Energy generation and its utilization is bound to increase in the following years resulting in accelerating depletion of fossil fuels, and consequently, undeniable damages to our environment. Over the past decade, despite significant efforts in renewable energy realization and developments for electricity generation, carbon dioxide emissions have been increasing rapidly. This is due to the fact that there is a need to go beyond the power sector and target energy generation in an integrated manner. In this regard, energy systems integration is a concept that looks into how different energy systems, or forms, can connect together in order to provide value for consumers and producers. Cogeneration and trigeneration are the two most well established technologies that are capable of producing two or three different forms of energy simultaneously within a single system. Integrated energy systems make for a very strong proposition since it results in energy saving, fuel diversification, and supply of cleaner energy. Optimization of such systems can be carried out using several techniques with regards to different objective functions. In this study, a variety of optimization methods that provides the possibility of performance improvements, with or without presence of constraints, are demonstrated, pinpointing the characteristics of each method along with detailed statistical reports. In this context, optimization techniques are classified into two primary groups including unconstrained optimization and constrained optimization techniques. Further, the potential applications of evolutionary computing in optimization of Integrated Energy Systems (IESs), particularly Combined Heat and Power (CHP) and Combined Cooling, Heating, and Power (CCHP), utilizing renewable energy sources are grasped and reviewed thoroughly. It was illustrated that the employment of classical optimization methods is fading out, replacing with evolutionary computing techniques. Amongst modern heuristic algorithms, each method has contributed more to a certain application; while the Genetic Algorithm (GA) was favored for thermoeconomic optimization, Particle Swarm Optimization (PSO) was mostly applied for economic improvements. Given the mathematical nature and constraint satisfaction property of Mixed-Integer Linear Programming (MILP), this method is gaining prominence for scheduling applications in energy systems.

The utilization of conventional sources of energy releases harmful pollutants to the environment causing global warming and acid rain. For that reason, it becomes necessary to use a non-depletable, sustainable and eco-friendly renewable energy as a mean of producing electricity. Malaysia is tropical country rich in resources beneficial in electricity generation as it is in equatorial region therefore it has an abundance of solar irradiance of average annually. In addition, Malaysia’s demand in electricity is increasing to 124,677 GWh by 2020. Therefore, the electricity generation from renewable sources in Malaysia is anticipated to grow in the future alongside the government endorsement due to its clean, eco-friendly and free source of energy which can highly reduce the dependency on oil and gas that emits harmful pollutants to the environment. This paper gives a comprehensive review on the renewable projects and researches in Malaysia, challenges that affect popularity of renewable energy in Malaysia and available and successful renewable energy system in Malaysia.

Several rural areas in Sarawak do not have access to electricity. In Sarawak, most investigations prioritize conventional sources despite the emergence of technology such as fuel cells. Furthermore, diversified investigations on stand-alone hybrid renewable energy systems in Sarawak are lacking. This study aims to fill this gap by investigating the feasibility of a stand-alone hybrid renewable energy system for rural applications in Sarawak, with a specific focus on the potential benefits of incorporating hydrogen. Different configurations, namely A: Photovoltaic, hydrogen, micro-hydro, B: hydrogen, micro-hydro, C: micro-hydro, D: photovoltaic, and micro-hydro was explored, each with its results, and comparisons were performed between them. By analyzing the system under various load patterns and conducting a sensitivity analysis, the researchers found that scenario A was the most cost-effective and reliable option for a longhouse in Sri Aman, with a net present cost of $148,687, cost of energy of $0.19/kWh, and initial capital of $107,207. Simultaneously, scenario A has the highest annual generating capacity of up to 116521kWh annually. Sensitivity analysis revealed that the electrolyzer could impact the cost of the system. The electrolyzer is the third and second most expensive in scenarios A and B. This research demonstrates the potential of alternative energy sources to improve access to electricity in rural areas. It highlights the importance of continued exploration into emerging technologies to ensure everyone can access safe and reliable electricity.

The use of a new control method for grid-connected inverters for reducing the output current harmonic distortion in a wide range of grid-connected distributed generation (DG) applications, including wind turbine (WT) and fuel cell (FC) inverters is proposed in this paper. The control method designed to eliminate main harmonics in a microgrid (MG) and between MG and point of common coupling (PCC) and responsible for the correction of the system unbalance. Another advantage of the proposed control method is that it can be easily adopted into the DG control system without the installation of extra hardware. The proposed control method is comprised of the synchronous reference frame method (SRF). Results from the proposed control method are provided to show the feasibility of the proposed approach.