Voltage Deviation Research Articles

Optimal Reactive Power Dispatch and Demand Response in Electricity Market Using Multi-Objective Grasshopper Optimization Algorithm

Optimal Reactive Power Dispatch (ORPD) is a power system optimization tool that modifies system control variables such as bus voltage and transformer tap settings, and it compensates devices’ Volt Ampere Reactive (VAR) output. It is used to decrease real power loss, enhance the voltage profile, and promote stability. Furthermore, several issues have been faced in electricity markets, such as price volatility, transmission line congestion, and an increase in the cost of electricity during peak hours. Programs such as demand response (DR) provide system operators with more control over how small customers participate in lowering peak-hour energy prices and demand. This paper presents an extensive study on ORPD methodologies and DR programs for lowering voltage deviation, limiting cost, and minimizing power losses to create effective and economical operations systems. The main objectives of this work are to minimize costs and losses in the system and reduce voltage variation. The Grasshopper Optimization Algorithm (GOA) and Dragonfly Algorithm (DA) have been implemented successfully to solve this problem. The proposed technique has been evaluated by using the IEEE-30 bus system. The results obtained by the implementation of demand response systems show a considerable reduction in costs and load demands that benefit consumers through DR considerations. The results obtained from the GOA and DA are compared with those generated by other researchers and published in the literature to ascertain the algorithm’s efficiency.

Collaborative deployment of multiple reinforcement methods for network‐loss reduction in distribution system with seasonal loads

AbstractThe integration of seasonal loads, such as cereal baking and aquatic‐product processing loads, often leads to significant voltage deviations and severe peak loads of the distribution system during specific periods, resulting in increased network losses. Traditional approaches for reducing network losses are becoming less effective and cost‐efficient due to the spatiotemporally uneven distribution characteristics of seasonal loads. To address this issue, this study proposes an optimisation model that collaboratively integrates mobile energy storage, switching capacitors, and tie lines to minimise annual network losses in special planning scenarios affected by seasonal loads. The deployment strategies of multiple reinforcement methods are thoroughly analysed, greatly enhancing the explainability and feasibility of the collaborative deployment model. Then, the proposed model is reformulated to a mixed‐integer linear programming model using the inscribed regular dodecagon approximation approach, thereby making it trackable for state‐of‐the‐art solvers. To illustrate the effectiveness of the model, case studies are conducted on a unique 55‐bus distribution system located in East China, which contains feeders with substantial seasonal variation aquaculture loads and with general loads. The effectiveness of multiple reinforcement methods is thoroughly analysed through detailed numerical results. Furthermore, a sensitivity analysis of the investment budget is conducted.

Voltage regulation and energy loss minimization for distribution networks with high photovoltaic penetration and EV charging stations using dual-stage model predictive control

The widespread integration of photovoltaic (PV) units and controllable loads like electric vehicles (EVs) might cause uncertain voltage fluctuations quite frequently. The reactive power from distributed generation (DG) units and EV charging stations (EVCSs) can effectively be used along with conventional devices like on-load tap changer (OLTC) to successfully control network voltages in real-time, with multi-time scale coordination. However, the control of a large number of available resources, with reliable communication among them becomes a complex task. Moreover, the substantial real-time data set amassed through the deployment of measurement devices across the network increases both cost and computational intricacies. This paper presents a novel approach, employing a time decomposition-based dual-stage model predictive control (MPC) with a reduced model control framework for voltage control and energy loss minimization in active distribution networks (ADNs), by significantly reducing the number of measuring devices. A minimum global set of available control resources is identified for real-time control, aiming to mitigate control complexity and minimize the demand for heavy communication. The proposed control strategy is validated on the 33-bus distribution network and modified IEEE 123-bus distributed network, under high PV penetration and EV charging stations. It is seen that the proposed reduced model framework with very limited measuring devices and control equipment can effectively regulate the voltages with a standard deviation of 0.0059 p.u. and 0.0035 p.u. as compared to the full order system model, for 33-bus network and IEEE 123-bus network, respectively. Furthermore, there is a net 23.24% reduction in energy losses when power loss minimization is considered along with the minimization of voltage deviations in the 33-bus network.

