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Manganese (Mn) accumulation and release in distribution systems degrade water quality and pose underappreciated risks to public health. Main cleaning is an essential preventive maintenance practice to sustainably remove legacy Mn and preserve water quality to the tap. [ABSTRACT FROM AUTHOR]

This article proposes a new three-stage stochastic framework for dealing with predictable two-phase natural disasters in distribution systems. This framework is a multiobjective optimization, in which the amount of curtailed energy, the number of switching actions, and the vulnerability of operational components are selected as the main criteria for decision-making process. The optimization problem is formulated in the form of a stochastic mixed-integer linear programming (MILP) problem. In this article, a windstorm event followed by flooding is analyzed as a two-phase natural disaster. In this regard, the uncertainties associated with gust-wind speed, floodwater depths, and load demands are taken into account by the proposed framework. The initial configurations of islands are formed just ahead of the storm event (first stage), and their borders are changed in the second stage, which is associated with the storm event and its aftermath. The final configurations of islands are determined by the third stage once the uncertainties of floodwater depths are revealed. In the proposed framework, the emergency generators (EGs) are assumed to be prone to flooding, and a novel approach is proposed for quantifying the flood-related failure probability of EGs. Likewise, overhead distribution structures are recognized as vulnerable components to storms. The proposed framework is implemented on a test system, and its effectiveness is investigated and verified through seven case studies. [ABSTRACT FROM AUTHOR]

Distributed generation (DG) aims to generate part of the required electrical energy on a small scale closer to the places of consumption. Integration of DG into an existing electric distribution network (EDN) has technical, economic, and environmental benefits. DG placement is typically determined by investors and local conditions such as the availability of energy resources, space, and licenses, among other factors. When the location of DG is not a decision of the distribution network operator (DNO), the simultaneous integration of distribution network reconfiguration (DNR) and DG placement can maximize the benefits of DG and mitigate eventual negative impacts. DNR consists of altering the EDN topology to improve its performance while maintaining the radiality of the network. DNR and optimal placement of DG (OPDG) are challenging optimization problems since they involve integer and continuous variables subject to nonlinear constraints and a nonlinear objective function. Due to their nonlinear and nonconvex nature, most approaches to solve these problems resort to metaheuristic techniques. The main drawbacks of such methodologies lie in the fact that they are not guaranteed to reach an optimal solution, and most of them require the fine-tuning of several parameters. This paper recasts the nonlinear DNR and OPGD problems into linear equivalents to obtain a mixed-integer linear programming (MILP) model that guarantees global optimal solutions. Several tests were carried out on benchmark EDNs evidencing the applicability and effectiveness of the proposed approach. It was found that when no DG units are considered, the proposed model can find the same results reported in the specialized literature but in less computational time; furthermore, the inclusion of DG units along with DNR usually allows the model to find better solutions than those previously reported in the specialized literature. [ABSTRACT FROM AUTHOR]

In this paper, we propose a safe deep reinforcement learning (SDRL) based method to solve the problem of optimal operation of distribution networks (OODN). We formulate OODN as a constrained Markov decision process (CMDP). The objective is to achieve adaptive voltage regulation and energy cost minimization considering the uncertainty of renewable resources (RSs), nodal loads and energy prices. The control actions include the number of in-operation units of the switchable capacitor banks (SCBs), the tap position of the on-load tap-changers (OLTCs) and voltage regulators (VRs), the active and reactive power of distributed generators (DGs), and the charging and discharging power of battery storage systems (BSSs). To optimize the discrete and continuous actions simultaneously, a stochastic policy built upon a joint distribution of mixed random variables is designed and learned through a neural network approximator. To guarantee that safety constraints are satisfied, constrained policy optimization (CPO) is employed to train the neural network. The proposed approach enables the agent to learn a cost-effective operating strategy through exploring safe scheduling actions. Compared to traditional deep reinforcement learning (DRL) methods that allow agents to freely explore any behaviors during training, the proposed approach is more practical to be applied in a real system. Simulation results on a modified IEEE-34 node system and a modified IEEE-123 node system demonstrate the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

Locating high impedance faults (HIF) in a distribution system increases the system’s reliability and improves the service’s quality for customers. However, this type of fault produces currents of low magnitude, making it difficult to locate using conventional techniques. Therefore, this paper proposes a method for the location of single-phase high impedance faults in distribution systems with photovoltaic cells in low voltage. The method is based on the use of the sequence component of the current circulating in the medium voltage feeders of the distribution system, which are obtained from synchronized phasorial meters positioned along the feeder. The method considers nonsymmetrical lines, unbalanced loads, capacitors banks, photovoltaic DGs, and load variation. The performance of the proposed method was evaluated and tested on several distribution systems using ATP or PSCAD® and the results show that the method can locate HIFs with high precision. Also, a comparison was made with recently published methods to show the efficiency of the proposed method. [ABSTRACT FROM AUTHOR]

