Energy Not Supplied Research Articles

Frekuensi Gangguan dan Energi Tak Tersalurkan Sebagai Indikator keandalan Sistem Jaringan Tegangan Menengah

The increasing need for electrical energy must be balanced with improved customer service. Customers will feel comfortable if they can enjoy an uninterrupted electricity supply in the form of a disconnection of the electricity supply for a certain period of time or what is commonly called the ENS (Energy Not Supplied) index and the AENS (Average Energy Not Supplied) index. The reliability of the electric power system is one of the things that is very important in order to maintain the performance of the three main pillars of PT. PLN (Persero). A reliable distribution network can be seen from the number of disruptions that often occur in the distribution network. Disruptions that arise in the distribution network arise due to internal factors such as less reliable distribution network components. In addition, it can also be caused by external factors such as natural disasters or the influence of trees and animals. Based on the results of the research that has been conducted, it is known that the occurrence of disruptions and the length of the duration of the disruption cause the Energy Not Supplied (ENS) value to increase as in April and July, the high duration of the blackout also makes the ENS higher, namely 792.857804 kW and 789.046811 kW. However, the distribution network at the Wagom feeder is still considered reliable in distributing electrical energy based on the SAIDI and SAIFI indices. From the research results, the SAIDI index at the Wagom feeder reached 8.96 hours/customer/year. While the SAIFI index at the Wagom feeder reached 2.08 times/customer/year.

Reliability and line loading enhancement of distribution systems using optimal integration of renewable energy and compressed air energy storages simultaneously under uncertainty

Recently, with the significant growth of load demand, further interest in renewable energy sources (RESs) incorporated into electric distribution systems (DSs) has become important. However, the output powers' stochastic fluctuations of the photovoltaic (PV) and wind turbine (WT) generation sources, as well as the load demand, result in obvious operational challenges related to the reliability and line loading of these systems. Therefore, this present study is intended to enhance the load-oriented reliability indices (LORIs), the customer-oriented reliability indices (CORIs), and the line loading index (LLI) of the DSs by assigning the optimal integration of RESs and compressed air energy storages (CAESs) through the determination of both the optimal placement and capacities of RESs and CAESs simultaneously, as well as the optimal charging and discharging powers of the CAESs and their initial state of charge based on the red-tailed hawk (RTH) optimization technique. The Monte Carlo Simulation (MCS) and the Scenario-based Reduction (SBR) uncertainty methods are implemented to address the uncertainties of RESs and loading. The influences of the linear failure rate of DS's feeders and the installation of protective devices are also demonstrated. The efficiency of the intended methodology is demonstrated on the IEEE 69-bus and IEEE 33-bus DSs associated with voltage-dependent, time-varying mixed loads. The outcomes obtained from the suggested RTH are compared to those of other reported optimization techniques to validate its effectiveness. The results reveal that the optimal integration of RESs along with CAESs in the IEEE 69-bus test DS can improve the energy not supplied (ENS), system average interruption duration index (SAIDI), system average interruption frequency index (SAIFI), average service unavailability index (ASUI), and LLI by 1.09 %, 1.86 %, 1.73 %, 1.89 %, and 64.53 %, respectively.

A new multi-objective-stochastic framework for reconfiguration and wind energy resource allocation in distribution network incorporating improved dandelion optimizer and uncertainty