Adaptive control strategy for microgrid inverters based on Narendra model

In recent years, microgrid technology has been widely studied and applied. However, with times developing, the installed capacity of distributed power generation devices has been improved, and work is being carried out in increasingly complex situations, resulting in a decline in the control performance of microgrids. In view of this, to effectively improve inverter’s control performance, research is conducted on the fusion of Narendra model and adaptive control strategies for real-time voltage correction and compensation in complex situations. Compared to traditional inverters, inverters under research methods have faster voltage recovery speed when encountering load switching, and can recover in about one cycle, with good control performance. In the comparison between the improved inverter adaptive control system and the inverter adaptive system, the improved inverter voltage recovery speed is faster, can be restored within one cycle, and the control effect of the inverter is better. The harmonic rate of the port voltage has decreased from 10.43 to 1.92%. The applicability of the research method was verified. It indicats that the research method can improve inverter’s control effect and solve problems such as voltage deviation, three-phase asymmetry, harmonic pollution, etc. that are easily generated by the output terminal voltage. Simultaneously, research has provided theoretical basis and data support for the research of microgrids.

Network reconfiguration and integration of distributed energy resources in distribution network by novel optimization techniques

Nowadays, electrical load demand in radial distribution networks (RDN) is continuously growing, therefore RDNs are facing some serious challenges like voltage violation and line losses. Since modern RDNs have reconfiguration capabilities, optimal network reconfiguration is preferred over the costly construction of new lines and cables. In addition, the optimal integration of distributed energy resources (DER) helps to decrease above mentioned network challenges. This paper presents an improved version of the radiality maintenance algorithm (IRMA) to solve the optimal reconfiguration problem using a meta-heuristics algorithm. It also proposes two different schemes of algorithms by the blending of a Genetic algorithm (GA) and Teaching learning-based optimization (TLBO) to solve the optimal DER integration problem, where blending enhances the search space of each scheme which helps to find global minima in less iterations and time, while existing algorithms get stuck in local minima with same number of searching agents. The proposed problems are solved by minimizing active power loss by the single objective function and the sum of active power loss, reactive power loss, and voltage deviation index by multi-objective function using the weighted sum method where all objectives are equally dealt with, and their sum is used as single fitness function for the optimization algorithm. Four case studies are simulated using different DER technologies to improve each objective function. The efficiency of the proposed IRMA and two newly developed schemes of optimization algorithms, named GA-TLBO and TLBO-GA, are tested on two IEEE benchmark RDN of 33 and 69 buses. The results validate the efficiency of proposed algorithms that remarkably minimize a single objective from 210.9800 kW in the base case to 58.8768 kW, whereas existing algorithms like GWO and HHO were able to only reduce it to 72.7861 kW and 72.900 kW for IEEE 33 bus RDN, respectively. Similarly, for IEEE 69 bus system, single objective is reduced from base case of 224.9917 kW to 36.2543 kW, while existing algorithms like QOTLBO and QOSIMBO were only able to reduce it to 71.6250 kW and 71 kW, respectively. Furthermore, in multi-objective functions, reactive power losses and voltage deviation are also significantly improved from their base values. After that, the results of improved algorithms are compared with those of existing algorithms in the literature review for a comprehensive evaluation, which proves that proposed algorithm schemes are much more efficient and stable for the proposed problem as well as for standard benchmark optimization functions.

