A Modified Firefly Algorithm for Optimal Sizing and Siting of Voltage Controlled Distributed Generators in Distribution Networks

This paper presents a modified firefly algorithm for optimal placement and sizing of voltage controlled distributed generators in unbalanced distribution networks. The proposed algorithm modifies the traditional firefly method to be able to deal with the practically constrained optimization problems by proposing formulas for tuning the algorithm parameters and updating equations. The proposed algorithm rigidly determines the optimal location and size of the distributed generation units in order to minimize the system power loss without violating the system practical constraints.  The proposed algorithms is implemented in MATLAB and tested on the IEEE 69 bus and the IEEE 123 -nodes feeder, the results that are validated by comparing them to published results obtained from other competing methods shows the effectiveness, accuracy and speed of the proposed method

An optimal sizing framework for autonomous photovoltaic/hydrokinetic/hydrogen energy system considering cost, reliability and forced outage rate using horse herd optimization

The components outage of an energy system weakens its operation probability, which can affect the sizing of that system. An optimal sizing framework is presented for an autonomous hybrid photovoltaic/hydrokinetic/fuel cell (PV/HKT/FC) system with hydrogen storage to supply an annual load demand with forced outage rate (FOR) of the clean production resources based on real environmental information such as irradiance, temperature, and water flow. The sizing problem is implemented with the objective of cost of energy (COE) minimization and also satisfying probability of load supply (PLS) as a reliability constraint. The FOR effect of the photovoltaic and hydrokinetic resources is evaluated on the hybrid system sizing, energy cost, reliability, and also storage contribution of the system. Meta-heuristic horse herd optimization (HHO) algorithm with perfect capability on exploration and exploitation phases is used to solve the sizing problem. The results proved that the PV/HKT/FC configuration is the optimal option to supply the demand of an autonomous residential complex with the minimum COE and maximum PLS compared with the other system configurations. The results demonstrated the overlap of hydrogen storage with clean production resources to achieve an economic-reliable power generation system. The findings indicated that the COE is increased and the PLS is decreased due to the FOR increasing because of reducing the generation resources operational probability. The results demonstrated that the hydrogen storage level is increased with FOR increasing to maintain the system reliability level. Also, the sizing results indicated that the FOR of the hydrokinetic is more effective than the photovoltaic resources in increasing the system cost and undermining the load reliability. In sizing of the hybrid PV/HKT/FC system, the COE is obtained 1.57 $/kWh without considering the FOR and is achieved 1.66 and 1.63$/kWh considering the FOR (8%) for the hydrokinetic and photovoltaic resources, respectively. Moreover, the results cleared that the HHO is superior in comparison with particle swarm optimization (PSO), genetic algorithm (GA), and grey wolf optimizer (GWO) in the PV/HKT/FC system sizing with the lowest COE and higher reliability

Decentralized community energy management: Enhancing demand response through smart contracts in a blockchain network

The integration of distributed energy resources (DERs) and digital technologies has accelerated the transition to decentralized energy systems. Among these technologies, blockchain stands out for its ability to facilitate peer-to-peer (P2P) energy trading efficiently and securely. This paper explores the concept of P2P energy trading within community microgrid systems, leveraging blockchain-based smart contracts. The proposed system integrates an incentive-driven demand response program directly into the smart contract framework, offering real-time rewards for load-balancing contributions. By incorporating the microgrid’s Energy Management System (EMS) and transparently recording all transactions on the blockchain, the proposed platform provides detailed data and immediate reward distribution. At the core of our system lies the Supply to Demand Ratio (SDR), ensuring fair energy exchange within the community. Dynamic pricing, enabled by blockchain and Tether (USDT) cryptocurrency, adjusts to real-time market conditions, enhancing transparency and responsiveness in energy trading. This adaptive pricing model fosters a more equitable and efficient trading environment compared to static approaches. Moreover, this system is tailored for community microgrids, emphasizing a community-centric approach. Local prosumers serve as validators in the blockchain network, aligning energy management decisions with community needs and dynamics. This localized engagement promotes efficiency and participation, fostering resilient, sustainable, and user-centric energy landscapes. Through rigorous analysis, we demonstrate the system’s effectiveness in optimizing economic efficiency, reducing operational costs, and increasing compliance rates. By combining blockchain technology with community-focused design principles, the proposed platform represents a significant advancement towards self-sufficiency and resilience in local energy systems

A novel primary and backup relaying scheme considering internal and external faults in HVDC transmission lines