A modified marine predators optimizer (MMPO) is proposed for simultaneous distribution network reconfiguration (DNR) associated with the allocation of distributed generators (DGs). In the MMPO, the predator's strategies are merged to consider the possibilities for variation in the environmental and climatic circumstances. The suggested MMPO is contrasted with the standard marine predators optimizer (MPO) and genetic, harmony search, fireworks, firefly and improved sine–cosine optimizers. The proposed MMPO is validated on single and multiple objectives using 33- and 69-bus distribution systems at light, nominal and heavy loading levels. The results obtained by the proposed MMPO are compared with those obtained by the original MPO and other optimizers. The achieved simulation outputs reveal a great improvement over the standard MPO and demonstrate the superiority of the proposed MMPO for simultaneous DNR and DG allocation. [ABSTRACT FROM AUTHOR]

In this manuscript, an Energy Management System (EMS) in distribution system with Internet of Things (IoT) using hybrid approach is proposed. The proposed approach is the combination of Shuffled Shepherd Optimization (SO) and Feedback Artificial tree (FAT) algorithm and hence it is named SOFAT technique. The major intention of the proposed work is "to optimally maintaining the power and resources of distribution system by constantly monitor the data from the IoT-based communication framework". Here, the distribution system is integrated to data acquisition module that is an IoT object with unique IP address resulting in a large mesh wireless network devices. The IoT-based communication framework is utilized to facilitate the demand response (DR) EMS development in distribution system. The framework collects the DR from load and transmits the data to the centralized server. The transferred data is activated by SOFAT approach. In distribution system, the IoT structure increases these networks flexibility and provides optimum usage of available resources. In addition, the proposed approach is responsible to satisfy the overall supply and energy requirements. Finally, the proposed approach is carried out in MATLAB/Simulink, its efficiency is analyzed with other existing approaches. [ABSTRACT FROM AUTHOR]

One of the main causes of power supply interruption in distribution networks is the vegetation. The high cost of tree trimming schedules encourages utilities to look for more efficient ways to manage vegetation. In this work, the vegetation maintenance planning is formulated as a combinatorial multi-objective optimization problem, which determines the most appropriate tree trimming schedules in the zones of the distribution feeders. This formulation considers two conflicting objectives: one is the social cost given by the customer interruption cost, and another is the vegetation maintenance cost of utility. The constraints comprise the availability of human resources, distribution system reliability and priority zones. The solution technique of the proposed optimization problem is the elitist non-dominated sorting genetic algorithm II for two different application cases. In the first case, failure rates from vegetation in the zones are obtained using a reliability index and a vegetation growth model. In the second application case, failure rate multipliers are used to quantify the impact of maintenance on system reliability. Results from the application of the optimization algorithm for both cases in a real-world distribution system composed of 11 feeders are presented and discussed. [ABSTRACT FROM AUTHOR]

This study proposed a probabilistic methodology based on a confidence interval with the aim of overcoming the limitations of deterministic methods. A stability evaluation technique was required because the output variability of renewable energy can lead to instability of the distribution system. The proposed method can predict the possibility of violating stability in the future. It can also provide a theoretical basis for securing distribution system stability and improving operational efficiency by assessing the in-stability risk and worst-case scenarios. Because of steady-state analysis in the distribution system to which solar power is connected, the probability of violating the standard voltage during the daytime when PV fluctuations are severe was the highest. Moreover, as a result of a simulation of a three-phase short-circuit in the distribution system that is connected to the PV and WT, it was observed that it could violate the allowable capacity of the CB owing to the effects of the power demand pattern and output variability. [ABSTRACT FROM AUTHOR]

Recent advances in distribution-level phasor measurement unit (D-PMU) technology have enabled the use of voltage phase angle measurements for direct load sharing control in distribution-level microgrid interconnections with high penetration of renewable distributed energy resources (DERs). In particular, D-PMU enabled voltage angle droop control has the potential to enhance stability and transient performance in such microgrid interconnections. However, these angle droop control designs are vulnerable to D-PMU angle measurement losses that frequently occur due to the unavailability of a global positioning system (GPS) signal for synchronization. In the event of such measurement losses, angle droop controlled microgrid interconnections may suffer from poor performance and potentially lose stability. In this paper, we propose a novel distributed mixed voltage angle and frequency droop control (D-MAFD) framework to improve the reliability of angle droop controlled microgrid interconnections. In this framework, when the D-PMU phase angle measurement is lost at a microgrid, conventional frequency droop control is temporarily used for primary control in place of angle droop control to guarantee stability. We model the microgrid interconnection with this primary control architecture as a nonlinear switched system and design distributed secondary controllers to guarantee stability of the network. Further, we incorporate performance specifications such as robustness to generation-load mismatch and network topology changes in the distributed control design. We demonstrate the performance of this control framework by simulation on a test 123-feeder distribution network.

The task of determining the phase connection of customers, known as phase identification, is crucial to obtain accurate distribution system models. This paper starts with a thorough literature review of the existing phase identification methods, which are broadly divided into three categories: hardware-based, real power-based, and voltage-based methods. This is followed by multiple case studies assessing the accuracy of six real power- and voltage-based phase identification algorithms on four realistic distribution test systems. Synthetic load profiles along with network models are used to quantify accuracy of each method for different scenarios: varying advanced metering infrastructure (AMI) coverage, number of initially mislabeled customer phases, number of available samples, and measurement noise. A case study using a real AMI data set, including field verification, is also provided. Finally, several aspects key to accurate phase identification are discussed in detail.

