F. Careri

Generation Expansion Planning in the Age of Green Economy

Generation expansion planning (GEP) is the problem of finding the optimal strategy to plan the construction of new generation plants while satisfying technical and economical constraints. It is a challenging problem due to its nonlinearity, large-scale, and to the discrete nature of the variables describing unit size and allocation. Originally, GEP was faced by vertically integrated utilities with the aim of minimizing production and capital costs. After deregulation, generation companies were forced to consider GEP from the viewpoint of market shares and financial risk. In recent years, increasing concern for environmental protection has driven lots of countries all over the world to promote energy generation from renewable sources. Different incentive systems have been introduced to support the growth of the investments in generation plants exploiting renewable energy. In the present paper, the impact of some of the most popular incentive systems (namely feed-in tariffs, quota obligation, emission trade, and carbon tax) on generation planning is considered, thus obtaining a comprehensive GEP model with a suitably modified objective function and additional constraints. The resulting problem is solved by resorting to the generalized Benders decomposition (GBD) approach and implemented in the Matlab programming language. Tests are presented with reference to the Italian system.

Strategic bidding in a day-ahead market by coevolutionary genetic algorithms

In the present work, the problem of energy market price clearing and generation company (Genco) strategic bidding is considered in the framework of existing day-ahead markets with system marginal price auction. The situation of imperfect competition arising when one of the Gencos is large enough to exert market power is considered in detail, showing what bidding behaviors are to be expected when such a market arrangement occurs. The impact that inter-area transmission system congestions may have on the mechanism of system pricing is also addressed. The bidding problem faced by each Genco is formulated as a strategic multi-player game in which the choice between different bidding levels and energy amounts to be sold at the market has to be made. The large size of the problem due to the number of competitors and to the presence of transmission constraints makes the application of classical game theory troublesome. Therefore, an agent based method belonging to the category of coevolutionary genetic algorithm was selected for the solution of this problem. Test cases illustrate the different strategies that the Gencos may implement to optimize their performance at the day-ahead market. Beside some small didactical examples, the situation of the Italian day-ahead market is considered in detail.

Definition of a zonal reactive power market based on the adoption of a hierarchical voltage control

The overall purpose of the paper is to suggest a suitable methodology of valuing the reactive power support in a deregulated electricity market from the point of view of Transmission System Operator (TSO). The valorization of the reactive resource available in a grid bus will be determined by means of an Optimal Reactive Power Flow (ORPF) procedure, which pursues both economic and security criteria. The methodology has been applied to a detailed model of the EHV Italian system with reference to a 2010 peak load condition and to a medium term forecast scenario (target year 2014). The results will show that an adequate Hierarchical Voltage Control (HVC) framework and above all a suitable definition of control areas may be a good way to implement a regional (zonal) reactive power market. The analysis will also investigate the possible consequences of delaying the network development planned by the Italian TSO.

An online optimization algorithm for coordinated control of VSC-HVDC link and DFIGs for offshore wind applications

The aim of this paper is to find out an optimization algorithm for wind power plants, where the wind turbines are equipped by DFIG, connected to on-shore grid by means VSC-HVDC link. The off-shore grid frequency is assumed variable under a fixed range and the wind power tracking characteristic of the DFIG has to be extended to consider the additional grid frequency dependence. The highly non linear problem is solved by means of a interior point algorithm and a sequential quadratic programming algorithm. The methodology has been tested on a wind park composed by 50 turbines. Results show that the best operation point is quickly reached, demonstrating the effectiveness of the tool for online operations.

Bidding strategies in day-ahead energy markets: System marginal price vs. pay as bid

In the present work, the problem of energy market price clearing and generation company (Genco) strategic bidding is considered with reference to existing day-ahead markets. The usual market settlement based on the system marginal price (SMP) is first addressed, this being the most widespread energy market implementation. The pay as bid (PAB) auction has been sometimes proposed as a viable alternative to SMP with the aim of increasing competition among Gencos by discouraging collusive behaviors and market power exploitation. The bidding problem is formulated as a strategic multi-player game in which the choice between different bidding levels and energy amounts to be sold at the market has to be made. The large size of the problem makes the application of classical game theory troublesome; for this reason, an agent based method belonging to the category of coevolutionary genetic algorithms was adopted. In the present paper the results to be expected form both market arrangements are then evaluated and compared, with particular reference to the day-ahead market applied to a small system. In the tests, the situation of imperfect competition arising when one of the Gencos has the opportunity to exert market power is considered. The impact that inter-area transmission system congestions may have on the mechanism of system pricing is also addressed.

