https://www.lef.wiwi.uni-due.de/fileadmin/fileupload/BWL-LEF/Seminarreihe/RWeron14_Essen.pdf

Electricity price forecasting: A review of the state-of-the-art with a look into the future

A variety of methods and ideas have been tried for electricity price forecasting (EPF) over the last 15 years, with varying degrees of success. This review article aims at explaining the complexity of available solutions, their strengths and weaknesses, and the opportunities and treats that the forecasting tools offer or that may be encountered. The paper also looks ahead and speculates on the directions EPF will or should take in the next decade or so. In particular, it postulates the need for objective comparative EPF studies involving (i) the same datasets, (ii) the same robust error evaluation procedures and (iii) statistical testing of the significance of the outperformance of one model by another.

Energy is vital for sustainable development of any nation – be it social, economic or environment. In the past decade energy consumption has increased exponentially globally. Energy management is crucial for the future economic prosperity and environmental security. Energy is linked to industrial production, agricultural output, health, access to water, population, education, quality of life, etc. Energy demand management is required for proper allocation of the available resources. During the last decade several new techniques are being used for energy demand management to accurately predict the future energy needs. In this paper an attempt is made to review the various energy demand forecasting models. Traditional methods such as time series, regression, econometric, ARIMA as well as soft computing techniques such as fuzzy logic, genetic algorithm, and neural networks are being extensively used for demand side management. Support vector regression, ant colony and particle swarm optimization are new techniques being adopted for energy demand forecasting. Bottom up models such as MARKAL and LEAP are also being used at the national and regional level for energy demand management.

Energy models for demand forecasting—A review

The Art and Science of Negotiation

Smart energy and smart energy systems

This paper presents a new uncertainty handling framework for optimal generation expansion planning (GEP) amalgamating the notions of single-stage and two-stage robust optimization (RO) The proposed multiyear robust GEP methodology, as a tractable mixed integer linear programming optimization problem, copes with the inherent planning uncertainties associated with forecasted electricity load demand, as well as estimated investment and operation costs through distribution-free bounded intervals producing polyhedral uncertainty sets The optimal generation expansion plan obtained from the proposed RO approach is immunized against worst-case planning uncertainties considering the adopted degree of conservatism for each uncertainty set Therefore, the proposed methodology is capable of controlling the robustness of the optimal investment schedule regarding the enforced planning uncertainties Simulation results demonstrate the efficacy and efficiency of the proposed RO framework throughout GEP studies

Two-Stage Robust Generation Expansion Planning: A Mixed Integer Linear Programming Model

This paper develops appropriate models of flexible ac transmission systems (FACTS) shunt-series controllers for multiobjective optimization and also presents a multiobjective optimization methodology to find the optimal location of FACTS shunt-series controllers. The objective functions are the total fuel cost, power losses, and system loadability with and without minimum cost of FACTS installation. The e-constraint approach is implemented for the multiobjective mathematical programming (MMP) formulation, including the FACTS shunt-series controllers (i.e., phase-shifting transformer (PST), hybrid flow controller (HFC), and unified power-flow controller (UPFC)). Simulation results are presented for the IEEE 14-bus system. The optimization method is numerically solved using Matlab and general algebraic modeling system (GAMS) software environments. The solution procedure uses nonlinear programming (NLP) and mixed-integer nonlinear programming (MINLP) to solve the optimal location and setting of FACTS incorporated in the optimal power-flow problem considering these objective functions and improving the power system operation. Furthermore, the results demonstrate that the HFC is outperformed by PST and UPFC from the analytical and technical point of views.

/pdf/multiobjective-optimal-location-of-facts-shunt-series-3t1dztoa5t.pdf

Ahad Kazemi

Papers

Two-Stage Robust Generation Expansion Planning: A Mixed Integer Linear Programming Model

Multiobjective Optimal Location of FACTS Shunt-Series Controllers for Power System Operation Planning

Day-ahead price forecasting in restructured power systems using artificial neural networks

Stochastic operational scheduling of distributed energy resources in a large scale virtual power plant

Distribution system resilience enhancement by microgrid formation considering distributed energy resources