Renewable-based Distributed Generation Research Articles

Optimal Allocation of Capacitor Banks and Distributed Generation: A Comparison of Recently Developed Metaheuristic Optimization Techniques on the Real Distribution Networks of ALG-AB-Hassi Sida, Algeria

Recent advancements in renewable energy technologies, alongside changes in utility infrastructure and progressive government policies, have bolstered the integration of renewable-based distributed generation units within distribution systems. This paper introduces the Energy Valley Optimizer, a novel tool designed for the strategic placement of distributed generation units and capacitor banks. This placement is crucial not only for optimizing energy loss and enhancing bus voltage stability but also for promoting sustainable energy use and reducing environmental impact over the long term. By minimizing energy loss and voltage fluctuations, the optimizer contributes to a more sustainable and resilient energy system. It achieves this through the optimal allocation of resources across various load patterns within a 24 h period and is tested on the ALG-AB-Hassi-Sida 157-bus distribution network in South Algeria. Comparative analysis with existing algorithms—such as the Liver Cancer Algorithm, Walrus Optimization Algorithm, and Zebra Optimization Algorithm—demonstrates the superior performance of the Energy Valley Optimizer. It not only enhances technical and economic efficiencies but also significantly lowers the total cost of energy over 24 years, thus supporting sustainable development goals in energy management.

Smart home energy management using demand response with uncertainty analysis of electric vehicle in the presence of renewable energy sources

In this article, smart home energy management is proposed using real-time pricing (RTP) based demand response for effective utilization of renewable-based distributed generation (DGs), battery, and electric vehicle (EV) to reduce grid dependency. The smart home consists of eighteen smart appliances and EV. The EV can be operated either in vehicle-to-home or home-to-vehicle. The smart appliances in the smart home are categorized into two types: electrical and thermal loads. Each smart appliance’s operating time slots and durations are scheduled based on the tariff, availability of DGs, and storage devices using the Binary Particle Swarm Optimization (BPSO) algorithm without affecting user preferences. Due to the proposed method, the grid dependency of the smart home in each time slot is reduced, which shall reduce the net electricity cost. Different cases are analyzed based on the various combinations of renewable-based DGs such as wind power, solar photovoltaic (PV), and battery in the smart home to showcase the effectiveness of the proposed method. The performance of the proposed system is analyzed under uncertain EV and DG operations. A detailed comparison is made with and without appliance scheduling in the proposed smart home to showcase the necessity of appliance scheduling and reduce the grid dependency. The simulation results show the profit of ₹13.046 and the grid dependency reduced by 9.636 kW in case 5 with appliance scheduling compared to case 5 without appliance scheduling. Therefore, the electricity cost and grid dependency are reduced using the BPSO algorithm for appliance scheduling in the smart home under normal and uncertain conditions.

Decentralized utilization of distributed energy storage resources for simultaneous frequency regulation in a microgrid

A microgrid has emerged for the transformation from centralized into localized electricity generation, which increases the opportunity for renewable-based distributed generators (DGs) applications such as photovoltaic (PV) and wind as well as small-scale conventional generation units. PV and wind integration provides environmental, technical, and economic benefits. However, the low inertia of the PV system and decoupling due to the presence of the power electronics converter in the wind generation system led to a significant declination in the microgrid's inertia, which causes an excessive frequency nadir and a high rate of change of frequency (ROCOF) in the presence of disturbances. The microgrid's inertia can be enhanced by storing or releasing energy within the microgrid. The wind generation system has intrinsic kinetic energy stored in the rotating parts, while the PV system's sorted energy is improved by introducing an external supercapacitor on the DC link. The aforementioned energy storage elements provide limited energy resources, which should be employed in a proper manner. This article proposes a control scheme to boost the cooperation of wind and PV generation systems for the enhancement of inertia and frequency support. Therefore, the proposed scheme prevents excessive stress on individual generators and improves the utilization of available energy resources. For example, at load increase by 0.5 MW, with frequency support using PV only or wind only, the frequency deviation is enhanced by 44.4 % and 29.5 %, respectively. While for both PV and wind support the frequency simultaneously, the frequency deviation is enhanced by 53.3 %. In addition, with the cooperation of PV and wind, the required energy from PV and wind is reduced by 29.5 % and 13.7 % respectively and the rate of change of frequency is enhanced by 61.9 %. The energy share among PV and wind generation systems is investigated and verified by simulations; tuning formulas are also developed to attain predefined energy support with equal energy sharing among different resources. For instance, with a non-equal energy share strategy and load increases by 0.5 MW, the wind delivers almost 1.6 times the energy delivered from the PV. Hence, more stress occurs on the wind generation compared to PV. While, using equal energy share strategy, the required energy from wind is about 1.03 times the energy delivered from the PV. The proposed control scheme is applied to an AC microgrid that comprises a thermal generation plant, a PV generation system, and a doubly-feed induction generator-based wind generation system. To verify the effectiveness of the proposed scheme, different simulation cases considering load change, wind speed variation, irradiance variations, and weak grid integration are implemented. When compared to individual frequency support, the proposed scheme provides a superior response.

