A Reinforcement Learning Algorithm to Economic Dispatch Considering Transmission Losses

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.

Reinforcement Learning approaches to Economic Dispatch problem

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.

A Reinforcement Learning Approach to Economic Dispatch using Neural Networks

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

Reinforcement Learning Approaches to Power System Scheduling

One major component of power system operation is generation scheduling. The objective of the work is to develop efficient control strategies to the power scheduling problems through Reinforcement Learning approaches. The three important active power scheduling problems are Unit Commitment, Economic Dispatch and Automatic Generation Control. Numerical solution methods proposed for solution of power scheduling are insufficient in handling large and complex systems. Soft Computing methods like Simulated Annealing, Evolutionary Programming etc., are efficient in handling complex cost functions, but find limitation in handling stochastic data existing in a practical system. Also the learning steps are to be repeated for each load demand which increases the computation time.Reinforcement Learning (RL) is a method of learning through interactions with environment. The main advantage of this approach is it does not require a precise mathematical formulation. It can learn either by interacting with the environment or interacting with a simulation model. Several optimization and control problems have been solved through Reinforcement Learning approach. The application of Reinforcement Learning in the field of Power system has been a few. The objective is to introduce and extend Reinforcement Learning approaches for the active power scheduling problems in an implementable manner. The main objectives can be enumerated as:(i) Evolve Reinforcement Learning based solutions to the Unit Commitment Problem.(ii) Find suitable solution strategies through Reinforcement Learning approach for Economic Dispatch. (iii) Extend the Reinforcement Learning solution to Automatic Generation Control with a different perspective. (iv) Check the suitability of the scheduling solutions to one of the existing power systems.First part of the thesis is concerned with the Reinforcement Learning approach to Unit Commitment problem. Unit Commitment Problem is formulated as a multi stage decision process. Q learning solution is developed to obtain the optimwn commitment schedule. Method of state aggregation is used to formulate an efficient solution considering the minimwn up time I down time constraints. The performance of the algorithms are evaluated for different systems and compared with other stochastic methods like Genetic Algorithm.Second stage of the work is concerned with solving Economic Dispatch problem. A simple and straight forward decision making strategy is first proposed in the Learning Automata algorithm. Then to solve the scheduling task of systems with large number of generating units, the problem is formulated as a multi stage decision making task. The solution obtained is extended in order to incorporate the transmission losses in the system. To make the Reinforcement Learning solution more efficient and to handle continuous state space, a fimction approximation strategy is proposed. The performance of the developed algorithms are tested for several standard test cases. Proposed method is compared with other recent methods like Partition Approach Algorithm, Simulated Annealing etc.As the final step of implementing the active power control loops in power system, Automatic Generation Control is also taken into consideration.Reinforcement Learning has already been applied to solve Automatic Generation Control loop. The RL solution is extended to take up the approach of common frequency for all the interconnected areas, more similar to practical systems. Performance of the RL controller is also compared with that of the conventional integral controller.In order to prove the suitability of the proposed methods to practical systems, second plant ofNeyveli Thennal Power Station (NTPS IT) is taken for case study. The perfonnance of the Reinforcement Learning solution is found to be better than the other existing methods, which provide the promising step towards RL based control schemes for practical power industry.Reinforcement Learning is applied to solve the scheduling problems in the power industry and found to give satisfactory perfonnance. Proposed solution provides a scope for getting more profit as the economic schedule is obtained instantaneously. Since Reinforcement Learning method can take the stochastic cost data obtained time to time from a plant, it gives an implementable method. As a further step, with suitable methods to interface with on line data, economic scheduling can be achieved instantaneously in a generation control center. Also power scheduling of systems with different sources such as hydro, thermal etc. can be looked into and Reinforcement Learning solutions can be achieved.

Sustainability of Medicinal Plants in Kerala Economic Considerations in Domestication and Conservation of Forest Resources

The present study on the sustainability of medicinal plants in Kerala economic considerations in domestication and conservation of forest resources. There is worldwide consensus on the fact that medicinal plants are important not only in the local health support systems but in rural income and foreign exchange earnings. Sustainability of medicinal plants is important for the survival of forest dwellers, the forest ecosystem, conserving a heritage of human knowledge and overall development through linkages. More equitable sharing of the benefits from commercial utilization of the medicinal plants was found essential for the sustainability of the plants. Cultivation is very crucial for the sustainability of the sector. Through a direct tie-up with the industry, the societies can earn more income and repatriate better collection charges to its members. Cultivation should be carried out in wastelands, tiger reserves and in plantation forests. In short, the various players in the in the sector could find solution to their specific problems through co-operation and networking among them. They should rely on self-help rather than urging the government to take care of their needs. As far as the government is concerned, the forest department through checking over- exploitation of wild plants and the Agriculture Dept. through encouraging cultivation could contribute to the sustainable development of the medicinal plant sector.

