We consider the optimal economic dispatch of power generators in a smart electric grid for allocating power between generators to meet load requirements at minimum total cost. We assume that each generator has a piece-wise linear cost function. We first present a polynomial time algorithm that achieves optimal dispatch. We then present a decentralized algorithm where, each generator independently adjusts its power output using only the aggregate power imbalance in the network, which can be observed by each generator through local measurements of the frequency deviation on the grid. The algorithm we propose exponentially erases the power imbalance, while eventually minimizing the generation cost.

Optimal generation scheduling attempts to minimise the cost of power production while satisfying the various operation constraints and physical limitations on the power system components. The thermal generation scheduling problem can be considered as a power system control problem acting over different time frames. The unit commitment phase determines the optimum pattern for starting up and shutting down the generating units over the designated scheduling period, while the economic dispatch phase is concerned with allocation of the load demand among the on-line generators. In a hydrothermal system the optimal scheduling of generation involves the allocation of generation among the hydro electric and thermal plants so as to minimise total operation costs of thermal plants while satisfying the various constraints on the hydraulic and power system network. This thesis reports on the development of genetic algorithm computation techniques for the solution of the short term generation scheduling problem for power systems having both thermal and hydro units. A comprehensive genetic algorithm modelling framework for thermal and hydrothermal scheduling problems using two genetic algorithm models, a canonical genetic algorithm and a deterministic crowding genetic algorithm, is presented. The thermal scheduling modelling framework incorporates unit minimum up and down times, demand and reserve constraints, cooling time dependent start up costs, unit ramp rates, and multiple unit operating states, while constraints such as multiple cascade hydraulic networks, river transport delays and variable head hydro plants, are accounted for in the hydraulic system modelling. These basic genetic algorithm models have been enhanced, using quasi problem decomposition, and hybridisation techniques, resulting in efficient generation scheduling algorithms. The results of the performance of the algorithms on small, medium and large scale power system problems is presented and compared with other conventional scheduling techniques.

[pt] Uma formulação geral do problema do despacho econômico de carga considerando as perdas nas linhas de transmissão, é apresentada dando ênfase e uma solução pelo método das penalidades de Fletcher Powell. Ao contrário do despacho de carga clássico, onde a solução é por vezes encontrada mediante ajustes repetidos das grandezas arbitradas, propõe-se resolver um sistema de inequações levando-se em consideração as restrições do problema, determinando pontos pertencentes a este conjunto de restrições. Vários testes foram realizados, comparando-se os resultados com aqueles obtidos por intermédio de outros métodos, provando-se a eficiência do algoritmo adotado tanto sob o ponto de vista de precisão quanto de convergência. / [en] The general formulation of a problem of economic dispatch concerning losses in transmission lines and the presentation with emphasis on a solution by the Fletcher Power penalty methold is employed. Not considerating the classic load flow where a solution is encountered by trail methods our purposal is to solve a system of inequalities taking into consideration the limits of the problem, determinig values belonging to the set of restrictions. Various tests were realized comparing the results with those obtained by the means of other methods, proving the efficiency of the algorithm from the point of view of precision and convergence.

Yes / The solution of generation scheduling (GS) problems involves the determination of the unit commitment (UC) and economic dispatch (ED) for each generator in a power system at each time interval in the scheduling period. The solution procedure requires the simultaneous consideration of these two decisions. In recent years researchers have focused much attention on new solution techniques to GS. This paper proposes the application of a variety of genetic algorithm (GA) based approaches and investigates how these techniques may be improved in order to more quickly obtain the optimum or near optimum solution for the GS problem. The results obtained show that the GA-based hybrid approach offers an effective alternative for solving realistic GS problems within a realistic timeframe.

Power grid is an interconnected system of supplying electricity from the supplier to the consumer, consisting of electricity generating plant, high voltage transmission lines- to carry electricity from the generating plant to the load center, and distribution lines- to carry electricity from load centers to individual consumers. A lot of research is being pursued to develop technologies for improving the next generation of power grid called the Smart Power Grid. The Smart Power Grid will have sophisticated communication infrastructure to improve the efficiency of electricity generation using renewable energy sources like the sun, water, etc and also to inform consumers of their electricity usage pattern. Also, the electricity market is now divided into three sections- generation, transmission and distribution. Private companies are competing with each other to provide electricity at the most competitive market price. We have developed two algorithms to help generating companies achieve their goal of meeting the hourly electricity need of the consumers and to do so at a minimum total cost.

In this dissertation, we propose a novel distributed approach to the control of generators in the electric grid. Specifically, we consider the problem of the optimal economic dispatch of generator; we present a simple, distributed algorithm, which adjusts the power-frequency set-points of generators to correct for power imbalances arising from generation and load fluctuations. In this algorithm each generator independently adjusts its real-power output based on its estimate of the aggregate power imbalance in the network; such as an estimate can be independently obtained by each generator through local measurements of the frequency deviation on the grid. Eventually, over the course of network operation, the distributed algorithm achieves the equal-marginal-cost power allocation among generators while driving the power imbalance exponentially to zero. In the absence of power losses, we prove the eventual optimality of the distributed algorithm under mild assumptions (strict convexity and positivity of cost functions) and present simulation results to compare its performance with traditional (centralized) dispatch algorithms. Furthermore, we present numerical simulation results that show that the distributed algorithm performs well even in the presence of power losses and other constraints. We argue that distributed control methods are especially attractive for electric grids with smart meters and other advanced capabilities at the end node and grids with high penetration of alternative energy generators and we identify interesting open problems for future work in this area.