Home Energy Management Systems (HEMSs) may not be able to solve network issues, especially in the presence of high penetration level of Electric Vehicles (EVs) and decentral renewable energy. To solve the problem, Grid Energy Management Systems (GEMSs) were introduced. However, because of the contradictory nature of the main objectives of HEMS which are economical oriented on end-users, e.g., cost minimization, and GEMS which are technical oriented on system operators, e.g., maximization of system stability and power quality cannot be satisfied simultaneously. Hence, a multi-level energy management system seems to be necessary to improve the techno-economic performance of the distribution system while satisfying end-users, electricity retailers, and the system operator. Because of the significance of the subject, this paper presents the state-of-the-art regarding different energy management systems at home, aggregator, and network levels. The advantages and disadvantages of each system are discussed and compared, considering their main elements such as objective functions, constraints, optimization algorithms, communication protocols, and impact of EVs. The challenges and limitations in hierarchical energy management are explained. Finally, some future research directions are suggested to improve the multi-level energy management system.

The increasing deployment of distributed energy resources (DERs) and microgrids benefits power grids by improving system resilience. In a resilience mode without the utility system, the distribution grid relies on DERs to serve critical load. In such a severe event with multiple faults on the distribution feeders, actuation of various protective devices (PDs) divides the distribution system into electrical islands. The undetected actuated PDs due to fault current contributions from DERs can delay the restoration process, thereby reducing the system resilience. In this paper, algorithms are proposed for outage management and feeder restoration for distribution systems with multiple DERs. The Advanced Outage Management (AOM) identifies the faulted sections and actuated PDs in a distribution system with DERs by incorporating smart meter data. The Advanced Feeder Restoration (AFR) is proposed to restore a distribution system with available energy resources taking into consideration the availability of utility sources and DERs as well as the feeder configuration. By partitioning the system into islands, critical load will be served with the available generation resources within islands. When the utility systems become available, the optimal path will be determined to reconnect these islands back to substations and restore the remaining load. The proposed method has been validated with modified IEEE 123-Bus and 8500-Node Test Feeders. Simulation results demonstrate the capability of the integrated AOM and AFR to enhance distribution system resilience.

This work proposes positive- and negative-sequence equivalent circuits of grid-forming inverters for steady-state analysis. The proposed models are especially attractive for performing long-duration voltage regulation analysis and short-circuit studies involving grid-forming inverters. Our proposed equivalent circuit models are based on the inverter’s voltage and current control loops in the $\alpha \beta $ and dq frames. For this reason, they operate according to prescribed control functions and specified impedances (i.e., filter impedance, current limiter block, virtual admittance block, and PI/PR controller block). The equivalent circuit model accuracy is validated by comparing system steady-state voltage and current responses obtained by detailed time-domain models in PSCAD/EMTDC to those by the equivalent circuit models implemented in steady-state load flow program (e.g., OpenDSS). Two distinct control structures implemented in the $\alpha \beta $ and dq frames are used for the validation. Single line-to-ground and line-to-line-to-ground faults are simulated in a small islanded microgrid as well as the IEEE 34-node test feeder. Fault impedances varying from 0 to 5 ohms are simulated. We show that the equivalent models precisely replicate the steady-state response of the detailed time-domain models.

The transition of the transportation sector from internal combustion engine vehicles to battery electric vehicles (EVs) will heavily increase the energy demand on the network, causing severe techno-economic problems. To solve these issues, advanced charging strategies were proposed to reduce the EVs’ charging impact on the network. The problem arises when all EV-owners decide to fully charge their EVs at night even if they might not use the total charged energy the next day. Hence, even with the presence of advanced charging and control strategies, the problem of high penetration level of EVs might not be completely solved without the positive participation of the EV-owners. Some questions can be asked and need answers. Is it necessary to fully charge all EVs at night? What happens if fully charging the EVs is delayed to the next day? To answer these questions, this paper studies the impact of charging EVs to different State of Charge (SOC) levels on the network. Since controlling the charging of all EVs is difficult, a three-level charging strategy is developed that suggests the SOC threshold-limit for each EV, which guarantees the network’s operation within its maximum limits even with a 100% penetration level of EVs charging simultaneously.

This study assesses the technical and financial impacts on voltage levels, peak demand, and technical losses of distribution systems due to the integration of battery energy storage systems (BESSs) associated with photovoltaic distributed generation (PVDG). It assumes that the BESS features (location and storage capacity) are chosen by consumers individually, aiming to directly benefit themselves according to self-consumption and price arbitrage uncoordinated control strategies. Computational simulations of several PVDG/BESS penetration levels in residential feeders of a Brazilian utility were performed via OpenDSS, employing real data of the load (consumption and load profiles) and photovoltaic generation (irradiance and temperature). We address the PVDG/BESS locations and the selection of solar irradiance, temperature, and load profile based on the Monte Carlo method. As the impacts on voltage levels, peak demand, and technical losses are expressed in different units, they were stochastically converted into monetary amounts to identify the control strategy that enhances the technical benefits in the distribution system. The results show that BESS installation with location, size, and control strategy defined by consumers creates technical benefits for the distribution system. The risk analysis allows us to conclude that price arbitrage should be prioritized in deciding incentives for penetration levels of up to 60%. Above these levels, self-consumption should be prioritized as an incentive. However, it is worth mentioning that the two strategies could be encouraged regardless of the penetration levels, as both create technical benefits for the distribution networks. The proposed method can support policy proposals to encourage BESS integration in distribution systems.

