Increasing Load Demand Research Articles

Application of Sooty Tern Optimization Algorithm on Solar‐Wind‐Battery‐Thermal Integrated Dynamic Economic Emission Dispatch Problems

ABSTRACTThe current challenges faced by conventional power plants, including increasing load demand over time, high generation costs, and excessive emissions from fossil fuels, have been helped to overcome by the integration of renewable energy sources (RESs) alongside conventional thermal power units. In this article, traditional dynamic economic emission dispatch (DEED) is enhanced by incorporating RESs, including two solar units, two wind units, and one battery power unit, forming the solar‐wind‐battery‐thermal (SWBT) integrated DEED (SWBTDEED). This integration aimed at reducing generation costs and minimizing excessive fuel emissions, and combining cost and emissions over a 24 h period. Four different test systems, each incorporating 6, 10, 30, and 40 thermal units alongside the same RESs, are considered to meet varying load demands hourly throughout the day. This article demonstrates that, in addition to reducing generation costs, SWBTDEED is capable of reducing emissions by 38.28%, 28.48%, 20.67%, and 20.44% for four test systems, thereby protecting the environment. The sooty tern optimization algorithm (STOA) is proposed in this article for solving complex DEED and SWBTDEED problems, considering various constraints such as generation limits, ramp rate limits, valve point loading, and Weibull distribution. Finally, the robustness, optimization efficiency, and capability of the STOA technique in handling complex nonlinear constraints are demonstrated in the results section, showcasing its ability to achieve optimal results compared to other algorithms such as the sine‐cosine algorithm, backtracking search algorithm, differential evolution, and particle swarm optimization.

Optimal sizing and placement of STATCOM, TCSC and UPFC using a novel hybrid genetic algorithm-improved particle swarm optimization

The increase in global power demand has caused most of today's power networks to become overloaded especially in Sub-Saharan Africa. The increased load demand can be met through expansion of existing generation and transmission system. However, construction of new power infrastructure is limited by financing and technical constraints. Thus, power networks have been left to operate at overload conditions with high power losses and many power quality (PQ) problems. Flexible AC Transmission System (FACTS) devices can improve the power transfer capability of the existing transmission networks without the need of constructing new power infrastructure. In this paper, a multi-objective function comprising of minimization of power loss (PL), voltage deviation (VD) and operational cost (OC) was formulated and solved using a novel algorithm. A novel Genetic Algorithm-Improved Particle Swarm Optimization (GA-IPSO) technique is proposed in this paper for optimization of size and location of FACTS devices. Static Synchronous Compensator (STATCOM), Thyristor Controlled Series Capacitor (TCSC) and Unified Power Flow Controller (UPFC) are the three FACTS devices considered. The proposed technique was validated using IEEE-33 Bus Test System, which is a popular benchmark Radial Distribution System (RDS). The three FACTS devices were optimized separately and also in a combined manner. Under the separate optimization, the size and location of individual FACTS devices were optimized. For combined optimization, the sizes and locations of more than one device were optimized in the same test system. For separate optimization, UPFC produced the best results by reducing the active power losses by 38.44 % and OC from $1.59×105 to $ 1.15×105. Under the combined optimization, combination of TCSC, STATCOM and UPFC gave better results by achieving active power loss reduction of 56.09 % and reducing OC from $1.59×105 to $ 1.03×105. Comparison of GA-IPSO technique with other algorithms such as Particle Swarm Optimization (PSO), Genetic Algorithm (GA), Improved Grey Wolf Optimization (IGWO) and Differential Evolution Algorithm (DEA) showed that the proposed hybrid technique was superior and more efficient in solving the FACTS optimization problem.

Investigation of fiber Bragg grating sensor measurability in concrete beams under static load conditions

Recent advances in construction materials and architecture have enhanced the safety of modern structures, which now feature higher, lighter, and more unique designs. Bridges, as critical infrastructure, face increasing load demands due to rising traffic volumes, leading to accelerated structural deterioration. This necessitates more frequent inspections and maintenance, resulting in increased costs and potential operational disruptions. Real-time built-in monitoring systems are thus essential for both new and aging structures. Fiber Optic Sensor (FOS) technology, particularly Fiber Bragg Grating (FBG) sensors, offers a reliable and stable solution for long-term Structural Health Monitoring (SHM). This study evaluates the performance and applicability of FBG sensors (FBGs) in comparison to traditional strain gauges (SGs) for capturing strains, loads, deflections, and temperature in full-scale beams under static loads. Four large-scale reinforced concrete (RC) and CFRP-strengthened beams were tested. Nonlinear finite element analysis (NLFEA) using VecTor2 was employed to simulate the behavior of these beams under static loading. Data from FBGs were compared to those from collocated electrical SGs. The results demonstrated the reliability and flexibility of FBGs in monitoring structural performance. Notably, FBGs continued to provide accurate readings even after the specimens reached their maximum load, showcasing their endurance and resilience compared to conventional SGs.

