The State-of-the-Art of the Short Term Hydro Power Planning with Large Amount of Wind Power in the System

Abstract-The amount of wind power is growing significantly in the world. Large scale introduction of wind power in the power system will increase the need for improved short term planning models of hydro power, because additional variations are introduced in the system. This huge amount of uncertainties in the power system will cause changes in the power market and there will be a value of advanced planning techniques, that will allow more flexibility in hydropower generation by taking into account stochastic nature of spot and regulating markets, water inflow, future water value and so on. The application of multi-stage stochastic optimization in the planning of the daily production of hydro power is not wholly discovered and requires further research. The complexity of the mathematical programming of the short term hydro power production including several type of uncertainty, while keeping the problem size solvable, challenges the power system researchers. This paper overviews the literature in the field of short term hydro power planning in power systems with large amount of wind power

A generation expansion planning model of a strategic electricity generating firm

This paper derives a mathematical structure for investment decisions of a profit-maximising and strategic producer in liberalised electricity markets. The paper assumes a Cournot producer in an energy market with nodal pricing regime. The Cournot producer is assumed to have revenue from selling energy to the pool. The investment problem of the strategic producer is modelled through a leader-follower game in applied mathematics. The leader is the strategic producer seeking the optimal mix of its investment technologies and the follower is a stochastic estimator. The stochastic estimator forecasts the reactions of other producers in the market in response to the investment decisions of the producer in question. The stochastic estimator takes the investment decisions of the producer and it calculates the stochastic prices. The mathematical structure is a stochastic linear bilevel programming problem. This problem is reformulated as a stochastic MILP problem which can be solved using the commercially available software packages. Finally, the developed mathematical structure is applied to a six-node example system to highlight the strengths of the whole approach.© 2011 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. QC 2012013

Models for conductor size selection in single wire earth return distribution networks

The use of the ground as the current return path often presents planning and operational challenges in power distribution networks. This study presents optimization-based models for the optimal selection of conductor sizes in Single Wire Earth Return (SWER) power distribution networks. By using mixed integer non-linear programming (MINLP), models are developed for both branch-wise and primary-lateral feeder selections from a discrete set of overhead conductor sizes. The models are based on a mathematical formulation of the SWER line, where the objective function is to minimize fixed and variable costs subject to constraints specific to SWER power flow. Load growth over different time periods is considered. The practical application is tested using a case study extracted from an existing SWER distribution line in Namibia. The results were consistent for different network operating scenarios

Recent Developments Regarding the Freedom of Information Act: A “Prologue to a Farce or a Tragedy; or, Perhaps Both”

The ongoing growth in wind power introduce huge amount of uncertainties to the power market. The stochastic nature of these power sources increases the need for the reserve power in real-time market. Having a flexible power source, hydropower producer can provide reserve power and increase its profit. Therefore, to build a planning model, which will allocate available capacity in different market places is an essential task for the price-taker hydropower producer. This paper uses optimal bidding model to the day-ahead market considering real-time balancing market under the uncertainties of the day-ahead and real-time market prices. Specifically, the model is built using stochastic linear programming approach. According to the results, for simultaneous bidding to day-ahead and real-time markets two extreme cases are happening. To make the bidding strategies more realistic and robust different novel approaches are modeled and assessed. Discussions on the results are provided and summarized.QC 20140110</p

A heuristic model for planning of single wire earth return power distribution systems

G. Bakkabulindi, M. R. Hesamzadeh, M. Amelin, I. P. Da Silva, and E. Lugujjo - A Heuristic Model for Planning of Single Wire Earth Return Power Distribution Systems - The IASTED International Conference on Power and Energy Systems and Applications, PESA, Pittsburgh, USA 7-9 November 2011The planning of distribution networks with earth return is highly dependent on the ground's electrical properties. This study incorporates a load flow algorithm for Single Wire Earth Return (SWER) networks into the planning of such systems. The earth's variable conductive properties are modelled into the load flow algorithm and the model considers load growth over different time periods. It includes optimal conductor selection for the SWER system and can also be used to forecast when an initially selected conductor will need to be upgraded. The planning procedure is based on indices derived through an iterative heuristic process that aims to minimise losses and investment costs subject to load flow constraints. A case study in Uganda is used to test the model's practical application

Spine Toolbox: A flexible open-source workflow management system with scenario and data management