Water utilities stand a better chance of a smoothly run project to replace or rehabilitate pipes if they choose an approach that's the best fit for their specific circumstances. [ABSTRACT FROM AUTHOR]

A recent water treatment facility upgrade assessment included a thorough engineering analysis that compared three lime feed options based on corrosion control immersion testing results. [ABSTRACT FROM AUTHOR]

A New Mexico water utility augmented its advanced metering infrastructure to monitor pressure zones in its distribution system. [ABSTRACT FROM AUTHOR]

The penetration of renewable energy sources (RESs) is increasing in modern power systems. However, the uncertainties of RESs pose challenges to distribution system operations, such as RES curtailment. Demand response (DR) and battery energy storage systems (BESSs) are flexible countermeasures for distribution-system operators. In this context, this study proposes an optimization model that considers DR and BESSs and develops a simulation analysis platform representing a medium-sized distribution system with high penetration of RESs. First, BESSs and DR were employed to minimize the total expenses of the distribution system operation, where the BESS model excluding binary state variables was adopted. Second, a simulation platform based on a modified IEEE 123 bus system was developed via MATLAB/Simulink for day-ahead scheduling analysis of the distribution system with a high penetration of RESs. The simulation results indicate the positive effects of DR implementation, BESS deployment, and permission for electricity sales to the upper utility on decreasing RES curtailment and distribution system operation costs. Noticeably, the RES curtailments became zero with the permission of bidirectional power flow. In addition, the adopted BESS model excluding binary variables was also validated. Finally, the effectiveness of the developed simulation analysis platform for day-ahead scheduling was demonstrated. [ABSTRACT FROM AUTHOR]

Recent theoretical studies demonstrate the advantages of using decentralized architectures over traditional centralized architectures for real-time Power Distribution Systems (PDSs) operation. These advantages include the reduction of the amount of data to be transmitted and processed when performing state estimation in PDSs. The main contribution of this paper is to provide lab validation of the advantages and feasibility of decentralized monitoring of PDSs. Therefore, this paper presents an advanced trial emulating realistic conditions and hardware setup. More specifically, the paper proposes: (i) The laboratory development and implementation of an Advanced Measurement Infrastructure (AMI) prototype to enable the simulation of a smart grid. To emulate the information traffic between smart meters and distribution operation centers, communication modules, that enable the use of wireless networks for sending messages in real-time, are used, bridging concepts from both IoT and Edge Computing. (ii) The laboratory development and implementation of a decentralized architecture based on Embedded State Estimator Modules (ESEMs) are carried out. ESEMs manage information from smart meters at lower voltage networks, performing real-time state estimation in PDSs. Simulations performed on a real PDS with 208 buses (considering both medium and low voltage buses) have met the aims of this paper. The results show that by using ESEMs in a decentralized architecture, both the data transit through the communication network, as well as the computational requirements involved in monitoring PDSs in real-time, are reduced considerably without any loss of accuracy. [ABSTRACT FROM AUTHOR]

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Resilience problems from cyber-attacks on information communication technologies exist under their wide usage. False data injection (FDI) judiciously designed by attackers may cause severe consequences such as uneconomic operation and blackouts, particularly multivector energy distribution systems (MEDS), which are closely linked and interdependent. This article addresses the cyber resilient issues of an MEDS caused by FDI, considering the uncertainty from renewable resources. A novel two-stage distributionally robust optimization (DRO) is proposed to realize the day-ahead and real-time resilience improvement. The ambiguity set is based on both the Wasserstein distance and moment information. Compared to robust optimization which considers the worst case, DRO yields less-conservative solutions and thus provides more economic operation schemes. The Wasserstein metric-based ambiguity set enables to provide additional flexibility hedging against renewable uncertainty. Case studies are demonstrated on two representative MEDS networked with energy hubs, illustrating the effectiveness of the proposed cybersecured model. The produced adaptive robust economic operation for MEDS can reduce load shedding and enhance system resilience against severe cyberattacks. [ABSTRACT FROM AUTHOR]

Aiming at the deficiencies and shortcomings of traditional reactive power compensation devices in controlling and improving voltage quality, a bilevel programming model for the coordinated optimal allocation of active and reactive power capacity of photovoltaic power generation systems, wind-driven generators, shunt capacitors and static var compensators is established based on chance-constrained programming. In the optimization model, the randomness and intermittence of the output power of the distributed generation and the reactive power regulation ability of the distributed generation are considered. The correlation of illumination, wind speed and load fluctuation is considered and affected by seasonal variation. Latin hypercube sampling method is used to deal with the correlation of random variables. Monte Carlo probabilistic power flow method is used to calculate the power flow of distribution network with intermittent distributed generation systems. The location and capacity of active and reactive power sources in outer programming are optimized by adaptive artificial fish swarm algorithm, and the allocation scheme of active and reactive power sources determined by outer programming is optimized by inner adaptive artificial fish swarm algorithm. The two-layer nested adaptive artificial fish swarm algorithm is used to solve the optimal allocation problem constructed in this paper, and the optimal allocation scheme of active and reactive power supply capacity is determined. Taking IEEE33 distribution system as an example, the feasibility and applicability of the proposed model and algorithm are verified. [ABSTRACT FROM AUTHOR]

