Abstract A multi-objective problem, namely, dynamic economic emission dispatch is simultaneously solved under several practical equality and inequality operating power constraints, while applying the load demand management (LDM) on 30,000 electric vehicles (EVs) during crest shaving and valley filling (CSVF) regions. A novel algorithm named orthogonal particle swarm optimization (OPSO) is proposed for such a problem. Ten thermal-generating units (TGUs) from power-generating systems (PGSs) in two Case Studies, with and without LDM on the load demand of 30,000 EVs, are tested. The comprehensive analysis results reveal that the quantity of emissions released by the 10 TGUs was remarkable affected and reduced around 4005 kg/day and the operating fuel cost was saved around $409/day when applying the LDM on the load demand of 30,000 EVs in the CSVF regions. This study provides important outcomes about the future operation of PGSs when applying the LDM strategy on a large-scale penetration of EVs in smart cities into a sustainable environment. The study outcomes contribute in making the PGS flexible, economical, environmental, stable, and reliable.

中文翻译：

---

智能城市环境下电动汽车削峰填谷负荷需求动态经济排放调度与负荷需求管理

摘要 对 30,000 辆电动汽车 (EV) 进行削峰填谷 (CSVF) 期间的负载需求管理 (LDM) 应用，同时解决多个实际等式和不等式运行功率约束下的多目标问题，即动态经济排放调度问题。 ) 地区。针对这样的问题，提出了一种称为正交粒子群优化（OPSO）的新算法。测试了两个案例研究中来自发电系统 (PGS) 的 10 个热发电单元 (TGU)，在 30,000 辆电动汽车的负载需求下，使用和不使用 LDM。综合分析结果显示，在负载需求为 30 时，应用 LDM 时，10 个 TGU 的排放量受到显着影响，减少了约 4005 公斤/天，运营燃料成本节省约 409 美元/天，CSVF 地区的 000 辆电动汽车。当将 LDM 策略应用于智能城市中电动汽车大规模渗透到可持续环境中时，这项研究提供了有关 PGS 未来运营的重要成果。研究结果有助于使 PGS 灵活、经济、环保、稳定和可靠。