An integrated binary metaheuristic approach in dynamic unit commitment and economic emission dispatch for hybrid energy systems

The current generation portfolio is obligated to incorporate zero-emissions energy sources, predominantly wind and solar, due to the depletion of fossil fuels and the alarming rate of global warming. In the current scenario, power engineers must devise a compromised solution that not only advocates for the adoption of renewable energy sources (RES) but also efficiently schedules all conventional power generation units to balance the increasing load demand while simultaneously minimizing fuel costs and harmful emissions that are currently addressed by Unit Commitment (UC) and Combined Economic Emission Dispatch (CEED) problem solutions. However, the integration of renewable energy resources (RES) further complicates the UC-CEED problem due to their intermittent nature. Recently, metaheuristic algorithms are acquiring momentum in resolving constrained UC-CEED problems due to their improved global solution ability, adaptability, and derivative-free construction. In this research, a computationally efficient binary hybrid version of crow search algorithm and improvised grey wolf optimization is proposed, namely Crow Search Improved Binary Grey Wolf Optimization Algorithm (CS-BIGWO) by inclusion of nonlinear control parameter, weight-based position updating, and mutation approach. Statistical results on standard mathematical functions prove the supremacy of the proposed algorithm over conventional algorithms. Further, a novel optimization strategy is devised by integrating enhanced lambda iteration with the CS-BIGWO algorithm (CS-BIGWO-λ\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\lambda$$\\end{document}) to solve a day-ahead UC-CEED problem of the hybrid energy system incorporating cost functions of RES. For the model, a day-ahead forecast of wind power and solar photovoltaic power is obtained by using the Levy-Flight Chaotic Whale Optimization Algorithm optimized Extreme Learning Machines(LCWOA-ELM). The proposed algorithm is tested for the UC-CEED solution of an IEEE-39 bus system with two distinct cases: (1) without RES integration and (2) with RES integration. Several independent trial runs are executed, and the performance of the algorithms is assessed based on optimal UC schedules, fuel cost, emission quantization, convergence curve, and computational time. For case 1, the proposed algorithm resulted in a percentage reduction of 0.1021% in fuel cost and 0.7995% in emission. In contrast, for test case 2, it resulted in a percentage reduction of 0.12896% in fuel cost and 0.772% in emission with the proposed algorithm. The results validate the dominance of the proposed methodology over existing methods in terms of lower fuel costs and emissions.

Investigation of DG units influence on 66 kV sub-transmission system network considering region load growth: a case study

Abstract Voltage breakdown in sub-transmission networks resulting from an increase in load demand can be addressed by incorporating distributed generation (DG) units such as hydro, wind, photovoltaic, and geothermal in some buses. This approach can lower power losses and the price of the primary generated energy while increasing network dependability and efficiency. Feasibility of dispersed generation integration & its effect on sub-transmission system operation were investigated using Ben Walied 66 kV sub-transmission network in Libya as a state. This study presents modified particle swarm optimization (MOPSO) technique to select the best option with dispersed generation units under various operating conditions. The Ben Walied 66 kV sub-transmission network has been subject of effects research on both normal operation and load growth scenarios. The goal of this study was to find best way to adjust penetration level of the three DGs in response to changes in the network loading. Three DG units have been refitted to study the effects on test networks for sub-transmission, which consist of 30 and 51 buses. A comparison analysis shows that while the ideal locations of DG units remain constant with load expansion, the optimal solution for the penetration level percentage of three DG units was boosted by new optimal sizes. Research has been done on how the power factor of distributed generators (DGs) affects the practical performance of 66 kV networks in steady-state conditions. The integration of DG units in regulating these losses and voltage variations is demonstrated by the results, which indicate that the ideal solution for DG unit’s power factor was at a value of 0.87 lagging.