Smart grid management: Integrating hybrid intelligent algorithms for microgrid energy optimization

The increasing global demand for power drives the need for advancements in generation, control, and supply technologies. While bulk power generation remains cost-effective, it depends on transmission and distribution systems for delivery. Integrating distributed generators into the grid enhances reliability and direct power supply to consumers. Micro Grids (MGs) are a promising solution, offering smoother and more reliable operations. This study explores MGs, incorporating the latest loads and distributed generators to minimize operating costs for electrical power providers. This work utilizes intelligent algorithms, including the Firefly algorithm, Spider Monkey Optimization (SMO), and a hybrid approach combining both. The Firefly algorithm, inspired by swarm intelligence, mimics fireflies' light emission to optimize solutions. SMO, based on spider monkeys' social behavior, balances exploration and exploitation to achieve optimal results. A novel hybrid algorithm is developed to minimize MG operating costs by combining SMO's exploration with the Firefly algorithm's neighborhood optimization; enhancing navigation through the complex constraint solution space of MGs. Case studies on a modified IEEE 33-bus test feeder are conducted. The study compares three metaheuristic methods with non-convex solution spaces to MG operational algorithms. The results demonstrate the hybrid method's efficacy in determining the optimal location and size of real and reactive power support devices for MG operation. Numerical results show the hybrid algorithm's superiority over existing methods, reducing operating costs by 7.5 %, real power loss by 36.5 %, and reactive power loss by 1.7 %, and voltage deviation by 16.895. By considering MG constraints, the hybrid algorithm consistently delivers better outcomes, optimizing operating costs and ensuring efficient power supply. This study presents a novel MG optimization approach, enhancing future energy systems with improved solutions.

A 28-nm 9T SRAM-based CIM macro with capacitance weighting module and redundant array-assisted ADC

In the emerging field of Computing-in-Memory (CIM), this study introduces a 28-nm CMOS-based Static Random Access Memory (SRAM) CIM macro capable of various computational modes, potentially offering a solution to the Von Neumann bottleneck. Beyond traditional SRAM read and write operations, to enhance the flexibility of the CIM macro, a 9T cell is proposed for performing AND, OR, and XNOR operations; a new capacitive weighting module is introduced to reduce the area of conventional ladder capacitors; and a redundant array-assisted Analog-to-Digital Converter (ADC) is proposed to improve linearity during ADC quantization. The proposed architecture can achieve multi-bit multiplication and accumulation (MAC), OR accumulation (ORA), and XNOR accumulation (XNORA). Simulated using a 28-nm CMOS process, the architecture demonstrated a minor standard deviation in BL voltage of 16.27 mV at the SS process corner, as evidenced by Monte Carlo simulation. At the TT process corner, the energy expenditure for MAC, XNORA, and ORA operations was 5.76, 5.85, and 5.82 fJ/op, respectively.

An improved meta-heuristic method for optimal optimization of electric parking lots in distribution network

In this study, a stochastic multi-objective structure for optimization of the intelligent electric parking lots (EPLs) is implemented in the distribution network for minimizing the power losses annual costs, power purchased from the main grid, unsupplied energy of subscribers, cost of vehicles to the grid as well as minimizing the network voltage deviations considering battery degradation cost (BDC) and network load uncertainty (NLUn). In this research, the unscented transformation method (UTM) is used for NLUn modeling and this method is easily applicable and has a low computational cost. An improved meta-heuristic algorithm named improved fire hawks optimization (IFHO) is utilized for decision variables finding defined as the site and size of the EPLs in the distribution network. The conventional fire hawks optimization (FHO) algorithm is inspired by the fire hawks foraging behavior and in this research, the Taylor-based neighborhood technique (TBNT) is used to reduce the dependency and the possibility of becoming trapped in local optimal. To evaluate the proposed methodology, the simulations are implemented in three scenarios (1) EPLs optimization without BDC and NLUn based-UTM, (2) EPLs optimization with BDC and without NLUn, and (3) EPLs optimization with BDC and NLUn. The results of the third scenario considering BDC and NLUn showed that the EPLs optimization integrated with a multi-objective framework by finding the EPL's optimal size and capacity in the network via the IFHO has reduced the annual losses, voltage deviations, ENS cost, and substation cost by 21.06%, 12.15%, 70.82%, and 39.10%, respectively compared to the base distribution network. Additionally, the results demonstrated that incorporating the BDC and NLUn, the annual losses, voltage oscillations, ENS cost, grid cost, and EPLs have increased in comparison with the EPLs optimization without BDC and NLUn based-UTM. In addition, the TBNT based-IFHO superiority has been confirmed in different scenarios by achieving better values of the objectives and also obtaining the convergence process with lower convergence tolerance and higher convergence accuracy.

