Electric Grid System Research Articles

Water hammer issues in refurbishment of a high-head hydropower plant equipped with Francis turbine

Refurbishment and upgrading of ageing hydropower plants contribute to increase of renewable energy share in modern electrical grid systems. The potential increase of discharge and flexibility of load variation may result in much higher dynamic loads on both refurbished and non-refurbished plant components during transient operating events. First, water hammer control strategies are outlined including operational scenarios, surge control devices, redesign of the pipeline components, or limitation of operating conditions. Water hammer models and solutions are briefly discussed in the light of their capability, availability and uncertainty. The core of the paper is devoted to investigations of water hammer effects in a high-head hydropower plant Piva, Montenegro which is currently in the final phase of refurbishment. The flow-passage system of the Piva HPP is comprised of the intake structure, followed by three parallel penstocks each with Francis type water turbine at the downstream end. The outlet part starts with three parallel draft tubes that are connected to a common lower orifice-type surge tank followed by tailrace tunnel and outlet structure. Computed and measured results for a selected emergency shut-down (ESD) of one turbine form full-load are compared and discussed. Then the validated computational model is used for simulation of ESDs for a wide operating range.

A Fault Ride-Through Technique for PMSG wind turbines using Superconducting Magnetic Energy Storage (SMES) under Grid voltage sag conditions

Wind power penetration is growing, posing considerable technological challenges for developing electrical grid systems. Gearless permanent magnet synchronous generator (PMSG) wind energy conversion systems (WECS) are becoming more popular. On the flip side, they are susceptible to grid failures. The use of Superconducting Magnetic Energy Storage (SMES) to enhance fault ride-through in PMSG wind turbines is investigated. Per the current Grid code trends, WECS are not to be disconnected from the grid; rather, they should provide reactive power support during such situations. This work incorporates machine and grid side converters to manage reactive, active power and DC-link voltage during grid failures. To improves system performance, lessen voltage dips at the point of common coupling (PCC), provide reactive power support and reduce the transient length, a DC-link capacitor is used with SMES. SMES reserve energy capacity is necessary for FRT operation when the wind turbine's inertial response range is insufficient. Finally, a 1.5 MW PMSG-based WTG with SMES is developed. The Pre-fault, fault-period, and post-fault performance are all assessed. They confirm the system's efficiency, speed, and stability.

Analysis of animal-related electric outages using species distribution models and community science data

Animal-related outages (AROs) are a prevalent form of outages in electrical distribution systems. Animal-infrastructure interactions vary across species and regions, underlining the need to study the animal-outage relationship in more species and diverse systems. Animal activity has been an indicator of reliability in the electrical grid system by describing temporal patterns in AROs. However, these ARO models have been limited by a lack of available species activity data, instead approximating activity based on seasonal patterns and weather dependency in ARO records and characteristics of broad taxonomic groups, e.g. squirrels. We highlight available resources to fill the ecological data gap limiting joint analyses between ecology and energy sectors. Species distribution modeling (SDM), a common technique to model the distribution of a species across geographic space and time, paired with community science data, provided us with species-specific estimates of activity to analyze alongside spatio-temporal patterns of ARO severity. We use SDM estimates of activity for multiple outage-prone bird species to examine whether diverse animal activity patterns were important predictors of ARO severity by capturing existing variation within animal-outage relationships. Low dimensional representation and single patterns of bird activity were important predictors of ARO severity in Massachusetts. However, both patterns of summer migrants and overwintering species showed some degree of importance, indicating that multiple biological patterns could be considered in future models of grid reliability. Making the best available resources from quantitative ecology known to outside disciplines can allow for more interdisciplinary data analyses between ecological and non-ecological systems. This can result in further opportunities to examine and validate the relationships between animal activity and grid reliability in diverse systems.

Electrochemical Energy Storage and Conversion Systems – A Short Review

Electrochemical energy production systems – including fuel cells and electrolyzers – are vital technologies to address energy security and environmental demands. Also, their combination for improved performance is essential for future commercial applications. However, their real utilization (and integration with other alternative energy sources) goes beyond efficiency; large-scale penetration of renewable energy in the existing electrical grid systems is challenging due to destabilization possibility. Thus, electrochemical energy storage systems (e.g., electrochemical supercapacitors) are necessary for managing power generation intermittency and grid reliability. Therefore, this ultra-short review provides a brief overview of some of the most promising electrochemical devices for electrochemical energy production and storage for future systems in an engagement scenario.

