Optimal design of wind energy generation in electricity distribution network based on technical-economic parameters

In order to satisfy electricity customers and avoid some environmental constraints and problems, the transition to renewable energy sources has become increasingly important given their advantages and benefits, such as reducing pollution and improving the reliability of the targeted distribution system. In this paper, several state-of-the-art metaheuristic optimisation algorithms are used to investigate the optimal setting and sizing of wind turbines (WTs) when connected to the electricity distribution network (EDN). The selected algorithms were implemented to optimise and minimise a multi-objective function (MOF) considered as the sum of the techno-economic parameters of total active power loss (TAPL), total voltage deviation (TVD) and investment cost of the WTG (ICWTG) when the daily uncertainties and variations of the load-source powers are taken into account. The effectiveness of the selected algorithms was validated on the two standard test systems IEEE 33-bus and 69-bus. The simulation results in this paper showed the superiority of the Gorilla Troops Optimizer (GTO) algorithm compared to other new metaheuristic optimisation algorithms in terms of providing the best optimised results. Accordingly, the GTO algorithm showed excellent effectiveness and robustness in determining the optimal setting and sizing of the WTG units in EDN. Thus, the daily active power losses were reduced to 1,415 MWh for the first test system and 1,072 MWh for the second test system, while also improving the bus voltage profiles and favouring the investment costs of the installed WTG units, all with daily uncertainties in terms of load demand and WTG power variations

Chaos Game Optimization Algorithm for Parameters Identification of Different Models of Photovoltaic Solar Cell and Module

In order to achieve the optimum feasible efficiency, the electrical parameters of the photovoltaic solar cell and module should always be thoroughly researched. In reality, the quality of PV designs can have a significant impact on PV system dynamic modeling and optimization. PV models and calculated parameters, on the other hand, have a major effect on MPPT and production system efficiency. Because a solar cell is represented as the most significant component of a PV system, it should be precisely modeled. For determining the parameters of solar PV modules and cells, the Chaos Game Optimization (CGO) method has been presented for the Single Diode Model (SDM). A set of the measured I-V data has been considered for the studied PV design and applied to model the RTC France cell, and Photowatt-PWP201 module. The objective function in this paper is the Root Mean Square Error (RMSE) between the measured and identified datasets of the proposed algorithm. The optimal results that have been obtained by the CGO algorithm for five electrical parameters of PV cell and model have been compared with published results of various optimization algorithms mentioned in the literature on the same PV systems. The comparison proved that the CGO algorithm was superior

Zwiększenie efektywności energetycznej dla optymalnej integracji wielu fotowoltaicznych generatorów rozproszonych przy użyciu strategii kontroli masy bezwładności w algorytmach PSO

Recently, interest in incorporating distributed generators (DGs) into electrical distribution networks has significantly increased throughout the globe due to the technological advancements that have led to lowering the cost of electricity, reducing power losses, enhancing power system reliability, and improving the voltage profile. These benefits can be maximized if the optimal allocation and sizing of DGs into a radial distribution system (RDS) are properly designed and developed. Getting the optimal location and size of DG units to be installed into an existing RDS depends on the various constraints, which are sometimes overlapping or contradicting. In the last decade,meta-heuristic search and optimization algorithms have been frequently developed to handle the constraints and obtain the optimal DG location and size. This paper proposes an efficient optimization technique to optimally allocate multiple DG units into a RDS. The proposed optimization method considers the integration of solar photovoltaic (PV) based DG units in power distribution networks. It is based on multi-objective function (MOF) that aims to maximize the net saving level (NSL), voltage deviation level (VDL), active power loss level (APLL), environmental pollution reduction level (EPRL), and short circuit level (SCL). The proposed algorithms using various strategies of inertia weight particle swarm optimization (PSO) are applied on the standard IEEE 69-bus system and a real 205-bus Algerian distribution system. The proposed approach and design of such a complicated multi-objective functions are ultimately to make considerable improvements in the technical, economic, and environmental aspects of power distribution networks. It was found that EIW-PSO is the best applied algorithm as it achieves the maximum targets on various quantities; it gives 75.8359%, 28.9642%, and 64.2829% for the APLL, EPRL, and VDL, respectively, with DG units’ installation in the IEEE 69-bus test system. For the same number of DG units, EIW-PSO gives remarkable improved performance with the Adrar City 205-bus test system; numerically, it shows 72.3080%, 22.2027%, and 63.6963% for the APLL, EPRL, and VDL, respectively. The simulation results of this study prove that the proposed algorithms exhibit higher capability and efficiency in fixing the optimum DG settings.Ostatnio zainteresowanie włączeniem generatorów rozproszonych do sieci dystrybucji energii elektrycznej znacznie wzrosło na całym świecie ze względu na postęp technologiczny, który doprowadził do obniżenia kosztów energii elektrycznej, zmniejszenia strat mocy, zwiększenia niezawodności systemu elektroenergetycznego i poprawy profilu napięcia. Korzyści te można zmaksymalizować, jeśli opracuje się i zaprojektuje optymalną alokację i wielkość generatorów rozproszonych w promieniowym systemie dystrybucji. Uzyskanie optymalnej lokalizacji i wielkości jednostek generatorów rozproszonych, które mają być zainstalowane w istniejącym promieniowym systemie dystrybucji, zależy od różnych ograniczeń, które czasami nakładają się lub są sprzeczne. Aby poradzić sobie z ograniczeniami i uzyskać optymalną lokalizację i rozmiar generatora rozproszonego, w ostatniej dekadzie często opracowywano metaheurystyczne algorytmy wyszukiwania i optymalizacji. W niniejszym artykule zaproponowano skuteczną technikę optymalizacji, aby przydzielić wiele jednostek generatorów rozproszonych do promieniowego systemu dystrybucji. Zaproponowana metoda optymalizacji uwzględnia integrację jednostek generatorów rozproszonych opartych na ogniwach fotowoltaicznych w sieciach dystrybucji energii. Opiera się na funkcji wielokryterialnej, która ma na celu maksymalizację poziomu oszczędności netto, poziomu odchylenia napięcia, poziomu utraty mocy czynnej, poziomu redukcji zanieczyszczenia środowiska i poziomu zwarcia. Zaproponowane algorytmy wykorzystujące różne strategie optymalizacji roju cząstek o masie bezwładności (PSO) są stosowane w standardowym systemie IEEE 69-autobus oraz w rzeczywistym algierskim systemie dystrybucji autobusu 205. Proponowane podejście i projekt tak skomplikowanych, wielozadaniowych funkcji ma ostatecznie doprowadzić do znacznej poprawy technicznych, ekonomicznych i środowiskowych aspektów sieci dystrybucyjnych. Stwierdzono, że algorytm EIW-PSO jest najlepszy do zastosowania w systemie testowym IEEE 69-bus, ponieważ osiąga maksymalne cele dla różnych wielkości: 75,8359%, 28,9642% i 64,2829% odpowiednio dla utraty mocy czynnej, poziomu redukcji zanieczyszczenia środowiska i poziomu odchylenia napięcia w procesie instalacji jednostek rozproszonych. Dla tej samej liczby generatorów rozproszonych, EIW-PSO zapewnia znacznie lepszą wydajność w testach autobusów 205 w mieście Adrar; liczbowo: 72,3080%, 22,2027% i 63,6963% odpowiednio dla utraty mocy czynnej, poziomu redukcji zanieczyszczenia środowiska i poziomu odchylenia napięcia. Wyniki symulacji tego badania dowodzą, że zaproponowane algorytmy wykazują większą zdolność i skuteczność w ustalaniu optymalnych ustawień generatorów rozproszonych

