Monthly Unit Commitment Model and Algorithm with Renewable Energy Generation Considering System Reliability

Abstract

With the sustained growth in renewable energy penetration, it is important to incorporate the interval prediction information of the wind and photovoltaic power into the monthly unit commitment model and introduce the system reliable rate as an indicator to measure the system reliability, which make an important contribution to deal with the volatility and randomness of the wind and photovoltaic power and ensure the economy and reliability of the monthly unit commitment. To enhance the practicality of the model and improve the solving ability, the multiobjective function composed by operating cost and reliable rate is transformed into a single-objective function by using the evaluation function based on geometric weighting method. An adaptive genetic algorithm (AGA) is used to solve the above problem when the prohibiting inbreeding strategy is adopted to replace the mutation operator, avoiding the hybridization between close relatives and containing the diversity of the population. Finally, the testing systems verify the validity and accuracy of the proposed model and algorithm.

A Novel way out to Unit Commitment Problem utilizing Evolutionary Particle Swarm Optimization

“…the unit with highest FLAPC) should be in off status, whereas the cheaper unit (unit with least FLAPC) should be in on status . The FLAPC for unit ‗j' is given in following equation (5). Priority Listing is the simplest method from a modeling viewpoint.…”

“…The expanded issue size unfavorably impacts the computational time and the nature of the arrangements. The non-heuristic quest calculations for example particle swarm optimization, evolutionary programming, genetic algorithms and ant colony optimization are competent to achieve over the limitations of traditional improvement forms [4][5]. Superb arrangements are gained by methods for these techniques which can switch complex nonlinear limitations.…”

Solution for renewable future

“…To meet this energy need, 77% of global demand refers to buying a home solar system. Therefore, the use of distributed renewable energy, intellectualization of infrastructure and the transition of consumers to active, prosumer behavior patterns is obvious [12][13][14][15][16].…”

Unit commitment based on modified firefly algorithm

“…1,2 The differences in features of generating units make UC a considerable problem. 1,4 UC is a tool for economic power dispatch. 5 The nonlinearity and large scale of power systems compound the solution to UC because it involves two steps, that is, examining the unit to be operated and determining the power-sharing quantity of the demand in each unit.…”