Influence of the Operational Constraints in the Active Transmission Losses Allocation via Lagrange Multipliers

This paper presents an approach for the active transmission losses allocation between the agents of the system. The approach uses the primal and dual variable information of the Optimal Power Flow in the losses allocation strategy. The allocation coefficients are determined via Lagrange multipliers. The paper emphasizes the necessity to consider the operational constraints and parameters of the systems in the problem solution. An example, for a 3-bus system is presented in details, as well as a comparative test with the main allocation methods. Case studies on the IEEE 14-bus systems are carried out to verify the influence of the constraints and parameters of the system in the losses allocation.

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.

Allocation of the costs of transmission losses

An analysis of the performance of six major methods of loss allocation for generators and demands was conducted, based on pro-rata (two), on incremental factors (two), on proportional sharing (PS) (one), and on electric circuit theory (one). Using relatively simple examples which can easily be checked, the advantages and disadvantages of each were ascertained and the results confirmed using a larger sample system (IEEE-118). The discussion considers the location and size of generators and demands, as well as the merits of the location of these agents for each configuration based on an analysis of the effect of various network modifications. Furthermore, an application in the South-Southeastern Brazilian Systems is performed. Conclusions and recommendations are presented. (C) 2004 Elsevier B.V. All rights reserved.

Alternative continuation method for loading margin improvement and transmission losses reduction

This paper presents an alternative methodology for loading margin improvement and total real power losses reduction by using a continuation method. In order to attain this goal, a parameterizing equation based on the total real power losses and the equations of the reactive power at the slack and generation buses are added to the conventional power flow equations. The voltages at these buses are considered as control variables and a new parameter is chosen to reduce the real power losses in the transmission lines. The results show that this procedure leads to maximum loading point increase and consequently, in static voltage stability margin improvement. Besides, this procedure also takes to a reduction in the operational costs and, simultaneously, to voltage profile improvement. Another important result of this methodology is that the resulting operating points are close to that provided by an optimal power flow program. © 2004 IEEE.

Allocation of the cost of transmission losses in a multimarket framework

The paper addresses the issue of apportioning of the cost of transmission losses to generators and demands in a multimarket framework. Line flows are unbundled using equivalent bilateral exchanges on a DC-network model and allocated to generators and demands. Losses are then calculated based on unbundled flows and straightforwardly apportioned to generators and demands. The proposed technique is particularly useful in a multimarket framework, where all markets have a common grid operator with complete knowledge of all network data, as is the case of the Brazilian electric-energy system. The methodology proposed is illustrated using the IEEE Reliability Test System and compared numerically with an alternative technique. Appropriate conclusions are drawn. © The Institution of Engineering and Technology 2006.

A study of high pressure transmission losses /

Cover title: A study of high tensions losses.

Comparison of algorithms for loss allocation in transmission networks considering power redispatch

An analysis of the performances of three important methods for generators and loads loss allocation is presented. The discussed methods are: based on pro-rata technique; based on the incremental technique; and based on matrices of circuit. The algorithms are tested considering different generation conditions, using a known electric power system: IEEE 14 bus. Presented and discussed results verify: the location and the magnitude of generators and loads; the possibility to have agents well or poorly located in each network configuration; the discriminatory behavior considering variations in the power flow in the transmission lines. © 2004 IEEE.

Nodal price simulation in competitive electricity markets

Locational Marginal Prices (LMP) are important pricing signals for the participants of competitive electricity markets, as the effects of transmission losses and binding constraints are embedded in LMPs [1],[2]. This paper presents a software tool that evaluates the nodal marginal prices considering losses and congestion. The initial dispatch is based on all the electricity transactions negotiated in the pool and in bilateral contracts. It must be checked if the proposed initial dispatch leads to congestion problems; if a congestion situation is detected, it must be solved. An AC power flow is used to verify if there are congestion situations in the initial dispatch. Whenever congestion situations are detected, they are solved and a feasible dispatch (re-dispatch) is obtained. After solving the congestion problems, the simulator evaluates LMP. The paper presents a case study based on the the 118 IEEE bus test network.