Integration of Distributed Generations and electric vehicles in the distribution system

Power quality challenges are a major problem these days because of the significant increase in load demand and the existence of various reasons for disruptions in the distribution system. A voltage sag or a decrease in the magnitude of the line voltage can be brought on by rapid fluctuations in load or distribution system issues. Enhancing the grid current profile, reducing grid voltage sag, and improving other performances can all be achieved through the combination of electric vehicles with distribution generation systems. In this paper, the integration of Distributed Generation with Electric Vehicles in the distribution system has been done to enhance system performances such as true power loss index, imaginary power loss index, voltage deviation index, short circuit current index, cost function, and environmental impact reduction index for 16 bus distribution system in constant impedance, constant current, and constant power load models, or ZIP load models through proper location, sizing, coordinated controlling, and types of Distributed Generation and Electric Vehicle pairs for both charging and discharging modes by adopting Hybrid Genetic Algorithm - Monte Carlo Simulation optimization methodologies. This study's primary goal is to determine the optimal distribution generation and electric vehicle pairing to improve the aforementioned parameters, which will assist researchers in their future planning. Researchers, industry professionals, scientists, and individuals working with Distributed Generation and Electric vehicles in the smart grid will find this statement to be of great use.

Analysis of charging tariffs for residential electric vehicle users based on Stackelberg game

Establishing a rational pricing mechanism for electric vehicles (EVs) can incentivize users to engage in charging activities, foster positive consumption behaviors, and indirectly benefit the grid by increasing load demand. Therefore, this paper conducts a Stackelberg game study between the grid and EV users by utilizing real charging and discharging data of EV users in F city. First, the price elasticity of EV users' electricity consumption behavior is analyzed. Second, the Stackelberg game model is constructed, multiple tariff schemes are designed to calculate the equilibrium tariff of the game, and the benefits of the grid and EV users are analyzed in comparison with the tariff policy that will be implemented in F city. Lastly, different seasonal scenarios are constructed to extend the applicability of the research results. It is found that Scheme 3 effectively incentivizes charging load growth, increases cross-subsidies and transmission and distribution fees, and protects the level of charging and discharging costs for residents; furthermore, lowering charging and discharging tariffs during seasons of high price elasticity for residents to increase demand is an optimal strategy for grid pricing.

Efficient reduction of power losses by allocating various DG types using the ZOA algorithm

The continuous challenge of ensuring energy supply is a significant concern. Traditionally, utilities have addressed increasing load demands by building new central power plants or expanding existing ones. However, these methods are time-consuming and costly, providing only short-term solutions. Additionally, power plants are often far from load centers, resulting in higher power losses during long-distance transmission. The integration of distributed generation (DG), characterized by smaller scale and lower capital costs, with nearby consumers can effectively address these challenges. Due to the significant influence of DG penetration and their specific locations on the distribution system's performance, this study offers a novel Zebra Optimization Algorithm (ZOA). The suggested ZOA is used to optimize the capacity and position of various DG units connected to a radial distribution system to reduce power losses and study the stability of the system after DG allocation using the Voltage Stability Index (VSI). The evaluation is conducted using the IEEE 33-bus test system, considering various scenarios including single and multiple DGs of different types. The results demonstrate that in the best scenario, the ZOA can reduce losses by up to 94.25 % and improve the VSI from 0.69643 to 0.9605. These findings highlight the superior performance of the ZOA over other techniques in optimizing DG placement and sizing for power system performance.

Validation and test of a novel multi-input converter in extreme conditions using PSOPIC in hybrid power generation environment

The need for renewable energy resources increases due to the day-by-day increase in load demand. Still, there is a need for new technology to operate the existing power system optimally. Distributed generation (DGENs) are helpful in meeting the demand of power due to its lower cost compared to the construction of the complete power system. But this DGEN is constructed using renewable resources, which are intermittent in nature. So, hybrid power generation, which uses multiple sources, is used to satisfy the power need. In this paper, validation, and test of a new multi-input single ended primary inductor converter (MI-SEPIC) is proposed. The performance of the MI-SEPIC converter is tested by connecting photovoltaic (PV), wind, and fuel cells. The proposed system is connected to the grid, and the power transients are analyzed. The DQ control of grid synchronization is discussed in this paper. The conventional PI controller is replaced with a hybrid particle swarm optimization tuned proportional integral control (PSOPIC), and the results are compared. Verification is done using MATLAB software. Validation and test with different test cases to prove the sturdiness of the complete system is explained.