Economic Impacts Of Air Pollution on Human Health and Property Values

The objectives of the present study are to provide a systematic descriptive documentation of the nature of air pollution of the Cochin industrial agglomeration, estimate the willingness to pay for morbidity reduction due to air pollution in observed and hypothetical markets and to estimate the value of welfare loss in the purchase of property due to reduced air quality. This study is an attempt to examine economic impacts of air pollution on the human health and property values in the industrial capital of Kerala. The process of industrialization in Kerala and the increase in air pollution created damages to human, natural and economic resources in the state. The study documents the extent of air pollution and applied econometric approaches to estimate economic impacts of air pollution on human health and property values. The Important sources of air pollution identified in Cochin are emissions from industries and automobiles.

Industrial Growth in Madhya Pradesh: Structure and Economic Backwardness

It examine the aspects of Madhya Pradesh’s industrial structure which throw light on the development, viability and the efficiency of not only the over all industrial sector but also some of the selected industries of the state. The major objectives of are to examine the nature and characteristics of economic backwardness in Madhya Pradesh in an inter-state comparative framework and to analyse the pace and pattern of industrial growth in Madhya Pradesh against the backdrop of liberalization. To explore the industrial structure of Madhya Pradesh using the major structural ratios and industry mix. This study has underlined some structural as well as region specific constraints to the accelerated growth of the manufacturing industry in Madhya Pradesh. The industrial structure of Madhya Pradesh is concentrated and lop-sided. This is evidenced by the dominancy of single industry, basic metal and alloys. A diversified industrial structure is essential for promoting interdependent growth of the manufacturing industry based on the inter-industry linkages and agglomeration. The thesis gives a broad spectrum of regional disparities in development and evidence for Madhya Pradesh’s backwardness also portrayed and reflects the changing industrial structure of the state.

Factors Underlying Heterogeneous Individual Growth in Macrobrachium rosenbergii and the Effect of Size Heterogeneity on Economic Feasibility of Farming in Kuttanad ( Kerala )

School of Industrial Fisheries, Cochin University of Science and Technology

A Study on the Efficiency of Islamic Economic Instruments in Selected Islamic Countries

Department of Applied Economics,Cochin University of Science and Technology

Socio-Economic Issues and Sustainable Fishery Management .A Case Study of Kerala

Department of Applied Economics, Cochin University of Science and Technology.

Study on Solid Waste Management : An Economic Analysis with Respect to Ernakulam District

This thesis analyzed waste generation and waster disposal problems in municipalities and Cochin Corporation in Ernakulam district.Then the potential of resource recovery and recycling from biodegradable and non bio-degradable waste is established.The study further focused on the need for segregation of waste at the source as biodegradable and non biodegradable solid waste.The potential of resource recovery is explained in detail through the case study.The thesis also highlights the economically viable and environmental friendly methods o f treatment of waste.But the problem is that concerted and earnest attempts are lacking in making use of such methods.In spite of the health problems faced,people living near the dump sites are forced to stay there either because of their weak economic background or family ties.The study did not calculate the economic cost of health problems arising out of unscientific and irresponsible methods of waste disposal.

Dynamics of Deforestation and Socio-Economic profile of Tribal Women Folk in Kerala- A study of Attappady is

This thesis Entitled Dynamics of deforestation and Socio-Economic profile of tribal women flok in kerala -A study of Attappady. The study was based on both primary and secondary data. Primary data were collected through a sample survey conducted in three panchayaths .The thesis is organized in eight chapters. The first chapter provides the background to the study. Second chapter reviews the literature. Third chapter provides the profile of the study area and general conditions. Fourth chapter consists of the life cycle structure of the tribal woman. Fifth chapter covers the socio-economic conditions of the tribal women in the study area. Sixth chapter consists of relationship between tribal women and forest and the degradation of the forest. Seventh chapter provides the documentation of the development programmes implemented in Attappady and their importance to the tribals. Last chapter consists of summary and conclusions of the study, suggestions and recommendations of the study.