A Convex Micro-Grid-Based Optimization Model for Planning of Resilient and Sustainable Distribution Systems Considering Feeders Routing and Siting/Sizing of Substations and DG Units

Integration of distributed generation (DG) and renewable energy resources (DERs) has affected planning and operation of recent distribution networks (DNs). On the other side, resiliency and sustainability of DNs is the other concern of distribution network operators. In this paper, a new formulation is developed for optimal planning of resilient DNs based on optimal formation of resilient micro-grids (MGs). The formulation aims to implement optimal siting and sizing of conventional and renewable-based DGs, optimal routing and type of feeders, as well as optimal sizing and placement of HV/MV substations to configure adequate and resilient MGs against extreme events. The investment, operational, emissions, and resiliency costs have been included in the objective function, and all the problem constraints as well as uncertainty of renewable DGs have been taken into account. The new formulation uses line-flow-based (LFB) model of AC power flow equations, and reserve feeders (tie-lines) are employed to enhance resiliency after severe outages via reconfiguration concept. All the relations have been convexfied in order to compose a mixed-integer quadratically-constrained (MIQCP) model to be solved with global optimum solvers in GAMS. Efficiency of the conducted methodology has been evaluated by different experiments on the 24-node system, and the results are investigated.

DETECTION OF ELECTRICITY THEFT CYBER-ATTACKS IN RENEWABLE DISTRIBUTED GENERATION FOR FUTURE IOT-BASED SMART ELECTRIC METERS

Electricity theft represents a pressing problem that has brought enormous financial losses to electric utility companies worldwide. In the United States alone, $6 billion worth of electricity is stolen annually. Traditionally, electricity theft is committed in the consumption domain via physical attacks that includes line tapping or meter tampering. The smart grid paradigm opens the door to new forms of electricity theft attacks. First, electricity theft can be committed in a cyber manner. With the advanced metering infrastructure (AMI), smart meters are installed at the customers’ premises and regularly report the customers’ consumption for monitoring and billing purposes. In this context, malicious customers can launch cyber-attacks on the smart meters to manipulate the readings in a way that reduces their electricity bill. Second, the smart grid paradigm enables customers to install renewable-based distributed generation (DG) units at their premises to generate energy and sell it back to the grid operator and hence make a profit.
 Therefore, this project evaluating performance of various deep learning algorithms such as deep feed forward neural network (DNN), recurrent neural network with gated recurrent unit (RNN-GRU) and convolutional neural network (CNN) for electricity cyber-attack detection. Now-a-days in advance countries solar plates are used to generate electricity and these users can sale excess energy to other needy users and they will be maintained two different meters which will record consumption and production details. While producing some malicious users may tamper smart meter to get more bill which can be collected from electricity renewable distributed energy. This attack may cause huge losses to agencies. To detect such attack, this project is employing deep learning models which can detect all possible alterations to predict theft.

