Evolutionary computing in energy systems integration: Techniques and applications

In the foreseeable future, escalating energy production and consumption will rapidly deplete finite fossil fuel resources and cause significant ecological damage. Despite substantial progress in developing renewable energy and power generation technologies over the past decade, carbon dioxide emissions continue to surge at an alarming rate. This underscores the necessity of a holistic approach to energy generation, one that considers the entire power sector and its interconnected factors. The concept of energy systems integration highlights the potential synergies among diverse energy systems, offering significant benefits for both consumers and producers. Trigeneration and cogeneration, notable established technologies, can simultaneously produce multiple forms of energy framework. Integrated Energy Systems (IESs) show great promise in reducing energy consumption, diversifying fuel sources, and providing cleaner energy alternatives. Optimizing these systems requires a variety of strategies tailored to specific objectives. This study presents different optimization approaches aimed at enhancing system performance, both with and without constraints, each characterized by distinct methodologies and comprehensive statistical analyses. Optimization techniques in this domain are broadly categorized as constrained and unconstrained, each offering unique advantages and applications. Furthermore, the research delves into the expanding field of evolutionary computing and its potential applications in optimizing IESs. These applications include combined heat and power (CHP), combined cooling, heating, and power (CCHP), and the integration of renewable energy sources. Traditional optimization methods are gradually being supplanted by evolutionary computing techniques due to their effectiveness in addressing complex optimization challenges. Modern heuristic algorithms play a crucial role in optimizing specific components of energy systems. Unlike Particle Swarm Optimization (PSO), which is commonly used for economic upgrades, Genetic Algorithms (GA) excel in thermo-economic optimization. Additionally, Mixed-Integer Linear Programming (MILP) is gaining popularity for scheduling applications due to its mathematical rigor and ability to meet specific requirements. This study explores these various optimization methods and their applications, providing insights into how they can enhance the performance and sustainability of integrated energy systems. • · Reviews optimization in renewable energy with focus on CHP and CCHP-based integrated systems. • · Addresses lack of comprehensive reviews on optimization strategies for integrated energy systems. • · Proposes a novel classification of optimization methods based on computational constraints. • · Supports carbon neutrality by tackling thermodynamic, economic, and environmental challenges.