Optimization of Security Constrained Economic Dispatch for Integrated Renewable Energy Systems
| dc.contributor.advisor | Getachew Bekele (PhD) | |
| dc.contributor.author | Shewit Tsegaye | |
| dc.date.accessioned | 2023-12-14T14:49:55Z | |
| dc.date.available | 2023-12-14T14:49:55Z | |
| dc.date.issued | 2021-06 | |
| dc.description.abstract | One way of noticing the importance of electricity in our daily lives is when sudden interruption or blackout occurs. Considering a power system with Integrated Renewable Energy Systems (IRES) in which power supply interrupts every time it rains, such power system can cause serious damage to different types of loads connected, service centers and production plants. The main cause is the sudden increase or decrease in power output. According to Ethiopian electric power-network blackout report (2013-2016), 15 major blackouts were reported in three years’ time. Production plants and service centers were down for an average of four months a year. Natural incidents, equipment failure, and supply-demand mismatch collectively called contingencies cause most of these blackouts. The first challenging aspect of power system operation is that electrical energy, unlike other commodities; is difficult to store in significant amounts. Implying that electrical power must be consumed at same time it is generated. For a reliable supply of power, it is therefore essential to maintain the balance between generation and demand. This aspect requires an accurate method of balancing generation and demand considering generation limits, transmission security constraints, contingencies, and uncertainties. The other challenging aspect is the intermittency and variability of renewable energy sources. With increasing emphasis on improving efficiency and utilizing more renewable energy to mitigate climate change effects, power industry is confronted with such generation–demand mismatch challenges. These challenges are related to intermittency and non-dispatch ability of IRES. One of the daily power-system operation tasks that coins these challenges is Security-Constrained Economic Dispatch (SCED). SCED is a process of allocating generation levels to generating units to entirely and economically supply the load while satisfying security constraints. Practical power system economic dispatch is multi objective, constrained, and stochastic, as it has to consider the aforementioned challenges. Practically a solution method that can cope up with the varying generation is needed. This Ph.D. dissertation presents hybrid Genetic Algorithm-Hopfield Neural Network (GA-HNN) based optimization of SCED for IRES that address power mismatch problems of the Ethiopian power grid. Hopfield neural network can learn the stochastic behavior of varying generation and genetic algorithm can improve the convergence of global maxima by both reproducing and mutation the top solutions.This dissertation encompasses four main contributions. First, a review on recent trends and state of the art of SCED applied for renewable energy sources and hybrid systems is articulated. Second, development of global search algorithms that provide approximate solutions for SCED problem, and mathematical modelling of the objective functions of IRES is carried out. Third, study and assessments of security parameters with credible contingencies and uncertainty involving determination of the effect of contingencies and security constraints corresponding to renewable energy sources is made. Finally, optimal generation dispatch of modified IEEE 118 bus system and Ethiopian renewable energy system using hybrid GA-HNN is presented. According to the results obtained, hybrid GA-HNN helps to determine SCED global optimum solution of integrated, intermittent renewable energy systems. The obtained results include saving 0.519 million $/MW within 24 hours of operation at power loss of only 35.23 MW. This makes the proposed approach a strong financial solution in renewable energy markets. Utilizing hybrid GA-HNN resulted in the reduction of power mismatch by 23%. This mismatch enables power system operator deal with the unserved customers and unserved energy produce. Moreover, number of recursive blackouts were reduced by 12.36 % and execution time of the solution method by 56.89 %. | |
| dc.identifier.uri | http://etd.aau.edu.et/handle/123456789/974 | |
| dc.language.iso | en_US | |
| dc.publisher | Addis Ababa University | |
| dc.subject | Artificial intelligence, Hopfield neural network, Genetic algorithms, hybrid Genetic Algorithm-Hopfield Neural Network, Renewable energy system, and Security constrained economic dispatch. | |
| dc.title | Optimization of Security Constrained Economic Dispatch for Integrated Renewable Energy Systems | |
| dc.type | Thesis |