Iranian Electric Industry Journal of Quality and Productivity

fa بهینه‌سازی چندهدفه برای مدیریت یکپارچه منابع تولید پراکنده و شارژ/دشارژ خودروهای برقی در شبکه‌های توزیع با استفاده از مکانیزم قیمت‌گذاری حاشیه‌ای مکانی Multi-objective optimization for integrated management of distributed generation resources and electric vehicle charging/discharging in distribution networks using locational marginal pricing mechanism تخصصي Special پژوهشي Research این مقاله یک چارچوب نوین بهینه‌سازی چندهدفه برای مدیریت همزمان شارژ و دشارژ خودروهای برقی قابل اتصال به شبکه (PHEV) و واحدهای تولید پراکنده (DG) در شبکه‌های توزیع ارائه می‌دهد. رویکرد پیشنهادی با در نظر گرفتن همزمان سه بعد فنی، اقتصادی و زیست‌محیطی، به دنبال بهبود بهره‌وری شبکه و مدیریت هوشمند انرژی است. این مدل با بهره‌گیری از مکانیزم قیمت‌گذاری حاشیه‌ای مکانی (LMP) در باس‌های مجهز به خودروهای برقی و ایستگاه‌های شارژ، سه هدف اصلی را دنبال می‌کند: (۱) افزایش سود واحدهای DG و مالکان EV، (۲) بهبود شاخص‌های فنی شبکه از جمله کاهش تلفات و ارتقای قابلیت اطمینان، و (۳) کاهش آلایندگی ناشی از تولید برق. مسئله بهینه‌سازی پیشنهادی بر روی شبکه شعاعی ۸۳ باس IEEE پیاده‌سازی شده و با موفقیت به کمک الگوریتم بهینه‌سازی ازدحام ذرات (PSO) حل گردیده است. نتایج شبیه‌سازی نشان می‌دهند که چارچوب پیشنهادی توانایی چشمگیری در موازنه اهداف متضاد داشته و ابزار مناسبی برای اپراتورهای شبکه در جهت اتخاذ تصمیم‌های بهینه مبتنی بر اولویت‌های لحظه‌ای فراهم می‌کند.   This paper examines the coordinated charging and discharging of plug‑in electric vehicles (PEVs) in distribution networks with the dual aim of improving techno‑economic performance and mitigating environmental impacts. Rapid growth in electric vehicle adoption is transforming traditional power systems, creating new and highly stochastic demand patterns that challenge distribution network operation, protection, and planning. Uncontrolled or poorly coordinated charging can lead to feeder overloading, voltage violations, increased power losses, and protection miscoordination, while also raising system operating costs and emissions. At the same time, the inherent flexibility and distributed storage capability of PEV batteries offer significant opportunities for demand response, loss reduction, and provision of ancillary services if they are optimally managed. Motivated by these challenges and opportunities, the paper develops an optimization framework that explicitly models PEV charging and discharging decisions alongside distributed generation (DG) units within a distribution system. The problem is formulated as a multi‑objective optimization model that simultaneously minimizes power losses, energy not supplied (or interruption‑related costs), and the total operational cost associated with PEV participation and network operation. The framework incorporates locational marginal pricing (LMP) signals, grid constraints, and PEV owner preferences to determine spatio‑temporal charging patterns that are both system‑feasible and economically attractive. In addition, the model accounts for battery degradation costs through depth‑of‑discharge (DoD)‑based expressions, ensuring that intensive use of vehicle‑to‑grid (V2G) services does not impose unrealistic or uneconomic wear on PEV batteries. The proposed method is solved using a population‑based metaheuristic algorithm inspired by particle swarm optimization (PSO), which is well suited to handling the nonlinear, nonconvex, and mixed‑integer structure of the underlying problem. Decision variables include unit commitment of DGs, charging/discharging schedules of PEV fleets, and several network operation parameters over a 24‑hour horizon. The optimization evaluates different operating scenarios with varying levels of PEV penetration and DG integration, allowing the authors to compare uncoordinated, partially coordinated, and fully coordinated charging strategies. Performance indices such as total cost, energy losses, reliability‑related costs, and emission indices (e.g., <m:omath></m:omath><m:omath><m:r>C</m:r><m:ssub><m:ssubpr><m:ctrlpr></m:ctrlpr></m:ssubpr><m:e><m:r>O</m:r></m:e><m:sub><m:r>2</m:r></m:sub></m:ssub></m:omath> , <m:omath><m:r>N</m:r><m:ssub><m:ssubpr><m:ctrlpr></m:ctrlpr></m:ssubpr><m:e><m:r>O</m:r></m:e><m:sub><m:r>x</m:r></m:sub></m:ssub></m:omath> , and <m:omath><m:r>S</m:r><m:ssub><m:ssubpr><m:ctrlpr></m:ctrlpr></m:ssubpr><m:e><m:r>O</m:r></m:e><m:sub><m:r>2</m:r></m:sub></m:ssub></m:omath> ) are used to assess the effectiveness of the scheduling framework. Simulation studies are carried out on a test distribution network to demonstrate the applicability and benefits of the proposed scheme. Results show that coordinated PEV charging and discharging can significantly reduce active power losses and distribution network operating costs compared with conventional uncoordinated charging. The model also achieves noticeable improvements in reliability indices by lowering expected interruption costs through better utilization of PEV storage during operation periods. Furthermore, by shaping the net load profile and enabling higher penetration of clean distributed resources, the framework contributes to emission reductions relative to baseline operation. Sensitivity analyses on the level of PEV penetration, DG capacity, and pricing structures highlight how economic signals and technical constraints jointly influence optimal scheduling outcomes.     خودروهای برقی - قیمت‌گذاری حاشیه‌ای مکانی – مدیریت شارژ و دشارژ- بهینه سازی Electric Vehicles , Location Marginal Pricing , Charging and Discharge Management , Optimization 44 59 http://ieijqp.ir/browse.php?a_code=A-10-101-2&slc_lang=fa&sid=1 Mohsen Asgari محسن عسگری mohsenasgari13631363@gmail.com 4172190170 10031947532846007550 No Ehsan Azad-Farsani احسان آزاد فارسانی e.azad@iut.ac.ir 4679825588 10031947532846007551 Yes Amir Hosseini امیر حسینی s.hosseini@iut.ac.ir 0987654321 10031947532846007552 No