State-of-the-art review on energy management and control of networked microgrids

As the United Nations plans to “ensure access to affordable, reliable, sustainable and modern energy for all,” great attention is paid to deploying sustainable networked microgrids to fulfill the future energy demand. Several neighboring low-voltage microgrids in a fixed or dynamic electric boundary will form a Multi-Microgrid. The unprecedented deployment of microgrids, brilliant communication, and infrastructure enable the concept of multi-microgrids to be an operational exemplar in future distribution networks, and it paves the way to deliver critical loads against all odds. This paper provides a state-of-the-art review of the evolution of networked microgrids with deep insight into the most critical research areas, opportunities, and challenges in energy management and control. Their operation and control in coordination with medium voltage distribution networks in time-varying loads and intermittent characteristics of renewable-based distributed generation sources are challenging for both distribution network and microgrid operators. Further, the complexities involved in the multiple control layers in the multi-microgrid network need appropriate strategies for optimal sharing and trading among neighboring microgrids. Numerous solutions based on advanced distribution control, reinforcement learning, adaptive deep neural networks, and game theory were reported in the literature. A systematic review of various energy management strategies, optimization scheduling frameworks, and multi-microgrid voltage and frequency control strategies are presented. Then, a detailed survey on hierarchical, decentralized, and distributed control architectures is reported. Finally, a comprehensive overview of research objectives and more than 30 solution methodologies with respect to both energy management and control is illustrated.

Planning of EV Charging Station With Distribution Network Expansion Considering Traffic Congestion and Uncertainties

Proper planning for electric vehicle (EV) charging stations along with the required expansion of the distribution network is essential for the continuous growth of EV and conventional loads. This paper proposes a realistic and sustainable solution for deciding the location and capacity of the EV fast-charging stations along with the optimal multistage expansion of the distribution network to cope with future load growth. An overlaid traffic network with a distribution network is taken for the realistic approach. EV user convenience factor and user happiness factor are proposed along with the distribution network energy loss for better planning of fast-charging stations. To get charged, the appropriate fast-charging station and its best route are decided through minimum energy consumption by the EVs, considering traffic congestion and distances. Up-gradation of lines, up-gradation of the substation, addition of new fast-charging station, allocation of DSTATCOM and allocation of renewable-based distributed generation are performed for distribution network expansion planning. The whole work is formulated in three layers with a sub-layer. Uncertainties of EV driving patterns, energy requirement, traffic congestion, load and renewable generation are undertaken by the 2m point estimation method. The solution is carried out along with validation using different metaheuristic methods and integer linear programming.

Optimal allocation of renewable DGs using artificial hummingbird algorithm under uncertainty conditions

Renewable distributed generators (RDGs) have been widely used in distribution networks for technological, economic, and environmental reasons. The main concern with renewable-based distributed generators, particularly photovoltaic and wind systems, is their intermittent nature, which causes output power to fluctuate, increasing power system uncertainty. As a result, it's critical to think about the resource's uncertainty when deciding where it should go in the grid. The main innovation of this paper is proposing an efficient and the most recent technique for optimal sizing and placement of the RDGs in radial distribution systems considering the uncertainties of the loading and RDGs output powers. Monte-Carlo simulation approach and backward reduction algorithm are used to generate 12 scenarios to model the uncertainties of loading and RDG output power. The artificial hummingbird algorithm (AHA), which is considered the most recent and efficient technique, is used to determine the RDG ratings and placements for a multi-objective function that includes minimizing expected total cost, the expected total emissions, and the expected total voltage deviation, as well as improving expected total voltage stability with considering the uncertainties of loading and RDGs output powers. The proposed technique is tested using an IEEE 33-bus network and an actual distribution system in Portugal (94-bus network). Simulations show that the suggested method effectively solves the problem of optimal DG allocation. In addition of that the expected costs, the emissions, the voltage deviation, are reduced considerably and the voltage stability is also enhanced with inclusion of RDGs in the tested systems.

An Interlinking Converter for Renewable Energy Integration into Hybrid Grids

With the rise in global warming, distributed generators (DGs) based on renewable energy sources have become increasingly important in the power sector. Due to their small size and the capacity to 'island' while providing most of their loads during situations, microgrids provide an excellent framework to build smart grids, which increases system resilience. Given that renewable-based DGs dominate microgrids and are defined by their statistical properties and unpredictable power, sustaining load-generation balance is a significant challenge. Although microgrids are now widely used and researched, there is still considerable contention over whether they should use alternating current (AC) or direct current (DC), with the majority opinion leaning toward a combination of the two. nIn order to facilitate the flexible incorporation of renewable energy into hybrid grids, this article offers an interlinking converter design. The suggested converter may be configured as a DC-DC converter, a DC-AC inverter, or a DC-DC/AC multiport converter thanks to its one AC port and two DC ports, making it a versatile option for combining multiple DC and AC sources. Flexible conversion, high power density, low leakage currents, and tunable power flow are all features validated by the MATLAB/SIMULINK simulation results, proving the idea.