This thesis aims to provide a general overview of a cost and benefit analysis of incorporating a battery energy storage system within unit commitment model. The deregulation of the electricity market in the U.S. has only been around for the last two decades. With renewable energy and energy storage systems becoming less expensive, a decentralized market scheme is becoming more popular and plausible. The scope of this work is to provide a fundamental understanding of unit commitment and a cost analysis of applying a battery energy storage system to an already established power system. A battery energy storage system (BESS) was placed within a unit commitment schematic and modeled for a 7 day/168 hour forecast. Three models were generated, two with and one without the battery energy storage device (BESS). The comparison between the three systems was conducted to produce a visual economic justification to the feasibility of a BESS.

In recent years, the awareness of environmental protection has made the power dispatch model no longer purely economical-oriented. This thesis proposed the application of particle swarm optimization (PSO) algorithm and interactive compromise programming method to solve the 24-hour two-objective power dispatch problem. Considering simultaneously the lowest generating cost and the lowest pollution emission, the two mutually-conflicting objectives will choose a compromised dispatch model. This thesis joined the mixed-integer programming problem of optimal power flow (MIOPF) with the dynamic economic dispatch (DED), making this dispatch solution more realistic without electrical violations; The MIOPF considers both continuous and discrete types of variables. The continuous variables are the generating unit real power output and the generator-bus voltage magnitudes; the discrete variables are the shunt capacitor banks and transformer tap setting. Simulation were run on the standard IEEE 30 Bus system. In order to avoid the PSO local optimality problem, this thesis proposed the utilization of the PSO algorithm with time-varying acceleration coefficients (PSO_TVAC) plus the local random search method (LRS), so it can quickly and effectively reach the optimal solution, without advantages of performance and accuracy of PSO. This thesis also proposed the consideration of the available transfer capability (ATC) on transmission lines of the existing dispatch model. Applying sensitivity factors to calculate each generator¡¦s available transfer capability that can be offered in the analyzed time interval, enables the creation of a new constraint. Joined with the dynamic economic dispatch problem, it will make possible that a load client wishes to raise its demand. Simultaneously taking care of the minimum cost and the limits of system security, better dispatch results could be expected.

This dissertation proposes new methods to improve the efficiency of electricity markets with respect to market monitoring and reserve allocation. We first present new approaches to monitor the level of competition in electricity markets, a critical task for helping the markets function smoothly. The proposed approaches are based on economic principles and a faithful representation of transmission constraints. The effectiveness of the new approaches is demonstrated by examples based on medium- and large-scale electric power systems. We then propose a new system-operation model using stochastic optimization to systematically allocate reserves under uncertainty. This model aims to overcome the difficulties in both system and market operations caused by the integration of wind power, which results in a higher degree of supply uncertainty. The numerical examples suggest that the proposed model significantly lower the operation costs, especially under high levels of wind penetration. / text

Electric power is a basic requirement for present day life and its various economic sectors. To satisfy the ever-increasing needs for electricity, the number of generating units, transmission lines and distribution systems is rising steadily. In addition, electric power systems are among the most complex industrial systems of the modern age. Beside complexity, the generation of electric power is a main source of gaseous emissions and pollutants. The planning and operation of electric power systems must be done in a way that the load demand is met reliably, cost-effectively and in an environmentally responsible manner. Practitioners strive to achieve these goals for successful planning and operations utilizing various optimization tools. It is clear that the objectives to be satisfied are mostly conflicting. In particular, minimizing the fuel cost and the gaseous emissions are two conflicting and non-commensurate objectives. Therefore, multi-objective optimization techniques are employed to obtain trade-off relationships between these incompatible objective functions in order to help decision makers take proper decisions. In this thesis, two main power system operation problems are addressed. These are the economic load dispatch (ED) and the short-term hydro-thermal generation scheduling (STHTS). They are treated first as single-objective optimization problems then they are tackled as multi-objective ones considering the environmental aspects. These problems, single and multi-objective, are nonlinear non-convex constrained optimization problems with high-dimensional search spaces. This makes them a real challenge for any optimization technique. To obtain the optimal or close to optimal solutions, a modified bacterial foraging algorithm is proposed, developed and successfully applied. The bacterial foraging algorithm is a metaheuristic non-calculus-based optimization technique. The proposed algorithm is validated using diverse benchmark optimization examples before implementing it to solve the problems of this thesis. Various practical constraints are considered in the different cases of each problem. These include transmission losses, valve-point effects for both the ED and the STHTS problems and water availability and reservoir configurations for the STHTS problem. In all cases the optimal or near-optimal solution is obtained. For the multi-objective optimization cases, the Pareto optimal solution set that shows the trade-off relationship between the conflicting objectives is successfully captured.