Research on reactive power voltage control strategy of distribution network based on information sharing technology

Abstract In view of the fact that the traditional voltage control of the distribution network is suitable for local voltage control and affects the economy of the operation of the distribution network, a reactive power voltage control strategy of the distribution network based on information-sharing technology is proposed. First, the overall architecture of voltage control in the distribution network based on information-sharing technology is analyzed, and secondly, an algorithm based on the Jacobian sensitivity matrix is established with the minimum voltage RMS as the control goal. The model is solved, and finally, the effectiveness of the proposed voltage control strategy is verified by combining actual examples. The results show that the proposed voltage control strategy has a better voltage regulation effect and effectively reduces the overall voltage deviation of the distribution network.

Real-time techno-economical operation of preserving microgrids via optimal NLMPC considering uncertainties

In the modern era, managing optimal real-time control of microgrids during the operation phase has been a significant challenge, requiring careful consideration of both technical and economic factors. This paper introduces a framework for the real-time control of islanded microgrids using a preserving network. This structure incorporates various distributed generation sources, including rotating and non-rotating resources, along with energy storage systems. The optimization function within model predictive control (MPC) manages essential network parameters, such as frequency and voltage, while addressing real-time economic and technical objectives. To enhance precision and account for uncertainties in generation and consumption parameters, the integration of continuous power flow and the preserving network model is employed. This approach aims to create a model that closely mirrors real-world conditions, ensuring a more accurate representation of microgrid dynamics. The proposed structure demonstrates significant improvements in both technical and economic performance compared to Standard MPC and Adaptive MPC, highlighting its potential for more efficient islanded microgrid management. The proposed framework achieves notable reductions in total voltage deviation of 85.87% and 87.62% compared to Standard MPC and Adaptive MPC, respectively. Additionally, it delivers impressive enhancements in frequency deviation of 99.46% and 96.62% compared to Standard MPC and Adaptive MPC, respectively. Economically, the proposed framework significantly outperforms both, reducing costs by 39.29% compared to Standard MPC and by 28.12% compared to Adaptive MPC.

Transformasi Proteksi Tegangan: Sistem Monitoring IoT untuk Pemantauan Real-Time

Electric energy is a vital resource extensively used in various sectors such as industry, offices, agriculture, and trade. Its significance in supporting a nation's economy is undeniable, making the monitoring of power consumption and quality crucial. This study focuses on designing and implementing an Internet of Things (IoT)-based monitoring system for overvoltage protection in an electrical power system. The power system incorporates an Over Voltage Relay (OVR) as a protective measure against voltage fluctuations that may harm the equipment. The OVR operates based on electromagnetic principles, promptly responding to deviations in voltage beyond specified limits. Additionally, the system integrates the PZEM-004T module for measuring voltage, current, power, frequency, energy, and power factor, providing comprehensive insights into the electrical system's performance. The heart of the IoT-based monitoring system is the ESP32-32D module, facilitating real-time monitoring and remote control through the Blynk application. The implemented system successfully detects overvoltage conditions, automatically triggers protective measures, and allows users to monitor the power system through an intuitive interface.The study concludes with an evaluation of the average voltage measured by the OVR, emphasizing the system's reliability and potential for further industrial applications. The average OVR voltage, determined to be approximately 239 Volts, provides a comprehensive overview of measurement consistency throughout the experiments. This research contributes to the enhancement of electrical power management, ensuring the reliability and efficiency of overvoltage protection through IoT-based monitoring systems. Further evaluations and advancements could optimize the system's performance for broader industrial applications.