A Search Group Algorithm for Optimal Distributed Generation Placement

The integration of renewable-based distributed generation (DG) in distribution networks is increasingly common worldwide due to fossil fuel depletion, environmental pollution, and global warming. This paper proposes a Search Group Algorithm (SGA) method to determine the optimal location, capacity, and quantity of DGs in distribution networks. The SGA method has a good ability to find optimal solutions thanks to the ability to balance between the global search and local search in the optimization process. The objective of the problem is to minimize the power loss of the distribution networks while meeting the constraints of the networks, such as power balance, bus voltage limit, branch current limit, DG power limit, and DG penetration limit. The proposed method is applied to the IEEE 33-node and 69-node distribution networks with two different cases. After integrating DGs into distribution networks, the power loss are reduced by 70.14% and 70.23% for the 33-node and 69-node distribution networks, respectively. The results obtained from the SGA method were compared with other methods in the literature. The simulation results have proved that the SGA method has good performance for the optimal DG placement problem in distribution networks.

Peer-to-Peer trading with Demand Response using proposed smart bidding strategy

The electricity demand is increasing rapidly among residential consumers due to the utilization of many smart appliances. The renewable-based Distributed Generation (DGs) and Electric Vehicles (EV) investment is also rising among residential consumers. Consumers with renewable-based DGs and smart appliances are considered prosumers in this work. The main objective of this work is to reduce the electricity cost of the smart home by scheduling the smart appliances with Demand Response (DR) using Binary Particle Swarm Optimization (BPSO) algorithm and Peer-to-Peer (P2P) trading using a smart bidding strategy. The smart home consists of two prosumers and two consumers with different Distributed Generation (DG) availability, battery, EV, and smart appliances (thermal and electrical loads). The smart appliances are scheduled based on Real-Time Pricing (RTP), DGs and storage devices availability. The available excess power in the prosumers after self-consumption is traded to the neighbouring consumers to reduce the grid dependency. The novelty of this article lies in the trading decision and trading cost determination for P2P trading, which is called the proposed smart bidding strategy in this work. The proposed smart P2P trading algorithm involves the double auction mechanism, where the bidding occurs between the prosumers and consumers based on the supply–demand ratio (SDR) and RTP. The trading cost calculated is beneficial for both prosumers and consumers in reducing their electricity costs. The electricity cost of consumer 1, consumer 2, prosumer 1, and prosumer 2 is reduced by ₹58.073, ₹20.37, ₹51.656, and ₹20.37, respectively, as compared with grid tariff under the category of normal condition. Similarly, the electricity cost of consumer 1, consumer 2, prosumer 1, and prosumer 2 is reduced by ₹63.514, ₹3.208, ₹98.155, and ₹34.049, respectively, as compared with grid tariff under the category of EV uncertainty in both consumer & prosumer premises. The simulation results proved that the proposed smart bidding strategy effectively reduced the electricity cost of the prosumers and consumers under normal and uncertain conditions compared to the grid tariff.

Impacts of Electric Vehicles on Distribution System Planning and Operation

Nowadays, the use of electric vehicles (EVs) is becoming more popular due to technological improvements. Along with their ancillary services to the grid as a storage element, the integration of EVs into distribution systems (DSs) will definitely have impacts on the DS security and power quality in both vehicle to grid (V2G) and grid to vehicle (G2V) modes of operation. Hence, DSs’ planners and operators should regulate the integration of EVs and address relevant challenges for a reliable and efficient system. This paper surveys recent literature on the impacts of EVs on DSs considering the presence of renewablebased distributed generation (Re-DG). The paper summarizes potential services provided by EVs and identifies the potential impacts.