Improving the reliability and power quality of unbalanced distribution networks is crucial for ensuring consistent and reliable electricity supply. In this research, multi-objective optimization of unbalanced distribution networks reconfiguration integrated with wind turbine allocation (MORWTA) is implemented considering uncertainties of networks load, and also wind power incorporating a stochastic framework. The multi-objective function is defined by the minimization of power loss, voltage sag (VS), total harmonic distortion (THD), voltage unbalance (VU), energy not-supplied (ENS), system average interruption frequency index (SAIFI), system average interruption duration index (SAIDI), and momentary average interruption frequency (MAIFI). A new improved dandelion optimizer (IDO) with adaptive inertia weight is recommended to counteract premature convergence to identify decision variables, including the optimal network configuration through opened switches and the best location and size of wind turbines in the networks. The stochastic problem is modeled using the 2m + 1 point estimate method (PEM) combined with K-means clustering, taking into account the mentioned uncertainties. The proposed stochastic methodology is implemented on three modified 33-bus, and unbalanced 25-, and 37-bus distribution networks. The results demonstrated that the MORWTA enhanced all study objectives in comparison to the base networks. The results also demonstrated that the IDO had superior capability to solve the deterministic- and stochastic-MORWTA in comparison to the conventional DO, grey wolf optimizer (GWO), particle swarm optimization (PSO), and arithmetic optimization algorithm (AOA) in terms of achieving greater objective value. Moreover, the results demonstrated that when the stochastic-MORWTA model is considered, the power loss, VS, THD, VU, ENS, SAIFI, SAIDI, and MAIFI are increased by 18.35%, 9.07%, 10.43%, 12.46%, 11.90%, 9.28%, 12.16% and 14.36%, respectively for 25-bus network, and also these objectives are increased by 12.21%, 10.64%, 12.37%, 9.82%, 14.30%, 12.65%, 12.63% and 13.89%, respectively for 37-bus network compared to the deterministic-MORWTA model, which is related to the defined uncertainty patterns.

Adaptive protection coordination in microgrid based on nature inspired meta-heuristic optimization algorithm

Ensuring a robust protection system is crucial for safeguarding the integrity of the overall system against abnormalities. Incorporating distributed generation (DG) into the distribution network can introduce fluctuations in fault current levels and directions, potentially causing mismatches in the response of the existing coordination system. This study proposes an adaptive protection coordination scheme designed to accommodate both grid-connected and standalone modes, addressing various fault scenarios. Utilizing a hybrid WCMFO algorithm, optimal relay settings are determined to facilitate effective coordination within a microgrid setup. The proposed method has been analyzed on 9 bus Canadian benchmark system integrated with four DGs. The performance of the proposed method is compared to other optimization techniques to demonstrate its effectiveness. System modelling is conducted using MATLAB/Simulink, and validation is further carried out using industrial ETAP software on a test microgrid system. The analysis extends to evaluating the enhancement in overall system reliability, quantified in terms of energy not supplied (ENS).

Optimizing virtual power plant allocation for enhanced resilience in smart microgrids under severe fault conditions using the hunting prey optimization algorithm

The rapid expansion of renewable energy sources (RESs), such as photovoltaic (PV), wind turbine (WT), micro turbine (MT), and fuel cell (FC), has presented both opportunities and challenges to power systems, particularly in terms of environmental sustainability and economic feasibility. While RESs offer benefits like reduced environmental pollution, decreased power losses, and enhanced power quality, their intermittent nature and uncertainties pose challenges, resulting in variable generation and instability in distribution systems. To address these challenges, the concept of aggregating distributed energy resources (DERs), battery energy storage system (BESS), electric vehicles (EVs), and controllable loads into a virtual power plant (VPP) managed by an energy management system (EMS) has emerged. This study aims to determine the optimal location and size of VPPs within radial distribution system (RDS) while considering network resilience to severe weather events. The problem is formulated as an optimization task with dual objectives: minimizing the operating cost of VPPs and reducing energy not supplied (ENS) during natural disasters such as floods and earthquakes. To address this optimization problem, a novel meta-heuristic optimization algorithm called the hunting prey optimization algorithm (HPOA) is applied. HPOA serves various functions within the RDS, specifically targeting the optimization of VPP location, VPP resource management, and the objective functions. A case study is conducted, incorporating RESs, BESS, and EVs, and compared against existing algorithms such as BESA and SMA using a standard IEEE 85-bus RDS. Simulation results conducted in MATLAB demonstrate that the proposed HPOA algorithm effectively determines the optimal size and location of VPPs, leading to improved economic, operational, and resilience indices in the network.

