method generates bounds of superior quality and finds solutions for instances where other approaches fail.

Due to pressing environmental concerns, there is a global consensus to commit to a sustainable energy future. Germany has embraced Energiewende, a bold sustainable energy policy of no operational nuclear plants by 2022. California has set an ambitious goal that mandates 50% renewable penetration by 2025, 60% by 2030, and 100% by 2045 [1]. The vast integration of renewable energy into the power grid imposes daunting challenges on the conventional supply-side control paradigm. First, renewable energy is intermittent and uncertain. Integrating renewable energy will reduce the system inertia, inject undesirable variability, and substantially increase the need for system reserves. These reserves are typically provided by conventional generators. However, conventional generators have limited capacity and ramping rate, and they produce carbon emissions that defeat the carbon benefit of renewable integration. On the other hand, the increasing penetration of renewable energy also squeezes the response time needed to balance the power grid. That is, the time required to make critical operating decisions is decreasing from minutes to seconds and, in some cases, even subseconds due to the increasing variability of supply and demand. The shorter response time for decision making places significant challenges on the conventional power grid, which requires human interaction.

This paper presents a comprehensive and detailed study of an optimization-based approach to identify and analyze saddle-node bifurcations (SNBs) and limit-induced bifurcations (LIBs) of a power system model, which are known to be directly associated with voltage stability problems in these systems. Theoretical studies are presented, formally demonstrating that solution points obtained from an optimization model, which is based on complementarity constraints used to properly represent generators' voltage controls, correspond to either SNB or LIB points of this model. These studies are accomplished by proving that optimality conditions of these solution points yield the transversality conditions of the corresponding bifurcation points. A simple but realistic test system is used to numerically illustrate the theoretical discussions. [ABSTRACT FROM AUTHOR]

•Efficient computational strategies for a sample average approximation are proposed.•A virtual power plant operation is modeled using stochastic programming formulations.•The impact of the sample size on the variability of the results is quantified.•The sample size has more influence on the results for worst-case formulations.•Adequate sample sizes lead to solutions close to the optimal one.