This paper proposes a Transient Stability Constrained Optimal Power Flow (TSC-OPF) formulation in order to calculate the optimum operating point of synchronous generators in distribution networks, in terms of dispatch of their active and reactive powers. However, the problem is complex due to the high number of constraints and variables, as well as, the presence of nonlinear constraints. To circumvent these adversities, an optimization problem is proposed in this paper with the application of a set of mathematical approximations in the constraints of the active and reactive power balance equations. In addition, an approximation is explored in the swing equation of synchronous generators belonging to a group of coherent machines, particularly regarding the active power injection from the generators. The proposed TSC-OPF is tested in a 31-bus radial distribution system with two and four generators. The results show that the running time to solve the proposed optimization problem with approximations becomes smaller when compared to the time to solve it without approximations. [ABSTRACT FROM AUTHOR]

We now realize that manganese is tasteless at relevant drinking water concentrations and that manganese speciation plays an important role in the visual detection of manganese. Best practice involves setting a water quality goal for manganese that is less than 0.05 mg/L. In 2019, Health Canada established an aesthetic objective of 0.02 mg/L for manganese to avoid discolored water events and minimize distribution system loading with manganese. Manganese Deserves More Respect Like inorganic contaminants in general, manganese deserves more respect, and water utilities need to assess their own positions in relation to the changing picture involving manganese. [Extracted from the article]

Load growth in electrical distribution networks became naturally occurring due to industrial development and human population demand growth. Consequently, system losses are continually raised while the voltage profile is reduced. This paper presents a novel hybrid method to determine the best locations and sizes of single and multiple of different renewable distributed energy resources (DER). The presented hybrid method is based on Salp Swarm Algorithm (SSA) and combined power loss sensitivity (CPLS). Integration of photovoltaics (PV) and wind turbines (WT) in distribution network is used to enhance the system voltage, minimize system losses and increase the system capacity. The effect of annual load growth in system load and system operating constraints are taken in consideration. IEEE 33-bus and 69-bus radial distribution systems (RDS) are used to validate the presented algorithm for integrating the DERS in distribution system. In addition, the presented algorithm is compared with different recent optimization algorithms in order to prove its effectiveness and superiority. [ABSTRACT FROM AUTHOR]

Manganese (Mn) releases within water distribution systems aren't always apparent, as in their effect on the aesthetic quality of water. Still, even without obvious visual signs, legacy Mn can present public health risks. Water utilities should develop a comprehensive Mn control strategy to safeguard water quality to the tap. [ABSTRACT FROM AUTHOR]

Inventory management presents numerous challenges for many supply chains as they are becoming more complex and composed of multiple stages. Using appropriate multi-echelon inventory management policies allows supply chains to deliver the required level of responsiveness efficiently by optimizing inventory levels across the entire network and improving customer service levels. This paper provides a Multi-Criteria Decision Making (MCDM) approach for the multi-echelon inventory system selection problem. The scope of this paper is limited to the case of Distribution systems. The suggested approach identifies for a given supply chain configuration, a set of selection criteria related to supply chain costs and overall responsiveness. These criteria are used to compare and choose the best alternative from different multiechelon distribution inventory system configurations by using a suitable MCDM method. Eight different multi-echelon distribution inventory system alternatives are generated. Each one is a combination of three main inventory policies: (i) replenishment policies, (ii) ordering policies, and (iii) safety stock allocation policies. The suggested approach is illustrated in the case of the pharmaceuticals products supply chain in the public sector in Morocco. Depending on the decision problem nature and other criteria, the AHP method proved to be the suitable MCDM method for selecting the best multi-echelon inventory system for the Moroccan pharmaceutical products supply chain. The analysis indicates that assigning inventory to the most downstream facilities close to patients and adopting an installation stock ordering policy implemented by a decentralized decision system is the best option for the supply chain considered in the case study. [ABSTRACT FROM AUTHOR]

Power distribution systems are challenged with rapid growth in load demand. Most times, it is observed that under certain critical loading conditions, distribution systems have high power loss and poor voltage profile which result in the collapse of certain areas in the network. To overcome these challenges, a number of power distribution systems incorporate distributed generation (DG) on the grid near to the load center. However, for DG to serve its purpose, its location and size has to be determined optimally. In this paper, Grid-Based Multi-Objective Harmony Search Algorithm (GrMHSA) has been utilized to determine the size and location of DG in the Debre Markos town distribution system. An algorithm was developed to optimally place the distributed generators (DGs). This was with the aim to reduce power losses in the distribution network and enhance the voltage profile of the system under consideration. A matlab program was developed to mitigate power losses and improve the voltage profile by the optimal sizing and placing of DGs in the distribution network. After sizing and placing the DG in the network, the total voltage deviation, active and reactive power losses were reduced by 85.20%, 84.94% and 85.73%, respectively. In addition, the performance of the proposed algorithm (GrMHSA) was compared with Multi-objective Particle Swarm Optimization (MOPSO) and was found outperform the MOPSO.