Operation Scheduling of Distribution Network with Photovoltaic/Wind/Battery Multi-Microgrids and Reconfiguration considering Reliability and Self-Healing

In this paper, a new simultaneous framework of distribution network operation is proposed based on the scheduling of photovoltaic/wind/battery multi-microgrids with network reconfiguration considering self-healing. The objective function is considered to minimize the energy losses, and multi-microgrids cost and improve the reliability indices including minimization of energy not-supplied (ENS) and minimizing system average interruption duration (SAIDI) and system average interruption frequency (SAIFI) indices. The optimization variables are defined as the situation of the distribution network switches to find the network’s optimal configuration with the installation location and size of renewable resources and battery energy storage during 24 hours. An improved beluga whale optimization (IBWO) based on a nonlinearly diminishing inertia weight (NDIW) approach is used to find the optimal variable set of the problem. The recommended methodology is implemented on 33-bus and real 59-bus distribution networks. The results demonstrated that by obtaining an optimal state of the network switches in the event of a fault, as well as the optimal scheduling of the two microgrids, the energy losses have decreased and the reliability indices have improved. The outcomes of the proposed methodology based on the hybrid multi-microgrid allocation and network reconfiguration are stated that the losses, ENS, SAIDI, and SAIFI are reduced by 66.39%, 54.00%, 50.24%, and 33.61%, respectively, for 33-bus network and are declined by 65.29%, 57.44%, 48.63%, and 62.33%, respectively, for the real 59-bus Ahvaz network in comparison with the base network. The obtained results illustrated that in the condition of the network line outage, by simultaneously implementing the reconfiguration with the change of network switches and the optimal allocation and scheduling of HMGs in the network, self-healing is provided to prevent a significant weakening of the network performance so that the resilience is improved in addition to minimizing the energy losses and the cost of HMG energy injection compared to the base network. The findings revealed that the objectives including losses, ENS, SAIFI, and SAIDI are increased by 78.29%, 33.00%, 83.33%, and 35.11%, respectively, due to outage of line 22 of the 33-bus network and these objectives are increased by 52.32%, 10.30%, 32.29%, and 79.66%, respectively, due to outage of line 12 of the 59-bus network compared with not considering the line outage. Moreover, the superior capability of the recommended NDIW-based IBWO has been confirmed in comparison with the well-known particle swarm optimizer (PSO) and ant lion optimizer (ALO) in solving the problem to achieve better objective value.

Reliability Evaluation of Radial Distribution System with different locations and ratings of Photovoltaic

The primary objective of this work is to examine how strategically positioning Photovoltaic (PV) units can have effect on reliability of a distribution system. Feeder-1 of RBTS BUS2 system is used for the reliability evaluation. The study utilizes FMEA technique to evaluate the reliability indices, System Average Interruption Frequency Index (SAIFI), System Average Interruption Duration Index (SAIDI), Energy Not Supplied (ENS) and Average Service Availability (ASAI) linked to electrical interruptions in two different situations such as no PV unit and PV units are placed at different locations with different ratings along the feeder. The outcomes of installing PV units at different points along the distribution feeder are examined and compared. The prime focus lies in identifying the reliability based optimal placement of the PV unit, a determination made based on the reliability indices. By systematically analyzing and comparing the reliability indices under varying conditions, the research aims to provide insights into how the strategic positioning of PV units can lead to a substantial enhancement in the overall reliability of the distribution system. The findings contribute to the broader understanding of integrating renewable energy sources for reliability improvement within distribution systems and offer practical guidance for decision-makers in the field of energy distribution and planning.