Projeto de Sistema Fotovoltaico Para as Naves Industriais da Zona Econômica Especial de Luanda-Bengo

Com os preços do barril de petróleo a atingir níveis nunca antes atingidos, cada vez mais há uma maior sensibilização para a importância das fontes renováveis de energia, não só pelo seu baixo custo de exploração, mas também pela ausência de poluição ambiental. A integração de sistemas fotovoltaicos nas edificações, começa a ter uma expressão significativa especialmente por ser uma forma de produção renovável. Pelo seu carácter renovável, vai ao encontro de objetivos ambientais, e é também desejável pelo seu carácter distribuído, produção próxima do consumo, evitando perdas de transporte e utilizando o recurso disponível no consumidor. No presente projeto é feita uma breve descrição do atual sistema elétrico angolano, nomeadamente o seu potencial, capacidade instalada, e perspetivas futuras de desenvolvimento do mesmo. Com uma perspetiva introdutória são abordadas as energias renováveis especialmente a energia fotovoltaica, terminando com as diferentes formas de produção e tecnologias existentes. São apresentados diferentes equipamentos, que, com as inúmeras combinações poderão vir a constituir um sistema técnico e financeiramente viável. Devido aos vários cenários possíveis (combinações entre equipamentos), foram usadas como instrumentos de apoio ferramentas informáticas que permitem o dimensionamento de sistemas fotovoltaicos, análise de compatibilidades, e simulação do seu funcionamento. Foram dimensionadas quatro opções de sistemas fotovoltaicos, a instalar nas naves industriais da Zona Económica Especial Luanda-Bengo, para uma mesma área de cobertura, seguido de um estudo económico, onde é feito uma comparação custo/benefício dos vários sistemas.

Vaihtosuuntauksen ja suodatuksen toteuttaminen tasasähkönjakeluverkossa

Pienjännitejakeluverkko Suomessa on toteutettu 400 V:n kolmivaiheisella vaihtosähköllä. Pienestä jännitteestä johtuen 20/0.4 kV:n muuntajat täytyy sijoittaa lähelle kuluttajaa, jotta siirtohäviöt eivät nouse liian suuriksi. Suuremman vaihto- tai tasajännitteen käyttö pienjännitejakelussa kasvattaisi verkon tehonsiirtokapasiteettia ja mahdollistaisi pidempien siirtomatkojen käytön. Käynnissä olevassa tutkimushankkeessa käsitellään vaihtoehtoa, jossa tasajännitettä käytettäisiin 20 kV:n verkon ja kuluttajan välisessä tehonsiirrossa ja kuluttajalla sijaitseva vaihtosuuntaaja muodostaisi tasasähköstä standardien mukaista yksi- tai kolmivaiheista vaihtosähköä. Tässä diplomityössä käsitellään tehoelektroniikan soveltamista kuluttajalle sijoitetussa vaihtosuuntaajassa. Työssä tarkastellaan yksivaiheisia invertteritopologioita, niiden ohjausta ja soveltamista erilaisissa vaihtosuuntaajaratkaisuissa sekä LC- ja LCL-suotimien soveltuvuutta invertterin lähtöjännitteen suodatukseen. Lisäksi esitellään erilaisia rakenneratkaisuja vaihtosuuntauksen toteutukseen ja tarkastellaan näiden järjestelmien vikatilanteita ja sähköturvallisuutta. Lopuksi käsitellään koko järjestelmän häviöitä ja hyötysuhdetta eri suodinkomponenteilla sekä kytkentätaajuuksilla ja esitellään laboratorioprototyyppi. Työssä saatiin selville, että puolisiltainvertteri ei sovellu suurten kondensaattorien vuoksi syöttämään verkkotaajuista kuormaa, vaan joudutaan käyttämään kokosiltainvertteriä. Kokosiltainvertterin ja LC- tai LCL-suotimen käsittävää kokonaisuutta tarkasteltaessa havaittiin, että pienimmät häviöt saavutetaan LC-suotimella 5 %:n ja LCL-suotimella 1 %:n särövaatimuksella. Hyötysuhdekäyrää tarkasteltaessa saatiin sama tulos läpi koko invertterin tehoalueen. Suotimen häviöiden tarkka laskenta on kuitenkin erittäin haasteellista, joten tulokset ovat suuntaa-antavia.

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.

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

Comparação de propostas para alocação dos custos de perdas na transmissão

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Practical and prospective slow light Surface Nanoscale Axial Photonics (SNAP) devices

Miniature slow light Surface Nanoscale Axial Photonics (SNAP) devices are reviewed. The fabrication precision of these devices is two orders of magnitude higher and the transmission losses are two orders of magnitude smaller than for any of the previously reported technologies for fabrication of miniature photonic circuits. In the first part of the report, a SNAP bottle resonator with a few nm high radius variation is demonstrated as the record small, slow light, and low loss 2.6 ns dispersionless delay line of 100 ps pulses. Next, a record small SNAP bottle resonator exhibiting the 20 ns/nm dispersion compensation of 100 ps pulses is demonstrated. In the second part of the report, the prospects of the SNAP technology in applications to telecommunications, optical signal processing, quantum computing, and microfluidics are discussed. © 2014 IEEE.