Recently, renewable energy sources have been applied in distribution systems because of climate change and environmental pollution. However, the integration of these sources in the distribution systems is a complex problem because most of their output power is naturally intermittent and depending on the primary energy resources. Additionally, electricity prices and loads are uncertainties. Therefore, this paper proposes a novel multi-scenario optimisation solution to the optimal reinforcement of the distribution systems considering uncertainties of renewable energy sources, electricity prices and loads by a mixed-integer nonlinear programming model with the aim of minimising life cycle cost. A combined model with the clustering technique is utilised to integrate all uncertainty parameters of the distribution systems. The proposed methodology is verified with a modified 9-node test system by a program implemented in the general algebraic modeling system. Results with and without the participation of renewable energy sources are analysed and compared to validate the high performance of the proposed approach.

This paper addresses the classical problem of optimal location and sizing of distributed generators (DGs) in radial distribution networks by presenting a mixed-integer nonlinear programming (MINLP) model. To solve such model, we employ the General Algebraic Modeling System (GAMS) in conjunction with the BONMIN solver, presenting its characteristics in a tutorial style. To operate all the DGs, we assume they are dispatched with a unity power factor. Test systems with 33 and 69 buses are employed to validate the proposed solution methodology by comparing its results with multiple approaches previously reported in the specialized literature. A 27-node test system is also used for locating photovoltaic (PV) sources considering the power capacity of the Caribbean region in Colombia during a typical sunny day. Numerical results confirm the efficiency and accuracy of the MINLP model and its solution is validated through the GAMS package.

Purpose. Shunt capacitors are installed in power system for compensating reactive power. Therefore, feeder capacity releases, voltage profile improves and power loss reduces. However, determination optimal location and size of capacitors in distribution systems is a complex optimization problem. In order to determine the optimum size and location of the capacitor, an objective function which is generally defined based on capacitor installation costs and power losses should be minimized According to operational limitations. This paper offers a newly developed metaheuristic technique, named dice game optimizer to determine optimal size and location of capacitors in a distribution network. Dice game optimizer is a game based optimization technique that is based on the rules of the dice game.

In this paper, improved single- and multi-objective Harris Hawks Optimization algorithms, called IHHO and MOIHHO, respectively are proposed and applied for determining the optimal placement of distribution generation (DG) in the radial distribution systems. Harris Hawks optimizer (HHO) is a new inspired meta-heuristic optimization technique that is mainly based on the intelligence behavior of the Harris hawks in chasing prey. The IHHO and MOIHHO are applied for determining the optimal size and location of DG at different operating power factors (p.f) with the aim of minimizing the total active power loss, reducing the voltage deviation (VD), and increasing the voltage stability index (VSI) considering the operational constraints of distribution system. In IHHO, the performance of the conventional HHO algorithm is improved using the rabbit location instead of the random location. In MOIHHO, grey relation analysis is applied for identifying the best compromise solution among the non-dominance Pareto solutions. To verify the effectiveness of the proposed algorithms, IEEE 33-bus and IEEE 69-bus radial distribution systems are used, and the obtained results are compared with those obtained by other optimization techniques. The results prove the efficiency of the proposed algorithms in terms of best solutions obtained so far for the single- and multi-objective scenarios.

This article presents a two-layer optimization scheme for simultaneous optimal allocation of wind turbines (WTs) and battery energy storage systems (BESSs) in power distribution networks. The prime objective of this formulation is to maximize the renewable hosting capacity of the system. For outer-layer, a new objective function is developed by combining multiple objectives such as annual energy loss in feeders, back-feed power, BESSs conversion losses, node voltage deviation, and demand fluctuations caused by renewables subject to various system security and reliability constraints. Furthermore, a modified variant of African buffalo optimization (ABO) introduced to overcome some of the limitations observed in its standard variant. The proposed modifications are first validated and then introduced for simultaneous optimal integration of multiple distributed energy resources in distribution systems. The proposed modified ABO is employed to determine the optimization variables of outer-layer. Whereas, a heuristic is proposed to solve the inner-layer optimization problem aiming to determine the optimal dispatch of BESSs suggested by outer-layer optimization. By considering the high investment and operating cost of BESSs, minimum energy storage capacity has been ensured during the planning stage. To present the efficacy of developed model, it is implemented on a 33-bus, benchmark test distribution system for various test cases. The comparative simulation results show that the proposed optimization model and modified ABO is very promising to improve the performance of active distribution systems.

The impressive advantages offered by of demand-side participation have accelerated deployment of demand response (DR) programs. However, the first step to attain the benefits of DR programs is to increase awareness level of the customers. This paper proposes a simple-but-efficient platform to enlighten the costumers on manifested merits of demand-side load control. The proposed platform is a web-based application which acquires the load profile of the customer, associated flexible appliances, and the customer preferences for using the appliances. In turn, presents the optimal operation schedule for flexible appliances and attained benefits from using the optimal schedule. To calculate the optimal operation schedule, a mixed-integer linear optimization model is devised where the decision variables are settings of flexible appliances, charge/discharge status and amount of storage device, charge/discharge status, and amount of electrical vehicle. The devised optimization engine is linked to a database to acquire required data for optimization which encompasses historical data for customer load, forecasts of renewables, ratings of customers' flexible appliances, and subjected energy tariff. The attained optimal scheduling for the customer is then returned to the database. On the other hand, the database is linked to the web-based user interface to get the user preferences (write to the database) and represent the recommendation for optimal operation and attained benefits (read from database). To manage the links between web-based user interface, database, and optimization tool, proper linking application programming interfaces (APIs) are devised. The proposed platform is testified using real-world data and its effectiveness is assured by experimental studies.