Effects of guide vane shape on the performances of pump-turbine: A comparative study in energy storage and power generation

Pumped storage unit is a typical energy storage system in electric power grid system. Flow energy dissipation (FED) is a key factor affecting energy utilization efficiency. Guide vane has an important influence on the hydraulic performance and system stability. This study compares the FED difference of two typical guide vanes in turbine mode and pump mode based on Computational Fluid Dynamics (CFD) and entropy production method. The results show that at the same head, the flow rates in energy storage and power generation are different. In pump mode, FED concentrates in runner, guide vane, stay vane and volute. FED concentrates in runner and draft tube in turbine mode. The sources of FED in pump mode and turbine mode are different, including flow separation, jet wake, wall loss, vortex rope and back-flow. Guide vane with oval leading edge can better enhance the flow rate compared with that with sharp leading edge in turbine mode, which decreases FED at small flow rate condition by improving the matching of blade profiles and flow direction. This paper provides a reference for improving the performance of pump turbine through design.

Nuclear Power as an Option in Electrical Generation Planning for Small Economy and Electricity Grid

Implementing a NPP in countries with relatively small total GDP (small economy) and usually with small electricity grid face two major problems and constrains: the ability to obtain the considerable financial resources required on reasonable terms and to connect large NPP to small electricity grid. Nuclear generation financing in developing countries involves complex issues that need to be fully understood and dealt with by all the parties involved. Besides conventional approaches for financing power generation projects in developing countries, recently some alternative approaches for mobilizing financial resources are developed. The safe and economic operation of a nuclear power plant (NPP) requires the plant to be connected to an electrical grid system that has adequate capacity for exporting the power from the NPP, and for providing a reliable electrical supply to the NPP for safe start-up, operation and normal or emergency shut-down of the plant. Connection of any large new power plant to the electrical grid system in a country may require significant modification and strengthening of the grid system, but for NPPs there may be added requirements to the structure of the grid system and the way it is controlled and maintained to ensure adequate reliability. Paper shows the comparative assesment of NPP adn differrent base load technologies as an option in electrical generation planning for small economy and electricity grid.

Optimization and Design of a Sustainable Industrial Grid System

Electricity is a multifaceted form of energy and is used globally, with a continuously growing demand. Electrical power grids are there for more than 150 years. The generated electrical power is delivered to different industrial, commercial, and residential sectors, thereby fulfilling the ever-growing demand. In this research paper, the design and optimization of an industrial grid for various electrical loads is discussed. The electrical grid ensures a stable power supply to the loads by providing quality power with the minimum total harmonic distortion (THD) possible. A complete study of the short circuit current has been done in two different electrical grid systems, as it is seen that the short circuit current depends on the impedance of the transformer which feeds the load. These two designs of a single diagram will be simulated by using a power system analyzer, the Electrical Transient Analyzer Program (ETAP) software. The different electrical parameters, like choosing the optimised rated generator, cables, and transformers, are done. Load flow analysis is performed on both the design to evaluate the THD, short circuit fault, as well as to choose the right protection circuit for the system.

An in-Depth Study of How Zinc Metal Surface Morphology Determines Aqueous Zinc-Ion Battery Stability