Energy optimization of stand-alone electrical grid considering the optimal performance of the hydrogen storage system and consumers

The surge in energy storage systems and the increasing involvement of demand-side participation can be attributed to their favorable characteristics, including their seamless integration into electrical networks and their capacity to offer operational flexibility during critical periods. This scholarly article focuses on enhancing energy utilization in an autonomous electrical grid by incorporating hydrogen storage and demand-side participation. The optimization of the stand-alone electrical grid is based on maximizing efficiency and minimizing energy consumption costs as the main objective functions are modeled. The modeling efficiency is formulated considering the ratio of the energy not supplied (ENS) to energy generation by resources. And costs of energy consumption are modeled as consumption of fuel costs by resources. The consumers’ participation is proposed based on an incentive approach to consumers for demand shaving in peak times. Also, the hydrogen storage system is installed in the stand-alone electrical grid to improve the main objectives. The particle swarm optimization (PSO) algorithm for energy optimization and solving objective functions is applied. In the end, numerical simulation is carried out in some case studies to confirm and supremacy of energy optimization with the participation of the hydrogen storage system and consumers. The case studies based on non-participation and participation of the storage system and consumers in energy management are implemented. The implementation of case studies examines the impact of both non-participation and participation of the storage system and consumers in energy management. The findings reveal that when the storage system and consumers actively participate, there is a significant improvement in efficiency and a substantial reduction in energy costs. Specifically, the efficiency is enhanced by 3% and the energy costs are reduced by 29.5% compared to the scenario where they do not participate.

Probabilistic Energy Management of DGs and Electric Vehicle Parking Lots in a Smart Grid considering Demand Response

In this paper, a novel model of an energy management system (EMS) for a microgrid (MG) under uncertain conditions is proposed. The MG consists of renewable photovoltaic and wind sources along with electric vehicle parking lots. Hence, the model incorporates the uncertainties of renewable DGs, parking lots, and also load. In this study, the MG operation cost and voltage stability index are considered objective functions. A novel combined algorithm (hMOPSO-HS) is proposed for microgrid energy management. The hMOPSO-HS algorithm is a combination of the mutant multiobjective particle swarm optimization (MOPSO) algorithm and the harmony search (HS) algorithm. The simulations are performed in two parts, with and without considering the uncertainty. The comparative analysis involves evaluating the optimization outcomes achieved by the hMOPSO-HS algorithm in contrast to various other metaheuristic algorithms for multiobjective optimization. The simulation findings validate the superior efficacy of the hMOPSO-HS algorithm compared to other approaches. Also, the simulation results showed that in the conditions of uncertainty, the operating cost is 6.1% higher and the microgrid stability index is 6.8% lower. Also, considering the uncertainty has caused the penalty for energy not supplied (ENS) and demand response program (DRP) costs to increase by 3% and 4%, respectively.

A three-stage reliability-centered framework for critical feeder identification, failure modes prioritization, and optimal maintenance strategy assignment in power distribution system

In this paper, a three-stage reliability-centered maintenance (RCM) framework for critical feeder identification, failure modes prioritization, and optimal maintenance strategy assignment is presented for the power distribution system. This three-stage methodology addresses a notable gap in the literature by incorporating these three stages together. The first stage ranks critical feeders using the BWM and TOPSIS methods. In the second stage, the failure modes are prioritized using feeder rank, severity, occurrence, and detection factors and are assigned a score representing their priority. Finally, in the third stage, a maintenance strategy assignment, formulated as a mixed-integer linear programming (MILP) optimization problem is proposed. The optimization problem considers three strategies, cold-line, hot-line, and run-to-failure (RTF), assigned to each failure mode. Besides, the possibility of implementing cold-line repairs overlapping with the upstream grid's scheduled outages and the availability of several maintenance crews are taken into account. The objective function is to minimize the total maintenance costs, which include operation, equipment, energy not supplied (ENS), expected energy not supplied (EENS), and future corrective maintenance costs. The proposed approach is implemented on a real distribution system. The results indicate that the proposed RCM framework leads to a 26.32 % reduction in the EENS compared to selecting the RTF strategy for all components. Besides, the proposed optimization-based method leads to a 29 % lower objective function than the business-as-usual maintenance planning, and the proposed three-stage RCM framework outperforms a previously published state-of-the-art method by decreasing the objective function and the EENS.

