Now showing items 1-6 of 6
| Abstract: | Reinforcement Learning (RL) refers to a class of learning algorithms in which learning system learns which action to take in different situations by using a scalar evaluation received from the environment on performing an action. RL has been successfully applied to many multi stage decision making problem (MDP) where in each stage the learning systems decides which action has to be taken. Economic Dispatch (ED) problem is an important scheduling problem in power systems, which decides the amount of generation to be allocated to each generating unit so that the total cost of generation is minimized without violating system constraints. In this paper we formulate economic dispatch problem as a multi stage decision making problem. In this paper, we also develop RL based algorithm to solve the ED problem. The performance of our algorithm is compared with other recent methods. The main advantage of our method is it can learn the schedule for all possible demands simultaneously. |
| Description: | TENCON 2008-2008 IEEE Region 10 Conference |
| URI: | http://dyuthi.cusat.ac.in/purl/4480 |
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| A Reinforcement ... ng Transmission Losses.pdf | (265.1Kb) |
| Abstract: | This paper presents Reinforcement Learning (RL) approaches to Economic Dispatch problem. In this paper, formulation of Economic Dispatch as a multi stage decision making problem is carried out, then two variants of RL algorithms are presented. A third algorithm which takes into consideration the transmission losses is also explained. Efficiency and flexibility of the proposed algorithms are demonstrated through different representative systems: a three generator system with given generation cost table, IEEE 30 bus system with quadratic cost functions, 10 generator system having piecewise quadratic cost functions and a 20 generator system considering transmission losses. A comparison of the computation times of different algorithms is also carried out. |
| Description: | Electrical Power and Energy Systems 33 (2011) 836–845 |
| URI: | http://dyuthi.cusat.ac.in/purl/4484 |
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| Reinforcement L ... nomic Dispatch problem.pdf | (311.8Kb) |
| Abstract: | This paper presents a Reinforcement Learning (RL) approach to economic dispatch (ED) using Radial Basis Function neural network. We formulate the ED as an N stage decision making problem. We propose a novel architecture to store Qvalues and present a learning algorithm to learn the weights of the neural network. Even though many stochastic search techniques like simulated annealing, genetic algorithm and evolutionary programming have been applied to ED, they require searching for the optimal solution for each load demand. Also they find limitation in handling stochastic cost functions. In our approach once we learn the Q-values, we can find the dispatch for any load demand. We have recently proposed a RL approach to ED. In that approach, we could find only the optimum dispatch for a set of specified discrete values of power demand. The performance of the proposed algorithm is validated by taking IEEE 6 bus system, considering transmission losses |
| Description: | Fifteenth National Power Systems Conference (NPSC), IIT Bombay, December 2008 |
| URI: | http://dyuthi.cusat.ac.in/purl/4490 |
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| A Reinforcement ... using Neural Networks.pdf | (165.9Kb) |
| Abstract: | Unit Commitment Problem (UCP) in power system refers to the problem of determining the on/ off status of generating units that minimize the operating cost during a given time horizon. Since various system and generation constraints are to be satisfied while finding the optimum schedule, UCP turns to be a constrained optimization problem in power system scheduling. Numerical solutions developed are limited for small systems and heuristic methodologies find difficulty in handling stochastic cost functions associated with practical systems. This paper models Unit Commitment as a multi stage decision making task and an efficient Reinforcement Learning solution is formulated considering minimum up time /down time constraints. The correctness and efficiency of the developed solutions are verified for standard test systems |
| Description: | International J. of Recent Trends in Engineering and Technology, Vol. 3, No. 3, May 2010 |
| URI: | http://dyuthi.cusat.ac.in/purl/4489 |
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| Reinforcement L ... through pursuit method.pdf | (568.2Kb) |
| Abstract: | Unit commitment is an optimization task in electric power generation control sector. It involves scheduling the ON/OFF status of the generating units to meet the load demand with minimum generation cost satisfying the different constraints existing in the system. Numerical solutions developed are limited for small systems and heuristic methodologies find difficulty in handling stochastic cost functions associated with practical systems. This paper models Unit Commitment as a multi stage decision task and Reinforcement Learning solution is formulated through one efficient exploration strategy: Pursuit method. The correctness and efficiency of the developed solutions are verified for standard test systems |
| Description: | Advances in Computing, Control, & Telecommunication Technologies, 2009. ACT'09. International Conference on |
| URI: | http://dyuthi.cusat.ac.in/purl/4494 |
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| Reinforcement L ... through pursuit method.pdf | (300.8Kb) |
| Abstract: | Short term load forecasting is one of the key inputs to optimize the management of power system. Almost 60-65% of revenue expenditure of a distribution company is against power purchase. Cost of power depends on source of power. Hence any optimization strategy involves optimization in scheduling power from various sources. As the scheduling involves many technical and commercial considerations and constraints, the efficiency in scheduling depends on the accuracy of load forecast. Load forecasting is a topic much visited in research world and a number of papers using different techniques are already presented. The accuracy of forecast for the purpose of merit order dispatch decisions depends on the extent of the permissible variation in generation limits. For a system with low load factor, the peak and the off peak trough are prominent and the forecast should be able to identify these points to more accuracy rather than minimizing the error in the energy content. In this paper an attempt is made to apply Artificial Neural Network (ANN) with supervised learning based approach to make short term load forecasting for a power system with comparatively low load factor. Such power systems are usual in tropical areas with concentrated rainy season for a considerable period of the year |
| Description: | Power, Signals, Controls and Computation (EPSCICON), 2012 International Conference on |
| URI: | http://dyuthi.cusat.ac.in/purl/4488 |
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| Short-Term Load ... ive Learning Algorithm.pdf | (978.7Kb) |
Now showing items 1-6 of 6
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