Impact of demand growth on the capacity of long-duration energy storage under deep decarbonization

Abstract The weather-dependent uncertainty of wind and solar power generation presents a challenge to the balancing of power generation and demand in highly renewable electricity systems. Battery energy storage can provide flexibility to firm up the variability of renewables and to respond to the increased load demand under decarbonization scenarios. This paper explores how the battery energy storage capacity requirement for compressed-air energy storage (CAES) will grow as the load demand increases. Here we used an idealized lowest-cost optimization model to study the response of highly renewable electricity systems to the increasing load demand of California under deep decarbonization. Results show that providing bulk CAES to the zero-emission power system offers substantial benefits, but it cannot fully compensate for the 100% variability of highly renewable power systems. The capacity requirement of CAES increases by ≤33.3% with a 1.5 times increase in the load demand and by ≤50% with a two-times increase in the load demand. In this analysis, a zero-emission electricity system operating at current costs becomes more cost-effective when there is firm power generation. The least competitive nuclear option plays this role and reduces system costs by 16.4%, curtails the annual main node by 36.8%, and decreases the CAES capacity requirements by ≤80.7% in the case of a double-load demand. While CAES has potential in addressing renewable variability, its widespread deployment is constrained by geographical, societal, and economic factors. Therefore, if California is aiming for an energy system that is reliant on wind and solar power, then an additional dispatchable power source other than CAES or similar load flexibility is necessary. To fully harness the benefits of bulk CAES, the development and implementation of cost-effective approaches are crucial in significantly reducing system costs.

Deep reinforcement learning-based optimal scheduling of integrated energy systems for electricity, heat, and hydrogen storage

The increasing load demands and the extensive usage of renewable energy in integrated energy systems pose a challenge to the most efficient scheduling of integrated energy systems (IES) because of the unpredictability and volatility of both the load side and renewable energy.Integrating heat storage and hydrogen storage technologies into integrated energy systems can effectively alleviate the phenomena of wind and solar power desertion caused by the instability of renewable energy sources. This study introduces a real-time optimal scheduling method based on the Soft Actor-Critic (SAC) algorithm, aimed at reducing system operating costs and enhancing the consumption rate of renewable energy. First, the established mathematical model is converted into a Markov Decision Process (MDP), the objective function is converted by designing the action state space and the reward function, and the deep reinforcement learning framework is established. Finally, the decision-making outcomes of intelligence in various energy storage scenarios of renewable energy consumption and extreme cases are analyzed and compared, and the results show that the heat storage and hydrogen storage system significantly improve the rate of renewable energy consumption and the economy of the system. This method is also shown to significantly improve the rate of renewable energy consumption.

Optimal Deployment of DGs, DSTATCOMs and EVCSs in Distribution System using Multi-Objective Artificial Hummingbird Optimization

Distribution systems have a lot of obstacles to deal with, like increasing load demands, environmental issues, operating limits, and infrastructure development limitations. On the other hand, the number of plug-in hybrid electric vehicles (PHEVs) has grown significantly in recent years and is likely to continue due to concerns over the environment and fossil fuel shortages. Due to the increasing use of PHEVs, distribution systems were not built to accept them, requiring planners to create parking lots that support PHEV charging. To address these issues, in this study, optimal planning of distributed generation (DG) and electric vehicle charging stations (EVCS) in radial distribution systems by the maiden application of a novel Pareto-based multi-objective artificial hummingbird optimization (MOAHO) algorithm is addressed. Three technical aspects of the distribution system are improved by optimal planning of various types of DGs and EVCSs: active power loss reduction, total voltage deviation minimization, and voltage stability improvement. The Pareto-based MOAHO is employed to generate the optimal front between the three competing objectives and later TOPSIS method is employed for selecting the most compromised solution from the optimal front. The proposed methodology is tested on IEEE-33, IEEE-69 bus radial distribution test systems. To validate the efficacy of the MOAHO algorithm, the simulation outcomes of the proposed methodology are generated using a multi-objective non-dominated sorting genetic algorithm (NSGA2), particle swarm optimization algorithm (PSO), grey wolf optimization algorithm (GWO) and compared with the outcomes of the MOAHO algorithm.

Study the impact of renewable and non-renewable energy sources on micro-grid using time series data based information transfer

This paper addresses the challenges arising from the integration of renewable energy sources into microgrids, focusing on the impact on system dynamics and stability. Employing an advanced time series-based information transfer approach, the study identifies critical parameters influencing system instability under small perturbations. Unlike traditional methods, this technique effectively analyzes information flow and interactions within the complex system, providing insights into the influence of states on different operating modes. The study specifically investigates the disturbance caused by increased load demand and employs a three-layered small signal stability index to pinpoint crucial system-affecting parameters. Subsequently, Distributed Generation (DG) units are strategically placed to restore stability, guided by the identified critical parameters. The proposed approach is validated on IEEE 3 Bus and IEEE 39 Bus systems, demonstrating its effectiveness and outperforming existing methodologies in predicting and addressing system uncertainties. These systems experienced instability as a result of an introduced disturbance in the form of increased load, affecting the dynamics of the generator at the immediate bus. To enhance the generator dynamics and address the impact of the disturbance, a new DG unit was introduced to provide support. The DG unit was placed at the bus which was deduced to be most critical from the three-layered small signal stability.