Optimal siting and sizing of custom power system and smart parking lot in the active distribution network

AbstractNon‐linear loads, electrical vehicles (EVs) and power electronics are the sources of harmonic generation in the power system. Harmonics are the reason for the disturbance in the technical constraints and quality of the power system. These disadvantages are formed in the current and the voltage of the system branches and buses. To reduce these harmonics, active and passive power filters are used. Since active power filters (APFs) include electronic power switching devices, they are used to eliminate or reduce current or voltage harmonics. To properly reduce these harmonics and supply consumer demand, private investors (PIs) are considered to enhance the power quality, by investing in the APF units and the distribution network operator (DNO) coordinating and feeding the EVs, by equipping the network with smart parking lots. Total harmonic distortion, bus voltage profile, active power loss and energy not supplied (ENS) are considered problem attributes. Using techniques for order of preference by similarity to the ideal solution (TOPSIS), it is determined how the addition of photovoltaic (PV), energy storage system, and batteries affects the ranking of attributes from the perspectives of both DNO and PI. The best solutions are obtained in the first stage using a genetic algorithm (GA), and the attributes have been prioritized from a utility perspective in the second stage by TOPSIS. To verify the performance of GA and TOPSIS, all of the obtained results are compared with the other algorithms. As seen clearly, active, reactive power loss and ENS in scenarios with the participation PV are reduced. Furthermore, comparing the results of different algorithms confirms the efficiency of GA and TOPSIS.

Pengaruh Automatic Change Over Switch Terhadap Peningkatan Keandalan Gardu Distribusi T3-200A/1 Penyulang PLR-04

The process of distributing electrical energy has the potential to cause disturbances that can occur due to natural disturbances, disturbances to electrical equipment, disturbances at the generating center, transmission lines, and disturbances in the distribution network that can disrupt the reliability and service quality of the distribution of electrical energy. The quality of reliability can be rated by the length of outages, which can be determined using the System Average Interruption Duration Index (SAIDI), and how often blackouts occur in one unit of time, which can be determined using the System Average Interruption Frequency Index (SAIFI). In maintaining the reliability of electrical energy, additional equipment is needed that is capable of supplying backup energy to the load when a disturbance occurs, namely the Automatic Change Over Switch (ACOS). ACOS is a device that works automatically for the process of maneuvering the network from two voltage sources to maintain electricity availability without having to have long blackouts due to disturbances or loss of voltage at the main source. The use of the Automatic Change Over Switch (ACOS) is proven to be able to increase the reliability of electrical energy at distribution substation T3-200A/1 feeder PLR-04 by reducing the length of time outages (SAIDI) and reducing the frequency, because customers don't feel outages when the disturbance occurs. In addition, the use of Automatic Change Over Switch (ACOS) can also reduce the energy not supplied (ENS)

Reliability Comparison of Rural & Urban LVDS Networks with Corresponding HVDS Networks Including Substation Reliability

This paper mainly determines the comparison of reliability of an existing rural area & urban area LVDS (low voltage distribution system) with their respective proposed high voltage distribution system (HVDS) and also including their 33/11 kV Substation. Ring main substation and single bus bar substation configuration are considered for urban and rural distribution systems respectively. The reliability of ring bus substation used in urban area is more as compared to single bus substation used in rural area. The load point reliability indices are calculated considering both substation reliability indices and radial feeder indices. The substation failure rate is calculated by considering overlapping outages approach and whereas the radial feeder indices are calculated by using cut set approach. Further, the LVDS Network is converted into the HVDS Network by replacing a high rating distribution transformer (DTR) supplying to a group of customers with Large in number with multiple small rating distribution transformers near to consumer terminals with less number of customers in each group. This conversion leads to Minimum length of LT Lines in HVDS Which results in improving voltage profile, reliability and efficiency. In this paper the disconnecting switches and alternative supply are also considered on both LVDS and HVDS Networks in the calculation of the reliability of to assess the reliability of the radial distribution system (FMEA)Failure mode effect analysis is used. This study uses a LVDS and HVDS system, and uses an alternative power source and disconnectors, to determine reliability indices of the rural area network and urban area network distribution system. In this paper the important distribution reliability indices to reduce system average interruption durations (SAIDI), Energy Not Supplied (ENS) and ASAI for LVDS and corresponding HVDS are calculated. The research idea is proposed on a Indian practical networks.