The Spine Toolbox is open-source software for defining, managing, simulating and optimising energy system models. It gives the user the ability to collect, create, organise, and validate model input data, execute a model with selected data and finally archive and visualise results/output data. Spine Toolbox has been designed and developed to support the creation and execution of multivector energy integration models. It conveniently facilitates the linking of models with different scopes, or spatio-temporal resolutions, through the user interface. The models can be organised as a direct acyclic graph and efficiently executed through the embedded workflow management engine. The software helps users to import and manage data, define models and scenarios and orchestrate projects. It supports a self-contained and shareable entity-relationship data structure for storing model parameter values and the associated data. The software is developed using the latest Python environment and supports the execution of plugins. It is shipped in an installation package as a desktop application for different operating systems

On Monte Carlo simulation and analysis of electricity markets

This dissertation is about how Monte Carlo simulation can be used to analyse electricity markets. There are a wide range of applications for simulation; for example, players in the electricity market can use simulation to decide whether or not an investment can be expected to be profitable, and authorities can by means of simulation find out which consequences a certain market design can be expected to have on electricity prices, environmental impact, etc. In the first part of the dissertation, the focus is which electricity market models are suitable for Monte Carlo simulation. The starting point is a definition of an ideal electricity market. Such an electricity market is partly practical from a mathematical point of view (it is simple to formulate and does not require too complex calculations) and partly it is a representation of the best possible resource utilisation. The definition of the ideal electricity market is followed by analysis how the reality differs from the ideal model, what consequences the differences have on the rules of the electricity market and the strategies of the players, as well as how non-ideal properties can be included in a mathematical model. Particularly, questions about environmental impact, forecast uncertainty and grid costs are studied. The second part of the dissertation treats the Monte Carlo technique itself. To reduce the number of samples necessary to obtain accurate results, variance reduction techniques can be used. Here, six different variance reduction techniques are studied and possible applications are pointed out. The conclusions of these studies are turned into a method for efficient simulation of basic electricity markets. The method is applied to some test systems and the results show that the chosen variance reduction techniques can produce equal or better results using 99% fewer samples compared to when the same system is simulated without any variance reduction technique. More complex electricity market models cannot directly be simulated using the same method. However, in the dissertation it is shown that there are parallels and that the results from simulation of basic electricity markets can form a foundation for future simulation methods. Keywords: Electricity market, Monte Carlo simulation, variance reduction techniques, operation cost, reliability.QC 2010060

On Monte Carlo simulation and analysis of electricity markets

This dissertation is about how Monte Carlo simulation can be used to analyse electricity markets. There are a wide range of applications for simulation; for example, players in the electricity market can use simulation to decide whether or not an investment can be expected to be profitable, and authorities can by means of simulation find out which consequences a certain market design can be expected to have on electricity prices, environmental impact, etc. In the first part of the dissertation, the focus is which electricity market models are suitable for Monte Carlo simulation. The starting point is a definition of an ideal electricity market. Such an electricity market is partly practical from a mathematical point of view (it is simple to formulate and does not require too complex calculations) and partly it is a representation of the best possible resource utilisation. The definition of the ideal electricity market is followed by analysis how the reality differs from the ideal model, what consequences the differences have on the rules of the electricity market and the strategies of the players, as well as how non-ideal properties can be included in a mathematical model. Particularly, questions about environmental impact, forecast uncertainty and grid costs are studied. The second part of the dissertation treats the Monte Carlo technique itself. To reduce the number of samples necessary to obtain accurate results, variance reduction techniques can be used. Here, six different variance reduction techniques are studied and possible applications are pointed out. The conclusions of these studies are turned into a method for efficient simulation of basic electricity markets. The method is applied to some test systems and the results show that the chosen variance reduction techniques can produce equal or better results using 99% fewer samples compared to when the same system is simulated without any variance reduction technique. More complex electricity market models cannot directly be simulated using the same method. However, in the dissertation it is shown that there are parallels and that the results from simulation of basic electricity markets can form a foundation for future simulation methods. Keywords: Electricity market, Monte Carlo simulation, variance reduction techniques, operation cost, reliability.QC 2010060

Simulation of Trading Arrangements Impact on Wind Power Imbalance Costs

Uncertain wind power forecasts is a disadvantage in an electricity market where the majority of the trading is performed several hours before the actual delivery. This paper presents a model which can be used to study how changes in the trading arrangement-in particular changing the delay time between closure of the spot market and the delivery period or changing the imbalance pricing system-would affect different players in the electricity market. The model can be used in Monte Carlo simulation, which is demonstrated for an example system.© 2008 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.QC 2011030

Small-scale Renewable Energy Sources for Rural Electrification. Possibilities and Limitations

QC 2012022