In order to achieve the net-zero world initiative and combat the climate crisis, a global consensus of marching towards a sustainable energy structure has been built, where developing reliable, affordable, and sustainable energy storage devices, the medium of storing intermittent surplus electricity from clean and inexhaustible renewable energy sources, such as wind power and solar energy, and transferring to the smart electric grid system, is of great significance [1].Besides lithium-ion batteries (LIBs) and sodium-ion batteries (SIBs), the two dominant technologies having been developed substantially in the energy storage industry, researchers started pioneering studies on multivalent-ion systems of Ca [2, 3], Mg [4], Al [5, 6], and Zn [7-9] with competitive advantages, especially the ones as non-flammable economic substitutes, to ease manufacturing burden and enrich practical solutions for widespread application scenarios [10].Especially, zinc metal with benefits of aqueous compatibility, commensurate capacity (820 mAh/g), and crust abundance, a resurgence of rechargeable zinc-ion batteries (ZIBs) is happening. This battery system with water-based electrolyte chemistries is born with eye-catching benefits of safety and affordability; Zn/MnO2 with an improved energy density of 409 Wh/kg at 1.9 V is considered a promising candidate for grid-scale energy storage [11]. This revolutionary cheap and safe solution empowers the global energy structural transformation and enriches the public’s awareness of sustainable development.However, like most reactive metals, zinc exposed in the air naturally evolves a dense passivation layer of Zn5(CO3)2(OH)6 to discontinue the corrosion by oxygen and humidity, which, in batteries, can passivate the molecular dynamics at the interface between zinc and the electrolyte and demonstrate enormous electron transfer resistance due to the inferior conductivity [12]. Thus, wearing off this passivation layer is considered a facile approach to revitalize the frozen kinetics of zinc ions [13].Exposing fresh zinc to the electrolyte is also conductive of forming a functional solid-electrolyte interphase (SEI). Studies present that ZnF2-rich SEI plays a pivotal role in elongating the cycling life of zinc symmetric cells by effectively screening zinc from electrolyte solvents and reducing their sequence of side reactions [14].Additionally, a tactful change of zinc’s surface roughness before electrochemical operations should impact electron distribution, zinc nucleation and growth, and SEI formation. Especially, dendrites are often considered guilty of internal short-circuiting of batteries; similar to lithium, the far-end of zinc dendrites can become dead zinc, whose accumulation brings in issues of electrolyte depletion, anodic capacity loss, internal resistance growth, and cell polarization [15].In this work, a simple method was developed to change the surface of Zn anode to create more nucleation sites with lowered energy barriers (nucleation over-potentials), thus alleviating their dendrite growth. The cycling programs for zinc symmetric cells are standardized by fixing either the depth of cycling (DOC) or the areal current density in accordance with the constant energy or constant power supply in full batteries. In order to enunciate the battery degradation mechanism and shed light on the gas emission problems, we operate a careful electrochemical analysis cooperated with the differential electrochemical mass spectrometry (DEMS) technique. The preliminary data demonstrate an evident impact of initial zinc surface morphology on sequential zinc plating/stripping profiles and eventual lifespans at serial DOCs and current densities.

Comparing the economic performance of ice storage and batteries for buildings with on-site PV through model predictive control and optimal sizing

ABSTRACT Integrating renewable energy and energy storage systems provides a way of operating the electrical grid system more energy efficiently and stably. Thermal storage and batteries are the most common devices for integration. However, it is not clear which integrated storage system performs better in terms of overall economics. Ice storage has low initial and maintenance costs, but there is an efficiency penalty for charging of storage and it can only shift electrical loads associated with building cooling requirements. A battery's round-trip efficiency, on the contrary, is quite consistent and batteries can be used to shift both HVAC and non-HVAC loads. However, batteries have greater initial costs and a shorter life. This research presents a tool, using model predictive control and optimal sizing, and provides a case study for comparing life-cycle economics of battery and ice storage systems for commercial buildings that have chillers for cooling and an on-site photovoltaic system.

Binary-phase service battery energy storage system strategy for peak demand shaving and enhanced power quality

Owing to nonlinear and large consumer loads, power grid system operators encounter major challenges in matching the generated power supply to consumer power demand to smooth out the high-peak load demands and mitigate the voltage and current disturbances within the electrical grid. A battery energy storage system (BESS) is employed as a two-phase control technique to minimize the peak load demand of the system and enhance the power quality within the electrical grid system. The phase I control strategy presents a robust artificial neural network control, which is implemented considering the BESS state-of-charge (SoC) and operation time-of-day (ToD) of the customers’ loads to identify load peaks at each period of the day to effectively reduce the high-load demand. Moreover, in the phase II control strategy, a customized BESS-hybrid series-shunt active power filter (B-HSSAF) is presented to effectively mitigate the grid disturbances, which results in an enhanced grid system as suggested by the simulation results. The total harmonic distortion values after compensation using the B-HSSAF satisfy IEEE standard 519, and the power grid is enhanced. Furthermore, qualitative discussions of the optimal location of the BESS and dynamic thermal rating integration which are limitations of this paper are concisely presented with recommended further studies that can be taken into consideration.