Electric power distribution maintenance model for industrial customers: Total productive maintenance (TPM), reliability-centered maintenance (RCM), and four-discipline execution (4DX) approach

The reliability of the electric power distribution system is crucial for industrial consumers, particularly in Karawang Regency, one of the industrial centers in Indonesia. The management of electricity supply for industries in Karawang Regency is entrusted to the Indonesian State Electricity Enterprises, also known as PLN. However, there are still disturbances in the distribution system that affect industrial customers. The primary causes of these disturbances are 39% jointing damage, 36% damage caused by third parties, and 15% damage to cubicles. The occurrence of these disturbances is closely tied to maintenance activities on the electric power distribution system. To assess the distribution system's reliability, various indices are calculated, including the System Average Interruption Frequency Index (SAIFI), System Average Interruption Duration Index (SAIDI), energy not supplied (ENS), and feeder disturbance indices. Currently, PT PLN (Persero) UP3 Karawang employs a maintenance model that combines corrective and preventive methods with the four-discipline execution (4DX) approach. This study aims to develop an improved maintenance model that enhances the overall performance of the electric power distribution system for industrial customers. In this study, a maintenance model is proposed by integrating the total productive maintenance (TPM) method, reliability-centered maintenance (RCM) method, and four-discipline execution (4DX) approach. The developed maintenance model is tested on 40 feeders serving industrial customers. A comparative analysis is performed before and after implementing the maintenance model, considering the SAIFI, SAIDI, ENS, feeder disturbance, and overall equipment effectiveness (OEE) indexes. The analysis of the 40 feeders reveals improvements in performance following the implementation of the maintenance model using the TPM, RCM, and 4DX methods. Prior to implementation, the SAIFI was 0.24 times/cust.yr, SAIDI was 1.877 min/cust.yr, ENS was 60,470 kWh, feeder disturbance occurred 8 times, and OEE was 0.99935. After implementation, the SAIFI improved to 0.016 times/cust.yr, SAIDI reduced to 1.342 min/cust.yr, ENS decreased to 41,241 kWh, feeder disturbance reduced to 4 times, and OEE increased to 0.99962. Consequently, the performance improvements are as follows: SAIFI by 33%, SAIDI by 29%, ENS by 32%, feeder interference by 50%, and OEE by 0.027%.

A Heuristic-Driven Charging Strategy of Electric Vehicle for Grids with High EV Penetration

The widespread adoption of electric vehicles (EVs) poses challenges associated with charging infrastructures and their impact on the electrical grid. To address these challenges, smart charging approaches have emerged as a key solution that optimizes charging processes and contributes to a smarter and more efficient grid. This paper presents an innovative multi-objective optimization framework for EV smart charging (EVSC) using the Dynamic Hunting Leadership (DHL) method. The framework aims to improve the voltage profile of the system in addition to eliminating voltage violations and energy not supplied (ENS) to EVs within the network. The proposed approach considers both residential EV chargers and parking stations, incorporating realistic EV charger behaviors based on constant current charging and addressing the problem as a mixed integer non-linear programming (MINLP) problem. The performance of the optimization method is evaluated on a distribution network with varying levels of EV penetration connected to the chargers in the grid. The results demonstrate the effectiveness of the DHL algorithm in minimizing conflicting objectives and improving the grid’s voltage profile while considering operational constraints. This study provides a road map for EV aggregators and EV owners, guiding them on how to charge EVs based on preferences while minimizing adverse technical impacts on the grid.

