Probabilistic electric load forecasting: A tutorial review

This paper focuses on the role of networked microgrids as distributed systems for enhancing the power system resilience against extreme events. Resilience is an intrinsically complex property which requires deep understanding of microgrid operation in order to respond effectively in emergency conditions. The paper first introduces the definition and offers a generic framework for analyzing the power system resilience. The notion that large power systems can achieve a higher level of resilience through the deployment of networked microgrids is discussed in detail. In particular, the management of networked microgrids for riding through extreme events is analyzed. In addition, the merits of advanced information and communication technologies (ICTs) in microgrid-based distributed systems that can support the power system resilience are presented. The paper also points out the challenges for expanding the role of distributed systems and concludes that networked microgrids in particular provide a universal solution for improving the resilience against extreme events in Smart Cities.

Networked Microgrids for Enhancing the Power System Resilience

WHILE the introduction of statistical methods into the analysis of aeronautical experimental data, whether for quality control in production, for the interpretation of the results of structural and aerodynamic laboratory experiments, or for airline operation, has been brought about only in recent years, it may by now be fair to assert that their advantages and even their indispensability are no longer in dispute. Hitherto, investigations on these lines have usually involved, explicitly or implicitly, only the ‘normal curve of error’ and allied considerations; owing, it may be thought, to the controllability of the various manufacturing or laboratory techniques, but also perhaps to the scarcity of data hitherto available. It may well be, however, that with the accumulation of information arising out of investigations planned with particular reference to the statistical analysis of their results the whole range of the apparatus for statistical analysis, usually confined to such fields as those of biology or economics, will be called into full play.

The Statistical Analysis, of Experimental Data: A Fresh Approach Employing a Graphical Method for Investigating Non‐Normal Frequency Distributions

A recent article ‘Burden of proof: A comprehensive review of the feasibility of 100% renewable-electricity systems’ claims that many studies of 100% renewable electricity systems do not demonstrate sufficient technical feasibility, according to the criteria of the article's authors (henceforth ‘the authors’). Here we analyse the authors’ methodology and find it problematic. The feasibility criteria chosen by the authors are important, but are also easily addressed at low economic cost, while not affecting the main conclusions of the reviewed studies and certainly not affecting their technical feasibility. A more thorough review reveals that all of the issues have already been addressed in the engineering and modelling literature. Nuclear power, which the authors have evaluated positively elsewhere, faces other, genuine feasibility problems, such as the finiteness of uranium resources and a reliance on unproven technologies in the medium- to long-term. Energy systems based on renewables, on the other hand, are not only feasible, but already economically viable and decreasing in cost every year.

/pdf/response-to-burden-of-proof-a-comprehensive-review-of-the-gqgyr74h94.pdf

Response to 'Burden of proof': A comprehensive review of the feasibility of 100% renewable-electricity systems'

This paper proposes a robust optimization approach for optimal operation of microgrids. The uncertain output variation of renewable energy sources (RESs) is addressed by collaboratively scheduling of energy storage (ES) and direct load control (DLC) through a two-stage complementary framework: an hour-ahead charging/discharging of ES and a quarter-hour-ahead activation of DLC. The objective is to maximize the total profit of the microgrid considering operation and maintenance costs of ES units, wind turbines and photovoltaics, and transaction with main grid and customer loads. Assuming the power output of RES randomly varies within a bounded uncertainty set, the problem is modeled to a two-stage robust optimization model and solved by a column-and-constraint generation algorithm. Compared with conventional operation methods, the ES and DLC are coordinated in different time-scales, and RES uncertainties are fully addressed during operation decision-making, ensuring the solutions to be optimal and robust for any realization of uncertainty. The proposed methodology is verified on the IEEE 33-bus distribution system through a wide range of different tests.

Robust Operation of Microgrids via Two-Stage Coordinated Energy Storage and Direct Load Control

Power system congestion is a major problem that the system operator (SO) would face in the post-deregulated era. Therefore, investigation of techniques for congestion-free wheeling of power is of paramount interest. One of the most practiced and an obvious technique of congestion management is rescheduling the power outputs of generators in the system. However, all generators in the system need not take part in congestion management. Development of sound formulation and appropriate solution technique for this problem is aimed in this paper. Contributions made in the present paper are twofold. Firstly a technique for optimum selection of participating generators has been introduced using generator sensitivities to the power flow on congested lines. Secondly this paper proposes an algorithm based on particle swarm optimization (PSO) which minimizes the deviations of rescheduled values of generator power outputs from scheduled levels. The PSO algorithm, reported in this paper, handles the binding constraints by a technique different from the traditional penalty function method. The effectiveness of the proposed methodology has been analyzed on IEEE 30-bus and 118-bus systems and the 39 -bus New England system.