Large Geomagnetically Induced Currents at Equator Caused by an Interplanetary Magnetic Cloud

AbstractHere, we report a rare observation of an extremely large and rapid change of geomagnetic field (dB/dt), a proxy for the geomagnetically induced currents (GICs), at the equator caused by a sudden drop in solar wind density at the front boundary of a magnetic cloud (MC) during the great 31 March 2001 storm. The horizontal component at the Indian equatorial station, Tirunelveli, recorded a sharp decline of ∼350 nT in just 5 min with a peak dB/dt exhibiting a concerning value of 136 nT/min, a possible GIC risk to the electric power systems. The responsible physical mechanisms were examined through magneto‐hydrodynamic model simulations and found that a prompt penetration of strong westward‐overshielding electric fields and ionospheric currents at the equator play a dominant role. This study provides some new insights into the extent of extreme geomagnetic field changes that can occur at the equatorial region due to solar wind density reduction at MC, which can have potential impacts on the electric power grid systems.

Exploratory Weather Data Analysis for Electricity Load Forecasting Using SVM and GRNN, Case Study in Bali, Indonesia

Accurate forecasting of electricity load is essential for electricity companies, primarily for planning electricity generators. Overestimated or underestimated forecasting value may lead to inefficiency of electricity generator or electricity deficiency in the electricity grid system. Parameters that may affect electricity demand are the weather conditions at the location of the electricity system. In this paper, we investigate possible weather parameters that affect electricity load. As a case study, we choose an area with an isolated electricity system, i.e., Bali Island, in Indonesia. We calculate correlations of various weather parameters with electricity load in Bali during the period 2018–2019. We use two machine learning models to design an electricity load forecasting system, i.e., the Generalized Regression Neural Network (GRNN) and Support Vector Machine (SVM), using features from various weather parameters. We design scenarios that add one-by-one weather parameters to investigate which weather parameters affect the electricity load. The results show that the weather parameter with the highest correlation value with the electricity load in Bali is the temperature, which is then followed by sun radiation and wind speed parameter. We obtain the best prediction with GRNN and SVR with a correlation coefficient value of 0.95 and 0.965, respectively.

Smart Grid System Using IoT

Smart Grid is a flexible Electric Grid, Communication and IT systems that can monitor the flow of energy from production areas to utility areas (even down to the level of electrical equipment) and control the flow of energy or reduce load to keep pace with real or near generation. Real-time smart Grids can be achieved through effective transfer and distribution systems, system functionality, customer integration, and renewable integration. Intelligent grid solutions help to monitor, measure, and control the flow of energy in real-time which can contribute to the detection of losses and thus take appropriate technical and administrative measures to capture losses. Electricity is an important invention otherwise life on Earth would not be possible. It is therefore clear that there is a need to measure the electricity used. A wattmeter is achieved, but a person from TNEB should visit each customer's house to measure energy consumption and calculate customer debt. So it requires a lot of manual labor and is time-consuming. We aim to build an IoT-based power meter for each TNEB customer. So the proposed energy meter measures the amount of energy used and uploads it to the Thitspeak cloud the affected person can view the reading. Power reading is sent to the cloud using ESP 8266, a Wi-Fi module. Power readings in a digital wattmeter are read using coupler opt and transmitted digitally to Arduino. So it automatically performs the process of measuring domestic energy use using IoT and thereby enables remote access and digitalization for each TNEB customer.

Electricity Demand Pattern and Supply Availability On Nigeria Grid System

This paper presents the analysis of electricity transmitted and demand on Nigeria's electricity grid system from the year 2018 to 2020 to give the present progress of the electricity system in Nigeria. The daily electricity generated and transmitted data, daily distribution companies (DISCOs) electricity demand and consumption data, and data of transmission lines connected to other neighbouring countries (international lines) within the year 2018 and 2020 were used for the analysis. Also, the extrapolation of the monthly energy of each of the data obtained was computed. The analysis was done and graphs and results obtained showed that daily average electricity day-ahead demand by DISCOs varied majorly between 3.5GW to 4GW with a corresponding increase above 4GW and the total daily day-ahead electricity demand by DISCOs varied majorly between 80GW and 90GW from 2018 to 2020. But despite this demand, the study showed that distribution companies did not at any time pick up to their declared load demand despite being the major electricity stakeholder in electricity delivery to consumers. Also, some generating station units were not generating to their capacity due to fault and gas constraints and some generating stations were connected to the grid without using free governor mode (control required for the generating units to respond to the state of electricity demand on the grid in real-time). The study recommends that the government should ensure proper monitoring and impose necessary sanctions if needs be on any electricity stakeholders and participants who violate the Nigeria Electricity Supply Market Rules for effective and the Nigeria grid code created for efficient power delivery. The government should, as a matter of urgency, start the expansion of her generating stations as well as developing new ones considering other sources for power generation such as wind and solar which are predominately abundant in the northern part of the country.