Stochastic Multi-Objective Scheduling of a Hybrid System in a Distribution Network Using a Mathematical Optimization Algorithm Considering Generation and Demand Uncertainties

In this paper, stochastic scheduling of a hybrid system (HS) composed of a photovoltaic (PV) array and wind turbines incorporated with a battery storage (HPV/WT/Batt) system in the distribution network was proposed to minimize energy losses, the voltage profile, and the HS cost, and to improve reliability in shape of the energy-not-supplied (ENS) index, considering energy-source generation and network demand uncertainties through the unscented transformation (UT). An improved escaping-bird search algorithm (IEBSA), based on the escape operator from the local optimal, was employed to identify the optimal location of the HS in the network in addition to the optimal quantity of PV panels, wind turbines, and batteries. The deterministic results for three configurations of HPV/WT/Batt, PV/Batt, and WT/Batt were presented, and the results indicate that the HPV/WT/Batt system is the optimal configuration with lower energy losses, voltage deviation, energy not supplied, and a lower HS energy cost than the other configurations. Deterministic scheduling according to the optimal configuration reduced energy losses, ENS, and voltage fluctuation by 33.09%, 53.56%, and 63.02%, respectively, compared to the base network. In addition, the results demonstrated that the integration of battery storage into the HPV/WT enhanced the various objectives. In addition, the superiority of IEBSA over several well-known algorithms was proved in terms of obtaining a faster convergence, better objective value, and lower HS costs. In addition, the stochastic scheduling results based on the UT revealed that the uncertainties increase the power losses, voltage deviations, ENS, and HPV/WT/Batt cost by 2.23%, 5.03%, 2.20%, and 1.91%, respectively, when compared to the deterministic scheduling.

Optimal placement and coordination of the protection devices simultaneous with optimal allocation of the DGs

AbstractOptimal coordination of the protection devices (PDs) is performed here simultaneous with optimal allocation of them and the DGs. The PDs are a set of directional overcurrent relays (DOCRs) and voltage relays, each with its own fast switch (FS). In previous optimal DG placement studies the coordination of the PDs has been considered as a constraint. So, it was not possible to optimal coordination of the PDs simultaneously. In the present study, the coordination between the PDs is considered in calculating the value of the energy not supplied (ENS) that is a part of an overall cost function. The cost function considered here includes cost of purchase, installation, and maintenance of the DGs and the PDs, cost of the energy losses, cost of the ENS, cost of the purchased energy from the main grid and the DGs, and the penalty cost for emitting environmental pollutants. Here, output of the renewable DGs and the network loads during the year, in a 96‐h model, has been considered as variable. To solve the desired optimization problem, an algorithm based on evolutionary algorithms such as genetic algorithm (GA) is proposed. Finally, various cases have been simulated by considering a standard 33‐bus radial network.

Stochastic electrical, thermal, cooling, water, and hydrogen management of integrated energy systems considering energy storage systems and demand response programs

Energy hub (EH) is a multi-energy system that combines several energy sources to increase energy supply efficiency, flexibility, and reliability. In this paper, a stochastic two-step multi-objective optimization method is proposed for multi-EH planning that focuses on economic, environmental, and security concerns. In order to meet the demands for electricity, heating, cooling, hydrogen, natural gas, and water, the proposed model includes several components, such as nonrenewable and renewable energy sources, cogeneration units, converters, and storage systems. Also, price-based demand response programs are implemented to provide demand-side flexibility for EHs. The proposed multi-objective framework investigates the total cost of the system, average reserve index (ARI), emissions of the system, and energy not supplied (ENS), simultaneously. EHs categorize their objectives into main and secondary based on priority. Since the total cost is so important, it has been recognized as the primary objective that is minimized in the first step. Also, the emissions, ENS, and ARI are the secondary objectives that are optimized in the second step simultaneously. The proposed model's efficiency is evaluated using an 18-bus test network in both summer and winter seasons. The simulation results present that the proposed model improves the emission, ARI, and ENS by 47.78 %, 53.4 %, and 23.38 % in summer seasons, respectively. And also, the multi-objective framework improves the emission, ARI, and ENS by 45.66 %, 65.84 %, and 52.56 % in winter seasons, respectively.

