Distributed generators have grown in significance for the technical and financial operation of electric power systems in recent years. The integration of these generators into the electrical ...distribution network (EDN) has experienced rapid growth, primarily driven by advancements in renewable energy sources (RESs), particularly photovoltaic distributed generators (PVDGs). With the increasing implementation of solar energy as a RES, designing the optimal integration of PVDGs into medium voltage direct current (MVDC) networks is crucial to comprehensively analyzing technical and economic factors. To address this issue, a new multiple objective function (MOF) is proposed, which combines various techno‐economic parameters such as total active power loss (APL), voltage deviation (VD), and the investment cost of PVDGs (ICPVDG) installed in the test system. The objective is to minimize the MOF simultaneously to achieve the optimal incorporation of PVDGs. This study aims to solve the allocation problems related to the location and size of PVDG units in the modified MVDC test systems IEEE 27 and 33‐bus. Simulation results demonstrate the superior accuracy and effectiveness of the equilibrium optimization algorithm (EOA) in achieving the minimum MOF. The utilization of multiple PVDG units reduces overall active power losses and improves voltage profiles across all buses.
Distributed generators are crucial for electric power systems, especially photovoltaic distributed generators (PVDGs). To optimize PVDG integration into medium voltage direct current (MVDC) networks, a new multiple objective function (MOF) is proposed. This function combines techno‐economic parameters like total active power loss, voltage deviation, and investment cost. The study aims to solve allocation problems in modified MVDC test systems IEEE 27 and 33‐bus. Simulation results show superior accuracy and effectiveness of the equilibrium optimization algorithm.
This paper proposes an original multi-criteria decision-making optimization algorithm to select the best solar panels in an existing market and optimally size the photovoltaic (PV) system for an ...electric vehicle parking lot (EVPL). Our proposed algorithm is called rank-weigh-rank (RWR), and it is compared to the well-known technique for order of preference by similarity to ideal solution (TOPSIS) optimization algorithm under the same conditions for validation purposes. Results show that the speed of our proposed algorithm (RWR) in finding the best solution increases exponentially compared to TOPSIS when the numbers of alternatives and criteria increase. Moreover, 77% is the probability of obtaining results with more than 80% accuracy compared to TOPSIS, which validates the efficiency of our algorithm. In addition, we were able to design an EVPL with a power self-sufficiency ratio of 60.8%, the energy self-sufficiency ratio of 74.7%, and a payback period of 10.58 years. Moreover, the renewable energy-based EVPL was able to reduce the power losses on the network by 95.7% compared to an EVPL without a renewable energy system and improve the voltage deviation.
This paper applies a relatively new optimization method, the Grey Wolf Optimizer (GWO) algorithm for Optimal Power Flow (OPF) of two-terminal High Voltage Direct Current (HVDC) electrical power ...system. The OPF problem of pure AC power systems considers the minimization of total costs under equality and inequality constraints. Hence, the OPF problem of integrated AC-DC power systems is extended to incorporate HVDC links, while taking into consideration the power transfer control characteristics using a GWO algorithm. This algorithm is inspired by the hunting behavior and social leadership of grey wolves in nature. The proposed algorithm is applied to two different case-studies: the modified 5-bus and WSCC 9-bus test systems. The validity of the proposed algorithm is demonstrated by comparing the obtained results with those reported in literature using other optimization techniques. Analysis of the obtained results show that the proposed GWO algorithm is able to achieve shorter CPU time, as well as minimized total cost when compared with already existing optimization techniques. This conclusion proves the efficiency of the GWO algorithm.
Optimal power flow (OPF) is one of the fundamental mathematical tools currently used to operate power systems within the technical limits of the transmission power system. To determine OPF, a highly ...non-linear complex problem, it is essential to research power system planning and control. This study presents a practical and trustworthy optimization approach for the OPF problem in electrical transmission power systems. Many intelligence optimization algorithms and methods have recently been developed to solve OPF, particularly the non-linear complex optimization problems. In this paper, a novel meta-heuristic algorithm called the mountain gazelle optimizer (MGO) is suggested for solving the OPF problem. The suggested algorithm applies the improved three single objective functions to the MGO algorithm for the best OPF issue control variable settings. Three objective functions that reflect the minimization of generating fuel cost, the minimizing of active power loss, and the minimizing of voltage deviations have been used to investigate and test the proposed algorithm on the standard IEEE 30-bus test system. The simulation results demonstrate the efficiency of the proposed MGO algorithm; the fuel costs are reduced by 11.407%, power losses are considerably decreased by 51.016%, and the voltage profile is significantly reduced by 91.501%. Furthermore, the outcomes produced by the proposed algorithm have also been contrasted with outcomes produced by applying other comparable optimization algorithms published in recent years. The optimal results are encouraging and demonstrate the resilience and efficacy of the suggested strategy.
