Produced power of wind, solar, run of the river, ocean thermal, tidal and wave power plants is respectively, dependent on wind velocity, sun radiation, river flow, temperature of ocean upstream, period & height of waves, tidal level or tidal stream velocity. Due to wide change in these quantities, produced power of these renewable resources changes a lot over time. As the penetration level of renewable resources in electric network is increased, reliability and other aspects of electric network may be affected that should be studied. Analytical method is not suitable to study uncertainties of output power of renewable resources in reliability analysis of electric network with these renewable power plants. Thus, the current research suggests Monte Carlo simulation method to study effect of renewable power plants on reliability indices. renewable power plants studied in the research are wind turbines, solar farms, wave energy converters, run of the river power plants, both types of tidal units, and ocean thermal energy conversion systems. Numerical studies are performed on test electric networks, to study these renewable resources impact on reliability indices of electric networks with renewable power plants. It is concluded from numerical outcomes that these renewable power plants improve reliability performance of electric network. However, due to the variation of renewable resources, the impact of renewable power plants on reliability performance of the electric network is less than the conventional units with the same capacity.

Expanding the use of renewable energy is a matter of concern in many countries. Many Japanese local municipalities are attempting to promote business creation using renewable energy as an opportunity for regional revitalization based on the policy of the Japanese government. renewable energy makes significant contributions to the region in terms of energy security, stable energy supply, and job creation. However, the most important contribution is enhancing residents’ attachment to their community and supporting actions that appeal to the community through renewable energy utilization. Residents should actively participate in regional revitalization, which leads to sustainable prosperity of the region. However, the current measures of local municipalities seem to be lacking such a viewpoint. This study aims to analyse the environmental and economic aspects as well as the social acceptance of establishing a bathing business that utilizes woody biomass as its heat source. This study also discussed how the generation of heat from woody biomass contributes to regional revitalization. The case study area was Kobe and Mt. Rokko, Japan. First, heat production and utilization was assumed for the forest resources in Mt. Rokko, and the annual biomass energy production potential was estimated. The annual costs of lumber cutting, transportation, and chipping, and the annual CO2 reduction by substituting bunker A with woody biomass for heat production were also estimated. A questionnaire survey was conducted to determine community awareness of energy production using woody biomass from Mt. Rokko. Principal component analysis, contingent valuation method, and path analysis were used to quantify respondents’ awareness.

This paper deals with a Flexible Flow Shop (FFS) scheduling problem with unrelated parallel machines and a renewable resource shared among the stages. The FFS scheduling problem is one of the most common manufacturing environments, in which there is more than a machine in at least one production stage. In such a system, to decrease the processing times, additional renewable resources are assigned to the jobs or machines, which can lead to a decrease in the total completion time. For this purpose, a Mixed Integer Linear Programming (MILP) model is proposed to minimize the maximum completion time (makespan) in an FFS environment. The proposed model is computationally intractable. Therefore, a Particle Swarm Optimization (PSO) algorithm, as well as a hybrid PSO and Simulated Annealing (SA) algorithm named SA-PSO, are developed to solve the model. Through numerical experiments on randomly generated test problems, the authors demonstrate that the hybrid SA-PSO algorithm outperforms the PSO, especially for large size test problems.