Optimal day‐ahead scheduling in micro‐grid with renewable based DGs and smart charging station of EVs using an enhanced manta‐ray foraging optimisation

Micro-grids (MGs) are small parts of the electrical power system that work along with the electric system or autonomously based on environmental or economic conditions. The renewable-based distributed generators (RDGs) and electric vehicle charging stations (EVCSs) are wildly incorporated in MGs. Optimal day-ahead scheduling of the MG is ahead corner of energy management for cost reduction. In addition, solving the economic load dispatch and day-ahead scheduling of the MG is a complex optimisation problem, especially considering the RDGs, EVCS, and uncertainties in the electrical system. This paper aims to optimise the day-ahead scheduling of the MG with and without a smart charging strategy for electric vehicles. An enhanced manta-ray foraging optimisation (EMRFO) algorithm is proposed to solve this optimisation problem. EMRFO depends upon boosting population diversity and the searching ability of the standard MRFO using strategies. The proposed strategies are based on quasi-oppositional-based learning and local chaotic mutation. The studied MG consists of wind turbines, fuel cells, and diesel generators. The day-ahead scheduling of the MG is solved with and without considering the uncertainties of the load demand and the wind speed. The proposed algorithm for day-ahead scheduling of the MG is compared to well-known algorithms such as anti lion optimisation, particle swarm optimisation, whale optimisation algorithm, sine cosine algorithm, and harmony search algorithm. The simulation results demonstrate that the proposed algorithm is superior to these algorithms for solving the optimisation problem. The results show that the generation cost is reduced considerably from 77,745.61 $ to 76,984.2 $ by applying the smart operation strategy.

Dynamic Stabilization of DC Microgrids Using ANN-Based Model Predictive Control

Over the past decade, the high penetration of renewable-based distributed generation (DG) units has witnessed a considerable rise in electrical networks. In this context, direct current (DC) microgrids based on DGs are being preferred due to having less complexity for the establishment and control. At the same time, they offer higher efficiency and reliability compared to their alternating current (AC) counterparts. This paper proposes a new model predictive control (MPC)-trained artificial neural network (ANN) control strategy being an ANN-MPC instead of conventional cascaded-proportional-integral (PI)-trained ANN control for dynamic damping of photovoltaic (PV)-battery-based grid-connected DC microgrids. Unlike traditional controllers, the proposed control approach more rapidly attains generation-load power balancing under variable climate input (meteorological sensor data) and output (load demand), hence achieving quick DC-bus voltage damping. The proposed ANN-MPC scheme is examined under different operating conditions, and the results are compared with the ANN-based conventional PI controller. The results show the proposed control strategy's efficacy to lessen the instability issues and achieve effective attenuation of oscillations in DC microgrids.

Optimal multi-objective long-term sizing of distributed energy resources and hourly power scheduling in a grid-tied microgrid

The multi-objective long-term optimum sizing of renewable-based distributed generators as well as battery energy storage system is accomplished in the present work using grey wolf optimizer in a fuzzy formulation. The objectives considered for sizing optimization are the minimization of the total annual expenses, yearly emissions, and yearly energy loss, as well as the maximization of the annualized monetary benefit incurred due to the deferral of network upgrades. The optimal integration of distributed energy resources is intended to effectuate the deferral of network upgrades, leading to an appreciable financial benefit for the utility. An optimum hourly power schedule influenced by a selective price-based demand response strategy is acquired for the microgrid, considering the seasonal variations in the generated renewable power and demand. The profitability of the microgrid prosumer is augmented through the demand response program, which boosts renewable power utilization. The hourly probabilistic modelling of the uncertainties in renewable power and consumer demand is implemented. An optimal, multi-objective, integrated sizing and scheduling model for distributed energy resources is formulated in the present work, which targets the economic and sustainable operation of the microgrid in a multi-benefit domain.

