Solving the combined heat and power economic dispatch problems by an improved genetic algorithm and a new constraint handling strategy

Economic Dispatch of Renewable Generators and BESS in DC Microgrids Using Second-Order Cone Optimization

“…In the last decades, the development of power electronics has allowed increasing the levels of penetration of renewable energy resources in electricity networks and the creation of new kinds of network concepts as microgrids (MGs) or high-voltage direct current [1,2]. The MGs can be operated in alternating current (AC) and direct current (DC); However, the DCMGs present several advantages over AC counterparts, such as: (i) no need of synchronizing generators, (ii) less number of power converters, (iii) lower losses, (iv) better voltage profiles, and (v) do not require analysis and controls of reactive power and frequency [3].…”

“…Additionally, the DCMGs go hand-in-hand with the global concerns about the effects of global warming since they reduce greenhouse effects and the cost of energy generation and transportation [4]. However, the integration of renewable energy resources has a great disadvantage that lies in the great variability of their primary energy resources (e.g., solar radiation or wind speed) [1]. In order to reduce this disadvantage, the MGs include devices that allow the storage of electrical energy well-known as battery energy storage systems (BESSs) [5].…”

Economic Dispatch of BESS and Renewable Generators in DC Microgrids Using Voltage-Dependent Load Models

“…The most studied renewable power generation systems are photovoltaic solar and wind generation technologies, which produce sustainable energy and reduce greenhouse gas emissions [3] by extracting energy from solar radiation and wind speed for conversion to electrical energy. Nevertheless, because of its inherently stochastic nature and dependence on weather conditions, this type of generation is associated with many uncertainties, which must be taken into account during electrical planning processes and even short-term grid operation (typically, as economic dispatch problems) [4].…”

An improved genetic algorithm for optimizing ensemble empirical mode decomposition method

“…To the best of our knowledge, there is no such research that focuses on how to select these two parameters more scientifically and selfadaptively. Consequently, the GA algorithm is introduced to address the parameter selecting issue in EEMD, owing to its outstanding optimization capability (Song, Zhang, Song, & Chen, 2019;Zou, Li, Kong, Ouyang, & Li, 2019).…”

“…Traditional selection operators are determined by the roulette selection and the tournament selection (Song et al, 2019). Owing to the randomness property of these methods, the results are usually unstable and the individual with high fitness will be missed sometimes (Zou et al, 2019). Some studies demonstrated that the evolution process of the population was based on the diversity of the individual, and the decrease of individual diversity often led to the convergence to local optima.…”