Rechargeable lithium-ion batteries are promising candidates for building grid-level storage systems because of their high energy and power density, low discharge rate, and decreasing cost. A vital aspect in energy storage planning and operations is to accurately model the aging cost of battery cells, especially in irregular cycling operations. This paper proposes a semi-empirical lithium-ion battery degradation model that assesses battery cell life loss from operating profiles. We formulate the model by combining fundamental theories of battery degradation and our observations in battery aging test results. The model is adaptable to different types of lithium-ion batteries, and methods for tuning the model coefficients based on manufacturer's data are presented. A cycle-counting method is incorporated to identify stress cycles from irregular operations, allowing the degradation model to be applied to any battery energy storage (BES) applications. The usefulness of this model is demonstrated through an assessment of the degradation that a BES would incur by providing frequency control in the PJM regulation market.

This tutorial paper discusses some aspects of electricity markets from the perspective of the demand-side. It argues that increasing the short-run price elasticity of the demand for electrical energy would improve the operation of these markets. It shows, however, that enhancing this elasticity is not an easy task. The tools that consumers and retailers of electrical energy need to participate more actively and effectively in electricity markets are discussed. The paper also describes how consumers of electricity can take part in the provision of power system security.

This paper addresses the main challenges to the security constrained optimal power flow (SCOPF) computations. We first discuss the issues related to the SCOPF problem formulation such as the use of a limited number of corrective actions in the post-contingency states and the modeling of voltage and transient stability constraints. Then we deal with the challenges to the techniques for solving the SCOPF, focusing mainly on: approaches to reduce the size of the problem by either efficiently identifying the binding contingencies and including only these contingencies in the SCOPF or by using approximate models for the post-contingency states, and the handling of discrete variables. We finally address the current trend of extending the SCOPF formulation to take into account the increasing levels of uncertainty in the operation planning. For each such topic we provide a review of the state of the art, we identify the advances that are needed, and we indicate ways to bridge the gap between the current state of the art and these needs. * Corresponding author Email addresses: capitane@montefiore.ulg.ac.be (F. Capitanescu), camel@us.es (J.L. Martinez Ramos), patrick.panciatici@rte-france.com (P. Panciatici), kirschen@uw.edu (D. Kirschen), alejandromm@us.es (A. Marano Marcolini), ludovic.platbrood@gdfsuez.com (L. Platbrood), l.wehenkel@ulg.ac.be (L. Wehenkel) Preprint submitted to Electric Power Systems ResearchMay 2, 2011Keywords: mixed integer linear programming, mixed integer nonlinear programming, nonlinear programming, optimal power flow, security constrained optimal power flow MotivationThe SCOPF [1,2] is an extension of the OPF problem [3,4] which takes into account constraints arising from the operation of the system under a set of postulated contingencies. The SCOPF problem is a nonlinear, nonconvex, large-scale optimization problem, with both continuous and discrete variables [1,2]. The SCOPF belongs therefore to the class of optimization problems called Mixed Integer Non-Linear Programming (MINLP).The SCOPF has become an essential tool for many Transmission System Operators (TSOs) for the planning, operational planning, and real time operation of their system [5,6, 7,8]. Furthermore, in several electricity markets (e.g. PJM, New-England, California, etc.) the locational marginal prices calculated using a DC SCOPF are used to price electricity. This approach is also under consideration in other systems [9,10,11].Several papers discussing the challenges to the OPF problem were published during the 90's [5,6, 7,8]. Since then several important changes have taken place not only in power systems operation and control but also in mathematical programming:• Power systems operate today in conditions that are more "stressed" and were not foreseen at the planning stage. In particular the increase in load has not been supported by an adequate upgrade of the generation and transmission systems. Furthermore the creation of electricity markets has led to the trading of significant amounts of electrical energy over lo...

Because of the introduction of competition in the Since the introduction of competition in various countries electricity suppi industry, it has become much more im ortant around the world and the introduction of wheeling in North particular load, how much use each generator is &g of a forms. While the approaches which have been i m lemented are transmission line and what is each generator's contribution reasonable and reflect sound engineering @&emat, it is to the system losses. This a er describes a technique for probably fair to say that their scope is limited and that their answering these uestions wKi& is not limited to incremental application is not entirely satisfactory. In the United Kingdom changes and whic R is applicable to both active and reactive these issues were deemed too complex and were deliberately power. Starting from a power flow solution, the technique set aside. Consequently, a single non-geographically irst identifies the busses which are reached by power differentiated electricity market was created and generators roduced by each enerator. Then it determines the sets of are compensated if they are not allowed to produce due to &usses supplied %y the same generators. Using a transmission constraints[l]. Connection charges depend on proportionality assumption, it is then ossible to calculate the the location but are based on capacity and not energy. On the contribution of each generator to the yoads and flows. The other hand, the lon itudinal nature of the Chilean power applicability of the proposed technique is demonstrated using system makes possihe the introduction of the concept of a 30-bus example. influence area based on sensitivity analysis [2]. These areas of influence are used to allocate the cost of the transmission Keywords: power system operations, transmission access, system among the competin generators. In North America, the power flow, spot pricing, location-dependent pricing, power introduction of wholesie wheeling has led to the system economics. development of concepts such as "contract raths" and the ricing of transmission services based on MW-miles"[3]. This method is a plicable independently to both active and ;eactive power lows. In the following description, the t a m power" can be replaced by either "active power" or "reactive power" depending on the desired application.

Abstract-Scenario generation is an important step in the operation and planning of power systems with high renewable penetrations. In this work, we proposed a data-driven approach for scenario generation using generative adversarial networks, which is based on two interconnected deep neural networks. Compared with existing methods based on probabilistic models that are often hard to scale or sample from, our method is datadriven, and captures renewable energy production patterns in both temporal and spatial dimensions for a large number of correlated resources. For validation, we use wind and solar timesseries data from NREL integration data sets. We demonstrate that the proposed method is able to generate realistic wind and photovoltaic power profiles with full diversity of behaviors. We also illustrate how to generate scenarios based on different conditions of interest by using labeled data during training. For example, scenarios can be conditioned on weather events (e.g. high wind day, intense ramp events or large forecasts errors) or time of the year (e,g. solar generation for a day in July). Because of the feedforward nature of the neural networks, scenarios can be generated extremely efficiently without sophisticated sampling techniques.