Multi-Objective Optimization and Performance Assessments of an Integrated Energy System Based on Fuel, Wind and Solar Energies

Abstract

The integrated energy system (IES) is an efficient method for improving the utilization of renewable energy. This paper proposes an IES based on fuel, wind and solar energies, following an optimization study focused on determining optimal device capacities. The study included gas turbines, wind turbines, solar photovoltaic panels, ground source heat pumps, absorption chillers/heaters, batteries, and thermal storage. Objectives were incorporated into the optimization model for the overall performance of the IES; these included the primary energy saving rate, annual cost-saving rate, and carbon dioxide emission reduction. Then, the nondominated sorting genetic algorithm II was employed to solve the optimization problem for multiple objectives. Ultimately, the verification and sensitivity analyses of the optimization method were achieved by a case study of hospital buildings in Harbin. The optimization results indicated a primary energy saving rate, annual cost saving rate, and carbon dioxide emission reduction rate of 17.3%, 39.8%, and 53.8%, respectively. The total installed capacity for renewable energy generation accounted for 64.5% of performance optimization. Moreover, the price of natural gas affected the economic indicators of the IES–but failed to impact energy consumption indicators.

Hierarchical Optimization and Grid Scheduling Model for Energy Internet: A Genetic Algorithm-Based Layered Approach

“…Figure 14 shows the algorithm optimization vs. the number of iterations. It can be seen that the number of iterations of (Li and Zhao, 2021) algorithm is 29, which is better than the 37 iterations of (Wang et al, 2017) algorithm; however, the optimal solution corresponding to the proposed algorithm is better than the (Li and Zhao, 2021) algorithm.…”

“…Although the convergence speed of the proposed algorithm is lower than that of the (Li and Zhao, 2021) algorithm, it jumps out of the local optimum, and the optimization result is better than the (Wang et al, 2017) algorithm. The proposed algorithm exerts its powerful optimization performance in the application of energy interconnection microgrid system operation strategy.…”

“…To verify the performance of the algorithm proposed in this article, (Li and Zhao, 2021) and (Wang et al, 2017)'s algorithms and the proposed algorithms were compared, and the optimization solution of a typical summer day was taken as an example. Figure 14 shows the algorithm optimization vs. the number of iterations.…”

“…Studies have (Masood and Ali, 2013;Zhang L. et al, 2020) established an optimal configuration model of the multienergy system with energy storage with the goal of configuration economy, and it achieved the optimal configuration effect. Some studies have (Li D. et al, 2020;Chen et al, 2021;Li and Zhao, 2021) focused on the operational economy, with environmental protection as their goal, and the optimal dispatch model of a combined cooling, heating, and power system was established. Other studies (Eshraghi et al, 2019;Yang et al, 2021) aimed at the operation economy of the integrated energy system and established an optimal dispatch model of the multienergy system, which achieves the optimal operation effect.…”

An Improved Deep Reinforcement Learning Method for Dispatch Optimization Strategy of Modern Power Systems

“…Since the beginning of the 21st century, higher requirements for energy conservation, emission reduction, and sustainable development have been put forward as a result of the increasing pressure from the use of global resources. Thus, clean energy has gained much attention, which further accelerates the global energy transformation [ 1 , 2 , 3 ]. At present, the commonly used clean energy sources include wind energy, solar energy, and tidal energy.…”