Solving the economic load dispatch integrating clean energies in power system using Black Kite Algorithm

This research applies a novel meta-heuristic algorithm called the Black Kite Algorithm (BKA) to solve the economic load dispatch integrating the clean energies (CE-ELD) with the main objective of minimizing the overall fuel cost (OFC) of all thermal power generating sources (TPGSs). A 20-TPGS power system is used to conduct the whole research and assess the actual performance of the BKA. While solving the considered problem, wind and solar generating sources are both integrated into the power system. Besides, power loss caused by the transmission process and different levels of load demand ranging from 2500, 2600, to 2700 MW are employed. The results achieved by BKA in three cases of load demand are compared with another meta-heuristic algorithm, the Coati optimization algorithm. The comparison between the results of the two methods indicates that BKA is completely superior to COZ in all criteria, especially in reaching the best values of OFC and stability throughout the test runs, regardless of the noticeable increase in load demand. Mainly, BKA always requires fewer iterations to reach the best OFC value, resulting in a lower fluctuation of OFC values among the test runs. Based on these results and evaluations, BKA is acknowledged to be the robust and stable search method, and we highly recommend using BKA to solve optimizations such as the CE-ELD problem.

Performance Analysis of Automatic Generation Control for a Multi-Area Interconnected System Using Genetic Algorithm and Particle Swarm Optimization Technique

The primary focus of this paper is to assess an interconnected power system using different optimization techniques. The main purpose is to employ different optimization techniques, including genetic algorithms (GA) and particle swarm optimization (PSO), to systematically enhance the performance of a multi-area or two-area automatic generation control (AGC) system, aiming to optimize the three PID controllers gain values and improve system performance under diverse loading conditions. Two case studies are conducted exploring different loading conditions in the megawatt (MW) range, including increasing load demand and decreasing load demand. The analysis involves four scenarios, covering without any kind of controller, another with solely a proportional integral derivative (PID) controller, a PID controller enhanced through a genetic algorithm (GA), and lastly, a PID controller improved through particle swarm optimization (PSO). The optimization process utilizes the integral time absolute error (ITAE) as the objective function to evaluate the system's performance. The simulation outcomes for ITAE, settling time, overshoot, and undershoot for frequency deviation of area one, area two, and power deviation in the tie-line are compared with previous similar studies to assess the novelty of this work. The article highlights the importance of the multi-area AGC system and the significance of different optimization techniques in improving its performance.

Optimal sizing of multi-energy microgrid with electric vehicle integration: Considering carbon emission and resilience load

To address the intermittency of renewable energy sources, global warming, and increasing load demands, this paper proposes the optimal sizing of a multi-energy microgrid (MEMG) consisting of electrical, thermal, cooling, and hydrogen networks. The system integrates multi-energy storage and EVs along with the resilience load to facilitate a robust operation scheme. The paper introduces an improved resilience backup mechanism for EVs using hybrid storage. Then, random samples for stochastic parameters are generated with Monte Carlo Simulations (MCS). To this end, a mixed-integer linear programming-based model is developed to minimize cost, emissions, and load shed. Then, a CPLEX solver is applied to solve it efficiently. The optimal MEMG with the hydrogen network reduces cost by 4% and emissions by 40%. The case study validates that hybrid storage (BES-HST-TST) can effectively reduce the electricity grid purchase to zero, making MEMG self-sufficient while yielding the least annual system cost (ASC) of 3855562$ and decreasing emissions to 8385 kg, resulting in economic savings, environmental sustainability, and increased utilization of renewables. Notably, V2G can save 0.7546% of MEMG extra cost incurred due to EVs integration and significantly reduces CO2 emissions by 4%. A novel finding is that the proposed hybrid storage backup mechanism can effectively minimize the extra cost of keeping the resilience load by retaining 31.46% of backup in hydrogen form. It mitigates risk associated with power outages while achieving the least ASC of 3858933$ and CO2 emissions of 8043 kg. These results can help realize a green, cost-effective, and efficient energy system.