In recent years, the incorporation of wind turbine distributed generation (WTDG) in addition to a battery energy storage system (BESS) into an electrical distribution network (EDN) has developed into ...a beneficial solution for ensuring a satisfying balance between energy generation and consumption. The principal approaches used to locate and size multiple WTDG and BESS units inside an EDN are described in this article. To optimize overall multi-objective functions, this research investigates the optimal planning of multiple hybrid WTDG and BESS units in an EDN. In the first scenario, injecting active power into the EDN is accomplished by installing WTDG. In contrast, in the second scenario, hybrid WTDG and BESS units are deployed concurrently to provide the EDN, taking into consideration the seasonal uncertainty of load–source power variation in order to approach the practical case, where there are many parameters to be optimized, considering different constraints, during the uncertain times and variable data of a load and power generator. The suggested work’s originality is in completely designing a novel multi-objective function (MOF) based on the sum of three technical metrics of the active power loss (APL), voltage deviation (VD), and operating time of the overcurrent relay (OTR). The proposed MOF is validated on the standard IEEE 69-bus distribution network by applying a new, recently published meta-heuristic algorithm called the Light Spectrum Optimizer (LSO) algorithm. The optimized outcomes revealed that the LSO showed good behavior in minimizing each parameter included in the MOF during the year season.
The literature covering Plug-in Electric Vehicles (EVs) contains many charging/discharging strategies. However, none of the review papers covers such strategies in a complete fashion where all ...patterns of EVs charging/discharging are identified. Filling a gap in the literature, we clearly and systematically classify such strategies. After providing a clear definition for each strategy, we provide a detailed comparison between them by categorizing differences as follows: complexity; economics and power losses on the grid side; ability to provide ancillary services for integrity of the power grid; operation aspects (e.g., charging timing); and detrimental impact on the EV, the power grid, or the environment. Each one of these comparison categories is subdivided into even more detailed aspects. After we compare the EV charging/discharging strategies, we further provide recommendations on which strategies are suitable for which applications. Then, we provide ratings for each strategy by weighting all aspects of comparison together. Our review helps authors or aggregators explore likely choices that might suit the specific needs of their systems or test beds.
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.
The increased integration of Electric Vehicles (EVs) into the distribution network can create severe issues—especially when demand response programs and time-varying electricity prices are applied, ...EVs tend to charge during the off-peak time to minimize the electricity cost. Hence, another peak demand might be created, and other solutions are required. Many researchers tried to solve the problem; however, limitations exist because of the decentralized topology of the network. The system operator is not allowed to control the end-users’ load due to security and privacy issues. To overcome this situation, we propose a novel data-energy management algorithm on the transformer’s level that controls the power demand profiles of the householders and exchange energy between them without violating their privacy and security. Our method is compared to an existing one in the literature based on a decentralized control strategy. Simulations show that our approach has reduced the electricity cost of the end-users by 3%, increased the revenue of the system operator, and reduced techno-economic losses by 50% and 42%, respectively. Our strategy shows better performance even with a 100% penetration level of EVs on the network, in which it respects the network’s constraints and maintains the voltage within the recommended limits.
High penetration levels of Plug-in Electric Vehicles (PEVs) could cause stress on the network and might violate the limits and constraints under extreme conditions, such as exceeding power and ...voltage limits on transformers and power lines. This paper defines extreme conditions as the state of a load or network that breaks the limits of the constraints in an optimization model. Once these constraints are violated, the optimization algorithm might not work correctly and might not converge to a feasible solution, especially when the complexity of the system increases and includes nonlinearities. Hence, the algorithm may not help in mitigating the impact of penetrating PEVs under extreme conditions. To solve this problem, an original algorithm is suggested that is able to adapt the constraints’ limits according to the energy demand and the energy needed to charge the PEVs. Different case scenarios are studied for validation purposes, such as charging PEVs under different state of charge levels, different energy demands at home, and different pricing mechanisms. Results show that our original algorithm improved the profiles of the voltage and power under extreme conditions. Hence, the algorithm is able to improve the integration of a high number of PEVs on the distribution system under extreme conditions while preserving its stability.
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.
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