Impact of GHG Emission Reduction on Power Generation Expansion Planning

In this work the impact of greenhouse gas (GHG) emission reduction on Power Generation Expansion Planning (PGEP) is investigated. An overview of several PGEP models, which also consider environmental constraints and GHG emission limits, is presented. After a short introduction on regulations about GHG emission Cap and Trade System in Europe and in the United States and a survey on state of the art PGEP, a new approach to assess the effects of fuel and electric energy price volatility on long term generation planning by a GENCO (GENeration COmpany) is proposed. The objective function, to be maximized, consists of the total revenue obtained by the GENCO over a certain time horizon into the future. A general model is developed to find both present and future generation mixes of a given GENCO and the Lagrangian Relaxation method is used to solve the large scale mixed integer problem. However, its results will be not really suitable to define a generation planning strategy, unless the uncertainties of costs, prices and construction times are considered. For this reason, a Monte Carlo simulation procedure is implemented including an expansion planning computation at each step. In the model renewable resources, like off-shore and on-shore wind, biomass, mini hydro, geothermal, solar thermodynamic and photovoltaic power plants, are also taken into account. The results are presented with reference to an hypothetical GENCO, by setting all the scenario variables on the basis of available historical data.

Probabilistic load flow procedure for assessing the distributed generation impact on the high voltage network

This paper presents a Probabilistic Load Flow (PLF) procedure that allows the impact of distributed generation (DG) on the transmission system to be assessed. By means of the Monte Carlo technique, several system states are simulated, varying the power injection of DG. The load flow solution is performed by the Newton-Raphson method and a distributed slack bus formulation is adopted, in order to distribute the active power mismatch (created by the random generation) on all the thermal units in the system. Tests are carried out both on a small system and on the Italian network. Results lead to conclude that the probabilistic approach to the power system analysis provides more indications than the traditional deterministic techniques, especially considering a high penetration of DG.

Identification of the best power plant layout for large offshore wind farms connected via VSC-HVDC links

The overall purpose of this paper is to find out the best choice in terms of generator technology for large offshore wind farms connected to the EHV network by means of VSC-HVDC transmission: besides the traditional benefits compared with HVAC and line commutated HVDC, this technology allows variable frequency operation, in order to increase the overall production of the wind farm. Two different types of generators — Squirrel Cage Induction Generator (SCIG) and Doubly-Fed Induction Generator (DFIG) — are compared in variable frequency operation: the related power plant layouts are analyzed, for different wind speed conditions, by means of a slightly modified version of an online optimization algorithm. The resulting programs are coded in MATLAB® environment and some tests are carried out.

Centralized coordinated control of VSC-HVDC link and DFIGs in very large offshore wind power plants

The aim of this paper is to improve an optimization algorithm for coordinated management of VSC-HVDC link and DFIGs in variable frequency operation, presented by the authors in a previous work. The new features introduced in this paper concern different upper bounds on DFIG rotor and stator power flow and modified constraints on generator slips and AC offshore grid frequency. The upper bound on stator power flow is suitably set to allow an overload manageable for an electromechanical machine. The Central Optimizer procedure is followed by a Local one modifying (in dependence to real time measured wind speed) the optimization variables for each generators, taking constant the AC offshore grid frequency at the value previously found by the Central Optimizer. The highly non linear problem is solved by means of an interior point algorithm. Test results show the goodness of the proposed methodology.

Generation expansion planning in the age of green economy

Generation expansion planning (GEP) is the problem of finding the optimal strategy to plan the construction of new generation plants while satisfying technical and economical constraints. It is a challenging problem due to its nonlinearity, large-scale, and to the discrete nature of the variables describing unit size and allocation. Originally, GEP was faced by vertically integrated utilities with the aim of minimizing production and capital costs. After deregulation, generation companies were forced to consider GEP from the viewpoint of market shares and financial risk. In recent years, increasing concern for environmental protection has driven lots of countries all over the world to promote energy generation from renewable sources. Different incentive systems have been introduced to support the growth of the investments in generation plants exploiting renewable energy. In the present paper, the impact of some of the most popular incentive systems (namely feed-in tariffs, quota obligation, emission trade, and carbon tax) on generation planning is considered, thus obtaining a comprehensive GEP model with a suitably modified objective function and additional constraints. The resulting problem is solved by resorting to the generalized Benders decomposition (GBD) approach and implemented in the Matlab programming language. Tests are presented with reference to the Italian system.