Utilization of an ultracapacitor (UC)-based energy storage device can provide one of the most efficient solutions for short-term operational challenges in grid-connected photovoltaic (PV) systems. This paper proposes an algorithm for coordinated control of PV and ultracapacitor-based energy storage system to minimize the effects of sudden changes in solar irradiance and the presence of low voltages at the point of common coupling. In addition, this work proposes an improved multi-mode operational scheme for control of an ultracapacitor-based energy storage system that takes into consideration various associated limits during charging and discharging modes. The effectiveness of the proposed algorithm in mitigating the impacts of low voltages and short term changes in irradiance is demonstrated using simulation analysis carried out on the modified Consortium for Electric Reliability Technology Solutions (CERTS) microgrid testbed.

The integration of Distributed Energy Resources (DERs) into distribution systems greatly increases the system complexity and introduces two-way power flow. Conventional protection schemes are based upon local measurements and simple linear system models, and are thus not capable of handling the new complexity and power flow patterns in systems with high DER penetration. In this paper, we propose a data-driven protection framework to address the challenges introduced by DERs. Firstly, considering the limited available data under fault conditions, we adopt the Support Vector Data Description (SVDD) method, a commonly used one-class classifier, for distribution system fault detection, which only requires the normal data for its training process. Secondly, incremental learning is incorporated into the proposed SVDD-based protection framework to accommodate variations of the integration level of DERs in distribution systems over time. In particular, the artificial uniform-hyperspherical data generation model is incorporated into the incremental SVDD to boost the training speed. Finally, we validate the proposed method under the IEEE 123-node test feeder. Simulation results demonstrate that our proposed SVDD-based fault detection framework significantly improves the robustness and resilience against DERs in comparison with conventional protection systems. Meanwhile, the proposed online updating model outperforms the existing incremental SVDD models in terms of successful training speed.

Integration of Information and Communications Technology (ICT) into the distribution system makes today's power grid more remotely monitored and controlled than it has been. The fast increasing connectivity, however, also implies that the distribution grid today, or smart grid, is more vulnerable. Thus, research into intrusion/anomaly detection systems at the distribution level is in critical need. Current research on Intrusion Detection Systems for the power grid has been focused primarily on cyber security at the Supervisory Control And Data Acquisition, and single node levels with little attention on coordinated cyberattacks at multiple nodes. A holistic approach toward system-wide cyber security for distribution systems is yet to be developed. This paper presents a novel approach toward intrusion prevention, using a multi-agent system, at the distribution system level. Simulations of the method have been performed on the IEEE 13-Node Test Feeder, and the results compared to those obtained from existing methods. The results have validated the performance of the proposed method for protection against cyber intrusions at the distribution system level.

Increasing electric vehicle (EV) charging demand and residential solar photovoltaic (PV) generation greatly alter traditional distribution system operation and have the potential to overload and otherwise threaten the operating life of legacy infrastructure. The rate and location of adoption of these technologies on residential distribution systems introduce operational uncertainties for which traditional utilities may not be prepared. This paper proposes a user-defined, data-driven risk assessment method to quantify the severity and likelihood of transformer and secondary conductor overload conditions posed by high levels of EV charging demand coupled with rooftop solar PV. The stochasticity inherent in the operation of a distribution secondary system is captured by employing data-driven probability distribution functions for residential loading, EV charging, rooftop solar generation and ambient temperature. Samples then are repeatedly drawn from each function as inputs to a Monte-Carlo, multi-period power flow analysis to calculate secondary line currents, total loading and accelerated transformer aging. The proposed approach is utilized to study transformer and secondary conductor overload risk as well as transformer loss-of-life for multiple EV and PV penetration scenarios using historical EV charging profiles and residential Salt Lake City load profile data for a peak summer load day.

Incorporating photovoltaic (PV) inverters in power distribution systems via static synchronous compensators (PV-STATCOM) during the nighttime has lately been described as a solution to improve network performance. Hunter prey optimization (HPO) is introduced in this study for efficient PV-STATCOM device allocation in distribution systems. HPO generates numerous scenarios for how animals could act when hunting, some of which have been expanded into stochastic optimization. The PV-STATCOM device allocation issue in distribution networks is structured to simultaneously minimize the electrical energy losses and improve the voltage profile while accounting for variable 24 h loadings. The impacts of varying the number of installed PV-STATCOM devices are investigated in distribution systems. It is tested on two IEEE 33-node and 69-node distribution networks. The effectiveness of the proposed HPO is demonstrated in comparison to the differential evolution (DE) algorithm, particle swarm optimization (PSO), artificial rabbits algorithm (ARA), and golden search optimizer (GSO). The simulation results demonstrate the efficiency of the proposed HPO in adequately allocating the PV-STATCOM devices in distribution systems. For the IEEE 33-node distribution network, the energy losses are considerably decreased by 57.77%, and the voltages variance sum is significantly reduced by 42.84%. The energy losses in the IEEE 69-node distribution network decreased by 57.89%, while voltage variations are reduced by 44.69%. Additionally, the suggested HPO is highly consistent than the DE, PSO, ARA, and GSO. Furthermore, throughout the day, the voltage profile at all distribution nodes surpasses the minimum requirement of 95%.