Microgrids Resiliency Enhancement against Natural Catastrophes Based Multiple Cooperation of Water and Energy Hubs

With the ever-growing frequency of natural catastrophe occurrences such as hurricanes, floods, earthquakes, etc., the idea of resilient microgrids (MGs) has attracted more attention than before. Providing the opportunity for a multi-carrier energy supply after a natural catastrophe can lessen power losses and improve power resiliency and reliability. Critical loads within the MG can be prioritized and restored in the shortest possible time based on the condition of the network after the damaging occurrence by considering the energy hub (EH) systems and the optimum design and allocation of these multi-carrier systems. To this end, this paper aims to address the resilience framework in MGs considering sets of water and EHs (WEHs) consisting of CHP (combined heat and power), a boiler, energy storage, and a desalination unit. This study focused on considering an effective resilient scheme to restore critical loads in a short period after a natural catastrophe when the MG experiences an unpredictable event. By applying the idea of WEHs, there would be a chance of restoring the system by using two sets of WEH systems in the appropriate islanded points to restore the system and critical loads of electricity, heat, and water. For this purpose, different scenarios were considered for assessing the resiliency of the system against a natural catastrophic event that causes serious damage to the network by analyzing the energy-not-supplied (ENS) factor. Moreover, the allocated WEHs can adequately supply the electrical, water, and thermal demand loads throughout the day after the natural catastrophe. To mitigate the unforeseen variations in the renewable sources, a battery is located in the WEH, which can attend to the optimal scheduling effectively. A scenario-based method is also introduced to improve the resiliency of MGs in an uncertain environment such as electrical, heat, and water stochastic demands. The appropriate efficiency of the offered model was considered on a modified IEEE test system.

Sizing of community centralized battery energy storage system and aggregated residential solar PV system as virtual power plant to support electrical distribution network reliability improvement

Virtual Power Plants (VPP) have been seen as one of the techniques to integrate more decentralized and distributed variable renewable energy systems into the grid. They will thus facilitate the greater democratization of the energy systems. VPP is an aggregation of renewable energy technologies, such as solar PV. It is usually operated with battery energy storage system (BESS) facilities to solve the variability issue of solar PV. However, BESS used as part of VPP has to be appropriately sized to work in the electrical network. While VPP operated with BESS may provide significant technical and financial advantage, most of the BESS sized to be used as VPP in the literature are large scale, operated with large scale VRE’s in the grid. Some of the literatures also provide the use of BESS sizing with Solar PV but for shared residential community use only. The context of BESS sizing for VPP from a residential community is not prevalent in the literature. In this paper, the context of a sizing BESS for VPP from a residential neighbourhood is studied. It is aimed that by optimally sizing BESS for a residential community with rooftop solar PV can supply power to the community and any excess energy may be used for VPP application for the grid. This paper provides a practical process for evaluating the proper size of a centralized BESS in a community with rooftop Solar PV by considering the energy consumption and peak demand profiles of the residential units in three types of communities with rooftop Solar PV, namely low-cost, medium-cost, and high-end community, by considering the mean and maximum energy consumption and peak demand profiles of each residential unit. Such values are used to allocate for the Solar PV system per residential unit and the centralized BESS at mean, 75% of maximum, maximum, and 125% of maximum energy consumption, to be used for the community and for any excess energy to support the electrical networks reliability improvement. The result shows that using the maximum and 125% of maximum energy consumption can provide more than enough energy capacity for VPP application, particularly electrical networks reliability improvement by supplying for the Energy not Supplied (EnS), EN. The result provides that for a High-end community, the stored energy in the BESS can reach up to 24.578 MW h using 125% of maximum sizing at either mean or maximum energy consumption. While for Medium-cost and Low-cost communities, the stored energy can reach up to 17.549 MW h and 14.527 MW h using 125% of maximum sizing at either mean or maximum energy consumption, respectively. Moreover, the result also shows that to alleviate Energy not Supplied (EnS) and contribute to reliability improvement, more residential units are needed if sizing is based on maximum energy consumption, while a lesser number of residential units if the size is based on 125% of the maximum energy consumption.