Optimal renewable-integrated economic load dispatch for a large-scale power system using One-to-One Optimization Algorithm

This study presents the application of a new meta-heuristic algorithm called One-to-One optimization algorithm (OOBO) for solving the renewable-integrated economic load dispatch problem (RI-ELD) with consideration of both wind and solar power plants. The whole study focuses on minimizing the overall expenses of fuel (OEF) for all thermal electric power plants (TEPPs). The considered power system consists of twenty TEPPs with different working limits. OOBO is applied to solve the given problem in three cases of load demand level, including 2500, 2600, and 2700 MW. The results achieved by OOBO in the three cases are compared with other meta-heuristic algorithms called Coati optimization algorithm (COA) in the four aspects, such as Best OEF (Bst.OEF), Average OEF (Aver.OEF), Maximum OEF (Max.OEF). OOBO not only outperforms COA in all comparison aspects but also provides faster convergence speed to the optimal values of OEF at all three cases of load demand. Moreover, OOBO shows its surprising stability over COA regardless of the increase of load demand in Case 2 and Case 3. By observing these results, OOBO deserved the highly effective search tool for solving the large-scale and highly complex RI-ELD problem.

Enhancement of ATC with FACTS Devices in Deregulated Power System Considering Various Contingency and Benefit Margins

The global electric power systems are under stressed condition because of increased per capita power consumption and ever-growing load demand. It is difficult to modify existing infrastructure of transmission systems to meet increasing load demand. The volume of electric power transmitted depends on the real and reactive power supply and the availability of margin in transmission system. Available Transfer Capability is an estimation of additional power transfer capability of the existing transmission system for a further market activity over and above the already committed power transactions. Factors such as weather, line and generator outages must be taken into account when computing the rigorous value of ATC. This paper focuses on enhancing the ATC limit of a system by employing STATCOM and Whale Optimization Algorithm for tuning system variables and to determine optimal size and location. An investigation has been carried out to understand the impacts of ATC by implementing line and generator outages in the test system. Additionally, an investigation has been done to realise the various benefit margins like ETC, TRM and CBM. The competence of the proposed methodology is validated by conducting different case studies on IEEE-30 bus test system using MATLAB for various bilateral and multilateral transactions.

Optimal Operation of Residential Battery Energy Storage Systems under COVID-19 Load Changes

Over the past few years as COVID-19 was declared a worldwide pandemic that resulted in load changes and an increase in residential loads, utilities have faced increasing challenges in maintaining load balance. Because out-of-home activities were limited, daily residential electricity consumption increased by about 12–30% with variable peak hours. In addition, battery energy storage systems (BESSs) became more affordable, and thus higher storage system adoption rates were witnessed. This variation created uncertainties for electric grid operators. The objective of this research is to study the optimal operation of residential battery storage systems to maximize utility benefits. This is accomplished by formulating an objective function to minimize distribution and generation losses, generation fuel prices, market fuel prices, generation at peak time, and battery operation cost and to maximize battery capacity. A mixed-integer linear programming (MILP) method has been developed and implemented for these purposes. A residential utility circuit has been selected for a case study. The circuit includes 315 buses and 100 battery energy storage systems without the connection of other distributed energy resources (DERs), e.g., photovoltaic and wind. Assuming that the batteries are charging overnight, the results show that energy costs can be reduced by 10% and losses can decrease by 17% by optimally operating batteries to support increased load demand.

A comprehensive resilience evaluation method for power distribution systems based on weighted Page-Rank algorithm

Resilience evaluation plays a critical role in the decarbonized development of modern power distribution systems since it objectively assesses how the distribution system performs in the face of stochastic renewable generation, increasing load demand, and promising to cut carbon emissions. This study proposed a comprehensive resilience evaluation method for regional distribution systems. The proposed resilience evaluation system comprises three sub-groups: performance, economy, and connections. To improve the objectivity of the proposed evaluation method, the weighting factors are calculated based on the Analytic Hierarchy Process (AHP) and weighted Page-ranking algorithm, respectively. Specifically, AHP is adopted to calculate the weighting factors for different metrics. At the same time, the original Page-ranking algorithm is improved according to the importance of the buses and then used to calculate the weighting factors for different buses within the same metric. The proposed method is applied to a real-world distribution network in China. Numerical results show that the proposed method can objectively evaluate the overall resilience level of the distribution network, including the source side, the network, and the load side. The evaluation results can help the decision-makers to make resilience enhancement in an economical and targeted way.