Optimal Reactive Power Dispatch Using Artificial Gorilla Troops Optimizer Considering Voltage Stability

The power system has been expanded to supply and fulfil the consumers’ requirements for reliability, affordability, and power quality. Power loss reduction and voltage stability enhancement are important points and have been considered interesting subjects for researchers and utilities. Furthermore, reactive power plays an important role in power system stability, security, and voltage improvement, and it is known as reactive power dispatch (RPD). In this paper, a newly developed meta-heuristic optimization technique that inspired the gorilla troop’s social intelligence in nature is applied. It is named Artificial Gorilla Troop Optimization (GTO). In addition, GTO is utilized to solve the optimal reactive power dispatch (ORPD) problem, whose real active power and voltage deviation reduction are the objective functions of this study. Generator voltage, transformer tap-changers, and reactive power compensators are the controlled variables that are optimized for achieving the minimum real power loss and bus voltage deviation. To illustrate the efficiency and performance of the proposed algorithm, IEEE 14-bus and 30-bus systems are employed. Moreover, the obtained results are compared with those obtained with other three already existing optimization algorithms, including the genetic algorithm (GA), particle swarm optimization (PSO), and whale optimization algorithm (WOA). Obviously, the proposed approach can prove the optimal values of controlled variables in solving the ORPD problem by giving the minimum real power loss and voltage deviation than those from compared techniques with less computation time.

Analytical distributed PV inverter reactive power support strategy for reduction of grid losses and voltage deviations

Analytical distributed PV inverter reactive power support strategy for reduction of grid losses and voltage deviations

Regenerative Braking Conscious Rule-Based Energy Management Strategy for Semi-Active Hybrid Energy Storage Systems for Electric Vehicles

Hybrid Energy Storage Systems (HESS) function as a solution to extend the lifespan of battery packs by maintaining a low charge/discharge rate. The integration of HESS in electric vehicles (EVs) is facilitated by supercapacitors (SC), known for their safe operation even under harsh conditions. The widely adopted rule-based power follower energy management strategy (EMS) is employed to control and restrict battery discharge within a defined range with SC support. Additionally, it charges the SC to accommodate regenerative energy, but it often neglects exploring the SC voltage set reference. This study investigates the SC voltage reference point and introduces a method to generate the reference point by equating the SC energy with the available kinetic energy in the moving vehicle. The proposed method was simulated and compared against the benchmark method using a portion of the medium segment Worldwide Harmonized Light Vehicles Test Procedure (WLTP) driving cycle. The results demonstrate a more stable discharge power for the battery bank, accompanied by improvements in battery voltage stability. The root mean square (RMS) battery current values were found to be 43.68 A and 42.92 A for the benchmarked and proposed methods, respectively, indicating a slight improvement of about 1%. Furthermore, the proposed method reduces the voltage deviation from 2.73% to about 2.57%, showcasing an additional positive outcome. These findings suggest that dynamically adjusting the SC voltage based on kinetic energy can enhance battery life and stability in EVs, offering a promising direction for future research and development of EMS.

Probabilistic prediction-based multi-objective optimization approach for multi-energy virtual power plant

Virtual power plants (VPPs) are encountering multiple challenges due to market uncertainties and power network instability. In this paper, a novel probabilistic prediction-based multi-objective optimization framework for VPP is proposed to maximize operating profit while minimizing pollutant emissions and voltage deviations in the distribution network, which considers the uncertainties of wind power and electricity price. In this framework, the VPP that participates in the energy and ancillary service markets firstly aggregates the wind farms, the electric vehicle charging stations (EVCS), and the combined cooling, heating, and power subsystems to improve the utilization efficiency and operational flexibility of multiple energy sources. Then, a new Pareto optimizer, called multi-objective hybrid sand cat swarm optimization and strength firefly algorithm, is proposed to tackle the multi-objective optimization model of VPP. The proposed hybrid algorithm utilizes the advantages of sand cat swarm optimization and strength firefly algorithm mechanisms to facilitate local exploitation and global exploration. Finally, a new deep reinforcement learning probabilistic prediction approach based on quantile regression deep deterministic policy gradient is modeled to evaluate the uncertainties. The proposed models and methods have been thoroughly discussed on a modified distributed network. It is calculated that compared with the VPP without EVCS, the operating profit of the proposed VPP increases by 18.69%, and the emissions and voltage deviation of the proposed VPP are reduced by 3.42% and 10.44%, respectively. Experimental results also prove that the performance of the proposed Pareto optimizer and probabilistic prediction approach is superior to other benchmark techniques.