Multi Dimension-Based Optimal Allocation of Uncertain Renewable Distributed Generation Outputs with Seasonal Source-Load Power Uncertainties in Electrical Distribution Network Using Marine Predator Algorithm

In the last few years, the integration of renewable distributed generation (RDG) in the electrical distribution network (EDN) has become a favorable solution that guarantees and keeps a satisfying balance between electrical production and consumption of energy. In this work, various metaheuristic algorithms were implemented to perform the validation of their efficiency in delivering the optimal allocation of both RDGs based on multiple photovoltaic distributed generation (PVDG) and wind turbine distributed generation (WTDG) to the EDN while considering the uncertainties of their electrical energy output as well as the load demand’s variation during all the year’s seasons. The convergence characteristics and the results reveal that the marine predator algorithm was effectively the quickest and best technique to attain the best solutions after a small number of iterations compared to the rest of the utilized algorithms, including particle swarm optimization, the whale optimization algorithm, moth flame optimizer algorithms, and the slime mold algorithm. Meanwhile, as an example, the marine predator algorithm minimized the seasonal active losses down to 56.56% and 56.09% for both applied networks of IEEE 33 and 69-bus, respectively. To reach those results, a multi-objective function (MOF) was developed to simultaneously minimize the technical indices of the total active power loss index (APLI) and reactive power loss index (RPLI), voltage deviation index (VDI), operating time index (OTI), and coordination time interval index (CTII) of overcurrent relay in the test system EDNs, in order to approach the practical case, in which there are too many parameters to be optimized, considering different constraints, during the uncertain time and variable data of load and energy production

Multi Dimension-Based Optimal Allocation of Uncertain Renewable Distributed Generation Outputs with Seasonal Source-Load Power Uncertainties in Electrical Distribution Network Using Marine Predator Algorithm

In the last few years, the integration of renewable distributed generation (RDG) in the electrical distribution network (EDN) has become a favorable solution that guarantees and keeps a satisfying balance between electrical production and consumption of energy. In this work, various metaheuristic algorithms were implemented to perform the validation of their efficiency in delivering the optimal allocation of both RDGs based on multiple photovoltaic distributed generation (PVDG) and wind turbine distributed generation (WTDG) to the EDN while considering the uncertainties of their electrical energy output as well as the load demand’s variation during all the year’s seasons. The convergence characteristics and the results reveal that the marine predator algorithm was effectively the quickest and best technique to attain the best solutions after a small number of iterations compared to the rest of the utilized algorithms, including particle swarm optimization, the whale optimization algorithm, moth flame optimizer algorithms, and the slime mold algorithm. Meanwhile, as an example, the marine predator algorithm minimized the seasonal active losses down to 56.56% and 56.09% for both applied networks of IEEE 33 and 69-bus, respectively. To reach those results, a multi-objective function (MOF) was developed to simultaneously minimize the technical indices of the total active power loss index (APLI) and reactive power loss index (RPLI), voltage deviation index (VDI), operating time index (OTI), and coordination time interval index (CTII) of overcurrent relay in the test system EDNs, in order to approach the practical case, in which there are too many parameters to be optimized, considering different constraints, during the uncertain time and variable data of load and energy production