Regular Article

Optimal demand management of smart energy hybrid system based on multi-objective optimization problem

¹ Erbil Polytechnic University, Erbil Technical Engineering College, Information System Engineering Department, Erbil, Iraq
² Department of Mathematics and Teaching Methods in Primary Education, Tashkent State Pedagogical University, Tashkent, Uzbekistan
³ Department of Management, AL-Nisour University College, Baghdad, Iraq
⁴ Al-Manara College for Medical Sciences, Maysan, Iraq
⁵ Technical College, Imam Ja’afar Al-Sadiq University, Baghdad, Iraq
⁵ Mechanical Engineering, Faculty of Engineering, South Valley University, Egypt
⁶ Department of Energy, Faculty of Engineering, Eastern University Sri Lanka, Chenkaladi, Sri Lanka

^* Corresponding author: oriza.candra.in@gmail.com

Received: 26 April 2024
Accepted: 2 July 2024

Abstract

This paper presents a multi-objective problem for the energy planning of an energy hybrid system. The problem considers three main objectives: minimizing emission pollution and operation cost on the generation side, addressing consumers’ dissatisfaction with the electrical demand, and reducing deviation from optimal levels in the 24 hours ahead to flatten demand profiles. To achieve this, a demand flexibility strategy is implemented, involving the optimal shifting of electrical demand using deferrable loads. The proposed approach utilizes the augmented epsilon-constraint method to identify Pareto solutions for the objectives. Additionally, the TOPSIS decision-making technique is employed to select the optimal solution from the set of Pareto solutions. The effectiveness and robustness of the proposed approach are validated through two case studies. Overall, the paper highlights the importance of considering multiple objectives in the energy scheduling of hybrid systems and demonstrates the effectiveness of the proposed approach in achieving a balance between environmental, economic, and consumer satisfaction goals. The use of demand flexibility strategies and multi-objective optimization techniques can significantly improve the operation of energy systems, paving the way for more efficient energy management practices. The implementation of demand-side management has resulted in a significant reduction of 2.8% and 64.9% in the first and second objectives, respectively, compared to the absence of such management.