The electric power calculator (EPC) software, for the simulation and analysis of electrical networks at the industrial frequency, is described in this article, including its source code and operational diagram. Compared to well-known commercial software, EPC directly provides the values of the voltages, currents, and powers in each subsystem. The voltages and currents are calculated, in phasor notation, applying Kirchhoff’s laws, while the powers are obtained through the components of the apparent power vector. In this paper, the EPC software could be applied to three-phase networks made up of a distribution transformer and two three-phase wye-configured loads. The correctness of the results of the EPC software was verified by comparison with those obtained using the Multisim commercial software and with the measurements recorded by the Fluke 435 analyzer in a real distribution network of residential buildings. The values of the voltages and currents provided by the EPC were the same as those measured by the Fluke, and the differences observed using Multisim were less than 0.6%. Consequently, the EPC software is an excellent complement for power analyzers, because it is capable of providing measurements in all the network subsystems at the same time, using a single analyzer.

A gradient-based optimizer (GBO) is a recently inspired meta-heuristic technique centered on Newton’s gradient-based approach. In this paper, an advanced developed version of the GBO is merged with a crossover operator (GBOC) to enhance the diversity of the created solutions. The merged crossover operator causes the solutions in the next generation to be more random. The proposed GBOC maintains the original Gradient Search Rule (GSR) and Local Escaping Operator (LEO). The GSR directs the search to potential areas and aids in its convergence to the optimal answer, while the LEO aids the searching process in avoiding local optima. The proposed GBOC technique is employed to optimally place and size the distribution static VAR compensator (D-SVC), one of the distribution flexible AC transmission devices (D-FACTS). It is developed to maximize the yearly energy savings via power losses concerning simultaneously different levels of the peak, average, and light loadings. Its relevance is tested on three distribution systems of IEEE 33, 69, and 118 nodes. Based on the proposed GBOC, the outputs of the D-SVCs are optimally varying with the loading level. Furthermore, their installed ratings are handled as an additional constraint relating to two compensation levels of 50% and 75% of the total reactive power load to reflect a financial installation limit. The simulation applications of the proposed GBOC declare great economic savings in yearly energy losses for the three distribution systems with increasing compensation levels and iterations compared to the initial case. In addition, the effectiveness of the proposed GBOC is demonstrated compared to several techniques, such as the original GBO, the salp swarm algorithm, the dwarf mongoose algorithm, differential evolution, and honey badger optimization.

Non-Technical Losses (NTL) represent a serious concern for electric companies. These losses are responsible for revenue losses, as well as reduced system reliability. Part of the revenue loss is charged to legal consumers, thus, causing social imbalance. NTL methods have been developed in order to reduce the impact in physical distribution systems and legal consumers. These methods can be classified as hardware-based and non-hardware-based. Hardware-based methods need an entirely new system infrastructure to be implemented, resulting in high investment and increased cost for energy companies, thus hampering implementation in poorer nations. With this in mind, this paper performs a review of non-hardware-based NTL detection methods. These methods use distribution systems and consumers’ data to detect abnormal energy consumption. They can be classified as network-based, which use network technical parameters to search for energy losses, data-based methods, which use data science and machine learning, and hybrid methods, which combine both. This paper focuses on reviewing non-hardware-based NTL detection methods, presenting a NTL detection methods overview and a literature search and analysis.

The ever-growing participation of Renewable Energy Sources (RES) in modern distribution networks is replacing an important portion of Conventional Generation (CG), which brings along new challenges in the planning and operation of distribution grids. As RES such as Photovoltaic Energy (PV) and Wind Power Generation (WPG) increase in distribution networks, studies regarding their integration and coordination become more important. In this context, the purpose of this paper is to propose a Multi-period Optimal Power Flow (MOPF) model for the optimal coordination of Battery Energy Storage Systems (BESSs) with PV, WPG, and CG in modern distribution networks. The model formulation was developed in A Modeling Language for Mathematical Programming (AMPL) and solved through the Knitro solver within a time horizon of 24 h. A distinctive feature and one of the main contributions of the proposed approach is the fact that BESSs can provide both active and reactive power. The proposed optimization model reduces power losses and improves voltage profiles. To show the applicability and effectiveness of the proposed model, several tests were carried out on the 33-bus distribution test system and a real distribution system of 141 buses located in the metropolitan area of Caracas. Power loss reductions of up to 58.4% and 77% for the test systems of 33 and 141 buses were obtained, respectively, when BESSs provided both active and reactive power. The results allow us to conclude that the proposed model for optimal coordination of BESSs with RES is suitable for real-life applications, resulting in important reductions of power losses and flattening of voltage profiles.