Smart EV Charging Strategies Based on Charging Behavior

Coming years, the number of electric vehicles (EVs) shall increase significantly, so the demand for electricity for charging EVs will proportionately increase as well. Thus, the growing energy requirements for charging these EVs might put huge burden on the electricity generation and supply infrastructure. Such a huge load growth opportunity for utilities if integrated successfully, or if not, a significant challenge to operate and balance grid loads in the future. Customarily, the increase in adoption of EVs in recent years has yielded challenges to the utilities as the electricity demand of EVs occurs mostly during peak hours. In some cases, a sojourn time may be longer than a charging time, that means, EVs will be connected to the charging station without charging. However, the load shifting potential of EVs may be consequential and might subsequently be used to alleviate challenges to the electric grid system. Considering charging behaviors for EV scheduling is crucial, as they depend on uncertainties of EV availabilities (i.e., sojourn time and energy required). Such uncertainties would impact substantially on the deployment of feasible EV charging scheduling. To address above-mentioned issues, firstly, we define an idle time ratio, which is basically load shifting potential. Consequently, we develop a heuristic EV charging scheduling scheme with an emphasis on inevitable charging behaviors of the EV users. Such a scheduling incorporates priority determination using the idle time ratio and TOU period as well as priority-based time slot allocation. Moreover, accurate prioritization of EVs is realized by predicting the energy demand and idle time ratio. Minimization of charging cost is perhaps the most perceptive objective, such that, the EV charging scheduling is done when TOU tariff is low. Performance evaluation shows that the proposed flexible smart charging scheduling outperforms the baseline scheduling in terms of the charging power and charging cost.

Improved Complete Ensemble Empirical Mode Decomposition with Adaptive Noise Deep Residual model for short-term multi-step solar radiation prediction

Global Solar Radiation (GSR) prediction models are critical to improve the dispatch, control, and stabilization of solar renewable power, and to integrate the solar energy into the electrical grid system. GSR, especially on a short-term scale, can have important fluctuations, which may affect the total energy expected to be supplied to the grid. To overcome this issue, prediction models with a high forecasting performance are needed. In this paper a novel framework based on Improved Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (ICEEMDAN) and Deep Residual Network with Bidirectional long short-term memory, i.e., DRESNET model, is proposed for obtaining accurate multi-step ahead GSR predictions. To train the proposed ICEEMDAN-DRESNET hybrid model, minute-level daylight data from Energy Sector Management Assistance Program in Nepalgunj (mid-western Nepal) are used. The results demonstrate ICEEMDAN-DRESNET model is an excellent tool for short-term solar energy monitoring, yielding excellent predictions, in all metrics such as MAE 9.769 W/m2, MAPE 5.657%, TIC 1.143, CPI 4.739, and TIC 0.023 for 5-min time-horizon predictions, improving the results from the benchmark models. As the forecasting time-horizon is increased, the ICEEMDAN-DRESNET model accuracy drops, with MAE 33.672 W/m2; MAPE 31.749% for 1-hr, MAE 22.625 W/m2; MAPE 18.312% (30-min) and MAE 14.897 W/m2; MAPE 10.358% (15-min), also better than the benchmark models. The results confirm the competitive merit of ICEEMDAN and DRESNET integration to improve deep learning and the potential of proposed model for the monitoring of solar or other renewable (e.g., wind or solar) energies.