Decentralized optimal voltage control for wind farm with deep learning-based data-driven modeling

The random fluctuations of wind energy and external grid voltage disturbances can both lead to serious voltage fluctuations and voltage deviations in the wind farm (WF). Voltage/reactive power control is an effective way to improve the voltage stability of WF. The existing research is based on complex physical models with prior parameter information, and the accuracy and calculation speed of the WF model are difficult to guarantee. To address this issue, this paper explores a decentralized optimal voltage control method for WF with deep learning-based (DL) data-driven modeling (DL-DOVC). A hybrid convolutional neural network (CNN)-Transformer architecture is proposed to establish the data-driven model of WF, leveraging its enhanced capabilities for extracting time series correlation, to learn complex patterns and dynamics from time series data. Then, we develop a fully decentralized voltage control method for WF to regulate the terminal bus voltage of wind turbines (WTs) within a feasible range. A DL-based data-driven predictive controller is specially designed to solve the established data-driven voltage optimization problem, it eliminates the necessity for frequent manual model maintenance and is suitable for real-time application. Additionally, the difficulty of WF decentralized optimal voltage control arises from the inability of local controllers to fully consider the impact of all non-local WTs on the local WT states. By designing the DL-based auxiliary state-feedback controller, the effects of non-local WTs are implicitly considered in the auxiliary feedback control law. A WF with 32*6.25 MW WTs is used to test the proposed DL-DOVC method. Simulation results show that the proposed DL-based model can efficiently learn and predict the WF dynamics under different operating scenarios. The near-global optimal voltage control performance is achieved only with local measurements by employing the DL-DOVC method.

An improved CB-DPWM strategy with NP voltage balance and switching loss reduction for 3-L NPC converter

Abstract The three-level (3-L) neutral-point-clamped (NPC) converter has found widespread applications in various fields, due to its simple structure, low failure rate and high efficiency. However, the 3-L NPC converter suffers the issue of unbalanced neutral-point (NP) voltage due to the divided two dc-side capacitors. The large NP voltage ripple and offset Based on the detailed analysis of the effects of vectors and clamping patterns on the NP voltage, a discontinuous optimized modulation method for NPC grid-connected inverter with both low switching loss and ability to balance NP voltage is presented. By measuring NP voltage and utilizing the property that superfluous clamping patterns have opposing effects on the NP voltage, the modulation method adjusts the NP voltage with the injection of various zero sequence voltages (ZSV) into the original sinusoidal waves. The results of this study demonstrate that the CB-DPWM can control the NP voltage balanced and decrease switching loss compared to conventional DPWM and SVPWM strategies. Moreover, the CB-DPWM strategy does not need to identify sectors and subsectors or calculate dwelling times. This modulation strategy effectively suppresses low-frequency fluctuations and deviations of the NP voltage with the injection of a simple ZSV, and improves the power quality of the output voltages and currents. Compared to the traditional SVPWM scheme, the suggested CB-DPWM has a 27 % lower overall IGBT loss. Moreover, this method is implemented through carrier waves, which is simpler than modulation schemes based on space vector, greatly shortens the program running time, and is conducive to engineering implementation.