Discrimination of different DC faults near a converter end of a DC section consisting of a filter, a smoothing reactor, and a transmission line is not an easy task. The faults occurring in the AC section can be easily distinguished, but the internal and near-side external faults in the DC section are very similar, and the relay may cause false tripping. This work proposes a method to distinguish external and internal faults occurring in the DC section. The inputs are the voltage signals at the start of the transmission line and the end of the converter filter. The difference in voltage signals is calculated and given to an intelligent controller to detect and discriminate the faults. The intelligent controller is designed using machine learning (ML) and deep learning (DL) techniques for fault detection. The long short-term memory (LSTM-) based relay gives better results than other ML methods. The proposed method can distinguish internal from external faults with 100% accuracy. Another advantage is that a primary relay is suggested that detects faults quickly within a fraction of milliseconds. Nevertheless, another advantage is that a backup relay has been designed in case the primary relay cannot operate. Results show that the LSTM-based protection scheme provides higher sensitivity and reliability under different operation modes than the conventional traveling wave-based relay

A novel nature-inspired nutcracker optimizer algorithm for congestion control in power system transmission lines

In the restructured power system, where uncertainties are common, managing congestion becomes a crucial aspect of power system operation and control. Congestion management aims to alleviate the power system transmission line congestion while meeting the system constraints at minimal cost. This research introduces a generation rescheduling method for congestion management in the electricity market, leveraging an innovative nutcracker optimizer algorithm. The nutcracker optimizer algorithm, inspired by nutcrackers’ food accumulation mechanisms, is a recently developed nature-inspired algorithm. The efficacy of this proposed approach is assessed across modified IEEE 30-bus, and IEEE 118-bus test systems, considering the system parameters. The effectiveness of the proposed congestion management with the nutcracker optimizer algorithm is analyzed by comparing its results with those generated by other recent optimization techniques. Results demonstrated that the nutcracker optimizer algorithm surpasses other comparative methods, requiring fewer fitness function evaluations, avoiding local optima, and displaying encouraging convergence traits. Implementing this approach can assist the system operators in swiftly addressing contingencies, ensuring secure and reliable power system operation within a deregulated environment

Enhancing the control of doubly fed induction generators using artificial neural networks in the presence of real wind profiles

This study tackles the complex task of integrating wind energy systems into the electric grid, facing challenges such as power oscillations and unreliable energy generation due to fluctuating wind speeds. Focused on wind energy conversion systems, particularly those utilizing double-fed induction generators (DFIGs), the research introduces a novel approach to enhance Direct Power Control (DPC) effectiveness. Traditional DPC, while simple, encounters issues like torque ripples and reduced power quality due to a hysteresis controller. In response, the study proposes an innovative DPC method for DFIGs using artificial neural networks (ANNs). Experimental verification shows ANNs effectively addressing issues with the hysteresis controller and switching table. Additionally, the study addresses wind speed variability by employing an artificial neural network to directly control reactive and active power of DFIG, aiming to minimize challenges with varying wind speeds. Results highlight the effectiveness and reliability of the developed intelligent strategy, outperforming traditional methods by reducing current harmonics and improving dynamic response. This research contributes valuable insights into enhancing the performance and reliability of renewable energy systems, advancing solutions for wind energy integration complexities

A novel approach based on crow search algorithm for optimal selection of conductor size in radial distribution networks

The selection process of conductor size in radial distribution network is very essential issue to improve the performance of the network. The true conductor size selection leads to less power loss, achieve improvement of the bus voltage profile and obtain reduction in the annual operating cost of the system. This paper proposes a novel approach based on crow search algorithm (CSA) for optimal selection of the conductors in a radial distribution network. The CSA is a recent meta-heuristic algorithm which is based on the intelligent behavior of crows. The objective function presented in our work is the sum of conductor capital cost and the conductor energy loss cost. The proposed constraints are bus voltage limits and the current capacity of the conductor. The independent variables are the type and the size of conductor such that minimizing the proposed objective function. The proposed approach is applied on two different network topologies, the first one is 16-bus system and the second is large scale system of 85-bus system. A sensitivity analysis of the CSA controlling parameters are also studied for 16-bus system. The obtained results via CSA are compared to previous works’ results; the CSA results encourage the usage of the proposed approach in optimal selecting the conductor type and size in the distribution network

A New Study of Maximum Power Point Tracker Techniques and Comparison for PV Systems

The maximum power point tracker techniques vary in many aspects as simplicity, digital or analogical implementation, sensor required, convergence speed, range of effectiveness, implementation hardware,popularity, cost and in other aspects. This paper presents in details comparative study between two most popular  algorithm  technique  which  is  incremental  conductance  algorithm  and  perturb  and  observe algorithm.  Two  different  converters  buck  and  cuk  converter  use  for  comparative  in  this  study. Few comparisons such as efficiency, voltage, current and power output for each different combination have been recorded. Multi changes in irradiance, temperature by keeping voltage and current as main sensed parameter been done in the simulation. Matlab simulink tools have been used for performance evaluation on energy point. Simulation will consider different solar irradiance and temperature variations