Classification and Analysis of Optimization Techniques for Integrated Energy Systems Utilizing Renewable Energy Sources: A Review for CHP and CCHP Systems

Abstract

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.

An overview of renewable energy technologies for the simultaneous production of high-performance power and heat

“…In addition, the discharge temperature does not change fuel consumption and therefore does not affect emissions. Rezania et al [64] study a variety of optimization methods that can improve the performance of integrated energy systems (IES), especially heat and combined energy (CHP) and cooling, heating, and combined energy (CCHP), using renewable energy sources; also it is shown by specifying the characteristics of each method along with accurate statistical reports. The results showed that classical optimization methods are no longer efficient for integrated energy systems and CHP, and innovative and modern optimization methods have been replaced.…”

Techno‐economic analysis of direct combustion and gasification systems for off‐grid energy supply: A case for organic rankine cycle and dual fluidized‐bed

“…Given that practical viability was one of the primary objectives, cost allocation for gasification and ORC systems was based on operational plants or the existing units in the market. In this context, the Gothenburg Biogas Gasification project located in Sweden [41], and the Scharnhauser Park ORC project located in Germany [42] were considered as the reference systems for unit cost allocation (investment, operating, and maintenance), development cost calculation, and fuel price estimation for the two proposed configurations; this was carried out using Equations ( 17)- (30).…”

“…For the application considered, a fluidized-bed gasifier was the best option since the possibility of up-scaling within a wide power range is provided [29]. In comparison with the fixed-bed technology, changes in fuel characteristics are more tolerable since the ratio of biomass to oxygen can be varied independently to maintain the temperature uniform throughout the bed [30]. The major drawback of such a compact and economic arrangement is the contamination of syngas with nitrogen, tars, and particles which drastically reduces the heating value of gaseous fuel.…”

Comparison Elicitation Techniques in Furniture Manufacture Requirement

“…The characteristics of requirements elicitation are described in several ways, namely collaborative (stakeholder expertise), observational (system understanding and knowledge) and contextual (user requirements) [15][16], contextual (user requirements) [17][18]. The elicitation techniques are classified into brainstorming, workshops, Joint Application Development (JAD), group work, ethnography, introspection and user scenarios [19][20].…”