Optimal Rescheduling of Generators for Congestion Management Based on Particle Swarm Optimization

This paper addresses the optimal cable layout design of a collector system in a large-scale wind farm. The objective is the minimization of total trenching length which is the sum of lengths of all branches of the collector system tree. A graph-theoretic minimum spanning tree algorithm has been used as a starting algorithm, and improvements and modifications have been proposed to cater to the constraints and characteristics of a wind farm collector system. The contribution of this paper is three-fold. First, an algorithm has been proposed to further improve the results of the minimum spanning tree algorithm by creating external splice locations separate from the wind turbine locations in computing the cable layout configuration. Second, an algorithm has been proposed to compute the minimum trenching length layout configuration under the constraint of a prespecified maximum number of turbines connected to a feeder cable. Third, an algorithm has been developed to automatically compute the direction and magnitude of power flow on the different cables and to assign cable sizes accordingly.

Optimal Wind Farm Collector System Topology Design Considering Total Trenching Length

One of the main characteristics of wind power is the inherent variability and uncontrollability. Even with state-of-the art forecasting techniques, actual wind generation can be substantially deviated from the forecasted values. In addition, the system load is also variable and needs to be forecasted ahead of time for unit commitment and system planning and operation purposes. Although daily loads follow a pattern, every forecast is associated with a certain degree of uncertainty. In a system consisting of only wind generation and load, with minimum or no connection to the grid, the task of energy balancing is extremely difficult. Incorporation of energy storage units is being considered as a possible solution to this problem. However, energy storage units till date are expensive. Hence the question that arises is, given a generation-load system, what is the optimal amount of storage required. This paper investigates the storage size required by a system consisting of a wind farm and a load in meeting certain specified reliability indices. The optimization model is formulated as a stochastic linear programming problem considering two random quantities, load and wind generation. The minimum initial energy required in the storage unit is also computed. The reliability index of the system is evaluated in terms of the LOLP (Loss of Load Probability). Another reliability index, SGP (Spilled Generation Probability), is defined to determine the probability of a wind energy spillage event due to excess wind generation that cannot be accommodated in the system. A realistic example case is presented where the developed methodology is tested with satisfactory results.

Optimal storage sizing for integrating wind and load forecast uncertainties

The goal of achieving 20% wind power penetration by 2030 in the US has stimulated the installation of large scale wind farms in recent years, both on-shore and off-shore. Collector systems consolidate the power generated by turbine units distributed over the geographical area of the wind farm to a substation from where the generated power is transmitted to the electric grid. Design of a wind farm collector system must take into consideration the economics and reliability of operation. Most modern day large scale wind farms consist of hundreds of wind turbines and are generally electrically connected in a radial feeder cable configuration or daisy chains. While these configurations are generally accepted as convention, not much research has been done to analyze other cable layout configurations. This paper proposes a clustering based algorithm for cable layout of a large scale wind power plant. Comparison of the proposed method with the radial feeder cable configuration shows that real power losses in collector system are lowered and greater reliability is achieved with the proposed design. An economic analysis has also been done to compare the cost of generated energy associated with the proposed design and the conventional configuration.

/pdf/a-clustering-based-wind-farm-collector-system-cable-layout-p6e240xek9.pdf

A clustering based wind farm collector system cable layout design

Grid integration of wind power plants is complicated by wind variability and the technical characteristics of wind generators. The generation mix is evolving in today?s power system as conventional coal plants reach the end of their useful lives and are being displaced by cleaner technology such as wind and gas. At high levels of wind power penetration, the need for ancillary services increases, while the traditional resources that provide those services may become less available or economically feasible. Developing wind plant controls that allow performance more like that of conventional generation presents the opportunity for wind plants to provide a full range of ancillary services.

S. Dutta

Papers

Optimal Rescheduling of Generators for Congestion Management Based on Particle Swarm Optimization

Optimal Wind Farm Collector System Topology Design Considering Total Trenching Length

Optimal storage sizing for integrating wind and load forecast uncertainties

A clustering based wind farm collector system cable layout design

Serving the Future: Advanced Wind Generation Technology Supports Ancillary Services