ENHANCED LSHADESPACMA ALGORITHM FOR ENERGY HUB OPTIMIZATION INCORPORATING STOCHASTIC WIND AND SOLAR ENERGY

Abstract

Nowadays, with the use of various energy technologies, interest in integrated energy systems is increasing, where the energy hub(EH) is the most attractive in them. EH optimization problems are the complex and high-dimensional due to the combining the different energy sources and the generation of different demands at the output. For this reason, the meta-heuristic search algorithms needs to be used to solve the EH problems. In this study, a novel LSHADE with semi-parameter adaptation hybrid with CMA-ES including fitness-distance balance(FDB-LSHADESPACMA) was developed to solve EH optimization problems. Using the five input energy carriers and four output energy sources, different EH structures were created and three test systems were presented to the literature for the first time. Besides, two objective functions were used which are minimization of total cost and total loss. To validate the performance of FDB-LSHADESPACMA, it was applied on benchmark and EH optimization problems. In experimental study about EH optimization problems, six case studies were considered. Accordingly, the FDB-LSHADESPACMA was obtained 3292.2784mu, 1.6753pu, 5052.0203mu, 2.1126pu, 5217.2151mu, and 2.7051pu for Case-1, Case-2, Case-3, Case-4, Case-5, and Case-6, respectively. The simulation results demonstrated that FDB-LSHADESPACMA achieved successful performance for solving both EH optimization and benchmark problems.

Keywords

• Energy hub
• Optimization
• Fitness-Distance Balance
• LSHADESPACMA algorithm

STOKASTİK RÜZGAR VE GÜNEŞ ENERJİSİNİN BİRLEŞTİRİLDİĞİ ENERJİ HUB OPTİMİZASYONU İÇİN GELİŞTİRİLMİŞ LSHADESPACMA ALGORİTMASI

Abstract

Günümüzde çeşitli enerji teknolojilerinin kullanılmasıyla birlikte entegre enerji sistemlerine, özellikle enerji hub (EH) problemine olan ilgi artmaktadır. EH optimizasyon problemleri, farklı enerji kaynaklarının birleştirilmesi ve farklı taleplerin üretilmesi nedeniyle karmaşık ve yüksek boyutludur. Bu nedenle EH problemlerinin çözümü için meta-sezgisel arama algoritmalarının kullanılması gerekmektedir. Bu çalışmada, EH optimizasyon problemlerini çözmek için FDB-LSHADESPACMA olarak isimlendirilen uygunluk-mesafe dengesi tabanlı yarı parametre uyarlamalı LSHADE ile hibrit CMA-ES (LSHADE-SPACMA) algoritması önerilmiştir. Çalışmada, beş giriş enerji taşıyıcısı ve dört çıkış enerji kaynağı kullanılarak farklı EH yapıları oluşturulmuş ve üç test sistemi ilk kez literatüre sunulmuştur. Ayrıca, toplam maliyetin ve toplam kaybın minimizasyonu olmak üzere iki amaç fonksiyonu kullanılmıştır. Önerilen algoritmanın performansı, kıyaslama ve EH optimizasyon problemleri üzerinde test edilmiştir. EH optimizasyon problemlerine yönelik deneysel çalışmada altı durum çalışması dikkate alınmıştır. Buna göre, FDB-LSHADESPACMA Durum-1, Durum-2, Durum-3, Durum-4, Durum-5 ve Durum-6 için sırasıyla 3292.2784mu, 1.6753pu, 5052.0203mu, 2.1126pu, 5217.2151mu ve 2.7051pu değerlerini elde etti. Simülasyon sonuçları, FDB-LSHADESPACMA algoritmasının hem EH optimizasyonu hem de kıyaslama problemlerinin çözümünde üstün performans elde ettiğini göstermiştir.

Keywords

• Enerji hub
• Optimizasyon
• Uygunluk-Mesafe Dengesi
• LSHADESPACMA algoritması

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