A hybrid deep learning approach to solve optimal power flow problem in hybrid renewable energy systems

The reliable operation of power systems while integrating renewable energy systems depends on Optimal Power Flow (OPF). Power systems meet the operational demands by efficiently managing the OPF. Identifying the optimal solution for the OPF problem is essential to ensure voltage stability, and minimize power loss and fuel cost when the power system is integrated with renewable energy resources. The traditional procedure to find the optimal solution utilizes nature-inspired metaheuristic optimization algorithms which exhibit performance drop in terms of high convergence rate and local optimal solution while handling uncertainties and nonlinearities in Hybrid Renewable Energy Systems (HRES). Thus, a novel hybrid model is presented in this research work using Deep Reinforcement Learning (DRL) with Quantum Inspired Genetic Algorithm (DRL-QIGA). The DRL in the proposed model effectively combines the proximal policy network to optimize power generation in real-time. The ability to learn and adapt to the changes in a real-time environment makes DRL to be suitable for the proposed model. Meanwhile, the QIGA enhances the global search process through the quantum computing principle, and this improves the exploitation and exploration features while searching for optimal solutions for the OPF problem. The proposed model experimental evaluation utilizes a modified IEEE 30-bus system to validate the performance. Comparative analysis demonstrates the proposed model’s better performance in terms of reduced fuel cost of $620.45, minimized power loss of 1.85 MW, and voltage deviation of 0.065 compared with traditional optimization algorithms.

Electric eel foraging optimization algorithm for distribution network reconfiguration with distributed generation for power system performance enhancement considerations different load models

Optimizing the placement of distributed generators (DGs) alongside network reconfiguration is crucial for enhancing the efficiency and reliability of modern distribution systems amidst growing energy demands. This paper introduces a novel Electric Eel Foraging Optimization (EEFO) algorithm for the optimal reconfiguration and placement of DGs in radial distribution networks (RDNs), considering different load models. EEFO utilizes an adaptive energy factor to balance exploration and exploitation across four search strategies, such as exploration, resting, hunting, and migrating. Additionally, its free-parameter feature eliminates the need for fixed settings, simplifying the optimization process. The effectiveness of EEFO is demonstrated through its application to the 33-node and practical 84-node RDNs under different DG power factor scenarios with and without network reconfiguration. The primary objective is to minimize active and reactive power losses while reducing voltage deviation. The results indicate that the scenario with DG allocation at optimal power factor and network reconfiguration achieves significant reductions in voltage deviation, active power loss, and reactive power loss, with improvements of up to 92.20 %, 94.03 %, and 92.33 % in the 33-node network and 55.95 %, 60.20 %, and 59.36 % in the 84-node network, respectively. Furthermore, the comparative analysis with the zebra optimizer, whale optimizer, and moth flame optimizer highlights EEFO's superior efficiency and effectiveness in optimal DG allocation and distribution network reconfiguration.

Optimal planning of electric-heating integrated energy system in low-carbon park with energy storage system

Electric-heating integrated energy system (EH-IES) is pivotal for advancing energy structure reforms, and proper planning of EH-IES components can markedly enhance the operation economy, environmental sustainability, and system stability. Nonetheless, the inherent randomness and intermittency of renewable energy sources, along with the peak and valley characteristics of the load, cause output fluctuations in EH-IES energy supply equipment, posing significant threats to system stability. To address these challenges, a multi-objective bi-layer EH-IES planning model considering energy storage system is established, aiming at optimizing both economic performance and stability. This model employs non-dominated sorting genetic algorithm II (NSGA II) to optimally plans the capacity and location of EH-IES's equipment under 13-node district heating network (DHN), IEEE-33, and IEEE-69 node test systems. Simulation results show that DHN's thermal customer satisfaction is improved by 77.19 % (7.076 °C), with the total cost is $234,310.09/day. For the distributed network (DN), the net load fluctuation is reduced by 1.8491 MW (23.38 %) and 2.6083 MW (26.16 %), and the voltage deviation is reduced by 0.3479 p.u. (44.23 %) and 1.7349 p.u. (61.89 %), with respective daily costs of $403.17 and $898.36. Consequently, the proposed method can improve the cost efficiency and sustainability of the system operation.