Energy Management Strategy of a Reconfigurable Grid-Tied Hybrid AC/DC Microgrid for Commercial Building Applications

This paper proposes an energy management strategy (EMS) of a reconfigurable grid-tied hybrid ac/dc microgrid (HMG) architecture for commercial building (CB) applications. This HMG architecture consists of a multi-mode configuration (MMC) with renewable-based distributed generators (DGs), energy storages (ESs), and genset. The main advantage of this architecture is its capability to reconfigure its structure based on the predicted building load power (BLP) and renewable-based DGs power. However, minimizing the building electricity cost (BEC) and maximizing the reliability index (RI) of a reconfigurable grid-tied HMG architecture is a challenging task due to the stochastic behaviors of BLP and renewable-based DGs power. In this context, the BLP and renewable-based DGs behaviors are modeled using an artificial neural network approach to predict future time slot values. Then, system level models are developed for HMG energy sources and grid-tied converters/switches. Furthermore, BEC and RI models are developed based on the dynamic pricing of grid/HMG electricity cost and supplied energy. Then, an EMS is proposed for the developed reconfigurable grid-tied HMG architecture, which consists of a two-stage control strategy, i.e., stage I and stage II. Stage I control strategy minimizes BEC and maximizes RI using multi-objective particle swarm optimization (MOPSO) and MMC. Stage II control strategy generates control signals for HMG energy sources, converters, and grid-tied converters/switches based on stage I reference signals. Historical data is used to demonstrate the effectiveness of the proposed EMS for a reconfigurable grid-tied HMG architecture. Through numerical simulation studies, it is shown that the proposed EMS is capable of reducing BEC and increasing RI by concurrently enabling MMC of a reconfigurable grid-tied HMG architecture.

Effect of Battery Degradation on the Probabilistic Optimal Operation of Renewable-Based Microgrids

In order to maximize the use of renewable-based distributed generators (DGs), in addition to dealing with the effects of the inherent power management uncertainties of microgrids (MGs), applying storage devices is essential in the electrical system. The main goal of this paper is to minimize the total operation cost as well as the emissions of MG energy resources, alongside the better utilization of renewable energy sources (RES) and energy storage systems. The uncertainties of wind speed, solar irradiation, market price and electrical load demand are modeled using reduced unscented transformation (RUT) method. Simulation results reveal that, as expected, by increasing the battery efficiency, the achievable minimum daily operational cost of the system is reduced. For example, with 93% battery efficiency, the operational cost equals EUR 9200, while for an efficiency of 97%, the achievable minimum daily operational cost is EUR 8900. Moreover, the proper economic/environmental performance of the suggested approach, which contributes to the possibility of selecting a compromise solution for the MG operator in accordance with technical and economic constraints, is justified.

Technoeconomic and Environmental Study of Multi-Objective Integration of PV/Wind-Based DGs Considering Uncertainty of System

For technological, economic, and environmental reasons, renewable distributed generators (RDGs) have been extensively used in distribution networks. This paper presents an effective approach for technoeconomic analysis of optimal allocation of REDGs considering the uncertainties of the system. The primary issue with renewable-based distributed generators, especially wind and photovoltaic systems, is their intermittent characteristic that results in fluctuating output power and, hence, increasing power system uncertainty. Thus, it is essential to consider the uncertainty of such resources while selecting their optimal allocation within the grid. The main contribution of this study is to figure out the optimal size and location for RDGs in radial distribution systems while considering the uncertainty of load demand and RDG output power. A Monte Carlo simulation approach and a backward reduction algorithm were used to generate a reasonable number of scenarios to reflect the uncertainties of loading and RDG output power. Manta ray foraging optimization (MRFO), an efficient technique, was used to estimate the ratings and placements of the RDGs for a multi-objective function that includes the minimization of the expected total cost, total emissions, and total system voltage deviation, in addition to enhancing predicted total voltage stability. An IEEE 118-bus network was used as a large interconnected network, along with a rural 51-bus distribution grid and the IEEE 15-bus model as a small distribution network to test the developed technique. Simulations demonstrate that the proposed optimization technique effectively addresses the optimal DG allocation problem. Furthermore, the results indicate that using the proposed method to optimally integrate wind turbines with solar-based DG decreases the expected costs, emissions, and voltage deviations while improving voltage stability by 40.27%, 62.6%, 29.33%, and 4.76%, respectively, for the IEEE 118-bus system and enhances the same parameters by 35.57%, 59.92%, 68.95%, and 11.88%, respectively, for the rural 51-bus system and by 37.74%, 61.46%, 58.39%, and 8.86%, respectively, for the 15-bus system.

Performance assessment of hybrid active filtering technique to enhance the hosting capacity of distorted grids for renewable energy systems

Increased green energy presence in distribution power networks can resolve rising energy demands while also reducing fossil fuel consumption, as well as providing economic and technical merits. However, the quantity of power delivered to customers cannot be allowed to take precedence over the quality of that electricity. This is because the high penetration of the renewable-based power generation comes at the cost of power quality (PQ) deterioration, especially at the point of common coupling (PCC), owing to nonlinear characteristics of renewable-based distributed generation (DG) systems and pre-contamination of modern distribution grids. So, the function of PQ improvement technologies unavoidably comes into the picture when the hosting capacity (HC) of renewable energy systems is constrained by any of the grid PQ performance indices. This paper investigates the application of a hybrid active power filter (HAPF) to improve the harmonic-constrained HC (HC-HC) of renewable DG systems in harmonically polluted distribution systems. A shunt type HAPF designing to achieve maximum HC-HC is considered as an optimization problem and solved by the firefly algorithm under several constraints the system particularly related to individual and total harmonic distortion limits in line current and voltage at PCC, harmonic currents overloading of distribution feeder. Aside from harmonics, constraints related to voltage rise and drop limits, as well as power factor, are also taken into account to ensure the optimal use of the HAPF as a compensator. The proposed HAPF-based HC-HC improvement approach's findings and efficacy are compared to those acquired from one of the literature's recently proposed techniques. Besides, the study is extended for HC-HC enhancement by HAPF, under changing the utility-side's background voltage distortion and the load-side's nonlinearity level conditions. The performed extended investigation of the system shows that HC-HC enhancing the effectiveness of the proposed approach is rather less susceptible to utility or customer side contribution to background distortion at the PCC. Highlights Hybrid harmonic filtering is investigated for enhancing the harmonic-constrained hosting capacity of distributed generation in distorted grid conditions. A hybrid filter has been found as a better alternative to achieve an advanced level of renewable energy penetration in distorted distribution grids. Along with increasing the penetration level of distributed generation the harmonic distortion is also substantially reduced instead of being at the verge of the standard limit. With the increase in background voltage distortion, the percentage enhancement in HC-HC degrades. High load-side's nonlinearity also results in a relatively lower enhancement in HC-HC. Background nonlinearity negatively influences the percentage of HC enhancement more than the load-side's nonlinearity. Though the utility, as well as customer-side nonlinearity, affect the performance of the hybrid filter also yet the variation is significantly lesser than the purely passive filtering-based approach.

A novel approach to increase the share of renewable purchase obligation for planning of distribution network including grid scale energy storage

Abstract In recent times, producing electricity with lower carbon emissions has resulted in strong clean energy incorporation into the distribution network. The technical development of weather-driven renewable distributed generation units, the global approach to reducing pollution emissions, and the potential for independent power producers to engage in distribution network planning (DNP) based on the participation in the increasing share of renewable purchasing obligation (RPO) are some of the essential reasons for including renewable-based distributed generation (RBDG) as an expansion investment. The Grid-Scale Energy Storage System (GSESS) is proposed as a promising solution in the literature to boost the energy storage accompanied by RBDG and also to increase power generation. In this respect, the technological, economic, and environmental evaluation of the expansion of RBDG concerning the RPO is formulated in the objective function. Therefore, a novel approach to modeling the composite DNP problem in the regulated power system is proposed in this paper. The goal is to increase the allocation of PVDG, WTDG, and GSESS in DNP to improve the quicker retirement of the fossil fuel-based power plant to increase total profits for the distribution network operator (DNO), and improve the voltage deviation, reduce carbon emissions over a defined planning period. The increment in RPO and decrement in the power purchase agreement will help DNO to fulfill round-the-clock supply for all classes of consumers. A recently developed new metaheuristic transient search optimization (TSO) based on electrical storage elements’ stimulation behavior is implemented to find the optimal solution for multi-objective function. The balance between the exploration and exploitation capability makes the TSO suitable for the proposed power flow problem with PVDG, WTDG, and GSESS. For this research, the IEEE-33 and IEEE-69 low and medium bus distribution networks are considered under a defined load growth for planning duration with the distinct load demand models’ aggregation. The findings of the results after comparing with well-known optimization techniques DE and PSO confirm the feasibility of the method suggested.