Smart grid reliability evaluation and assessment

PurposeSmart grid is an integration between traditional electricity grid and communication systems and networks. Providing reliable services and functions is a critical challenge for the success and diffusion of smart grids that needs to be addressed. The purpose of this study is to determine the critical criteria that affect smart grid reliability from the perspective of users and investigate the role big data plays in smart grid reliability.Design/methodology/approachThis study presents a model to investigate and identify criteria that influence smart grid reliability from the perspective of users. The model consists of 12 sub-criteria covering big data management, communication system and system characteristics aspects. Multi-criteria decision-making approach is applied to analyze data and prioritize the criteria using the fuzzy analytic hierarchy process based on the triangular fuzzy numbers. Data was collected from 16 experts in the fields of smart grid and Internet of things.FindingsThe results show that the “Big Data Management” criterion has a significant impact on smart grid reliability followed by the “System Characteristics” criterion. The “Data Analytics” and the “Data Visualization” were ranked as the most influential sub-criteria on smart grid reliability. Moreover, sensitivity analysis has been applied to investigate the stability and robustness of results. The findings of this paper provide useful implications for academicians, engineers, policymakers and many other smart grid stakeholders.Originality/valueThe users are not expected to actively participate in smart grid and its services without understanding their perceptions on smart grid reliability. Very few works have studied smart grid reliability from the perspective of users. This study attempts to fill this considerable gap in literature by proposing a fuzzy model to prioritize smart grid reliability criteria.

Winter Storm Uri: Resource Loss and Psychosocial Outcomes of Critical Infrastructure Failure in Texas

In February of 2021, Winter Storm Uri affected parts of the United States, Mexico, and Canada. Texas was particularly hard hit, as the state's primary power provider, ERCOT (the Electric Reliability Council of Texas), proved to be unprepared for the event—despite similar storms in 1989 and 2011 that revealed weaknesses in the state's electric grid system. This article investigates psychosocial outcomes of individuals who experienced Winter Storm Uri. Drawing upon survey data collected in Texas in April and May of 2022, we illustrate ways in which loss of critical infrastructure and compounding results influence levels of stress among respondents. Using Hofoll's (1989, 1991) Conservation of Resources model of stress, we find that Uri‐related losses of objects and conditions resources contribute to elevated stress as measured by the Avoidance subscale of the Impact of Event Scale (Horowitz 1976; Horowitz, Wilner, and Alvarez 1979)— more than one year after the disaster. Our regression model consisting of indicators of objects resource loss, conditions resource loss, and demographic characteristics explains approximately 33 percent of the variance in the Avoidance subscale. Findings suggest that more attention should be paid to the social impacts of critical infrastructure failures and that such impacts should be addressed by improving critical infrastructure policy and regulations, as well as the physical structures.

A Review on Wind Power Forecasting Models for improved Renewable Energy Integration

This paper gives an overview of the research that has been done on wind power forecasting models, which may be used to aid in the optimum integration of Renewable Energy (RE) into electric power networks. Aside from the economic advantages, the variable nature of wind energy production has a variety of negative consequences for the electric grid system, including stability, reliability, and the capacity to plan for future operations, among other things. As a result, precise forecasting of wind power output is critical for grid stability and security, as well as for the promotion of large-scale wind power. To ensure the accuracy of wind energy forecasts, a variety of conventional, artificial intelligence, and hybrid methodologies have been developed. The simplicity and robustness of time-series-based methods have made them popular for forecasting applications. Artificial Neural Networks (ANNs) and Fuzzy Logic have recently been supported by several researchers for forecasting because of their flexibility. This review covers the performance of many wind power forecasting models that are classified according to their categories. It is also offered a critical examination of contemporary studies, which includes statistical model and machine learning models that are based on historical data. Aspects of this study that are taken into consideration include the advantages and disadvantages of various forecasting models, including hybrid models, as well as performance matrices used in assessing the forecasting model. In addition, the possible advantages of model optimization are examined in detail as well.

A filter less improved control scheme for active/reactive energy management in fuel cell integrated grid system with harmonic reduction ability

Fuel-cell integrated electrical grid systems are considered the future of energy generation since it is highly energy-efficient and environmentally friendly. However, they are still in the initial phase of implementation and hold some drawbacks that need to be addressed. In fuel cell integrated grid systems, electrical loads usually produce active/reactive power which hampers the nominal operation of the grid system and depreciates the power factor of the system. This condition instigates massive energy loss in the system by generating harmful harmonic components. It also increases the energy demand and thus, the consumers are penalized with excessive bills. The conventional energy management systems in a fuel cell-based grid usually incorporate conventional power filters which are incapable of accurately eliminating harmonic components and unable to compensate reactive power generated by the local loads. Thus, these control systems cannot properly compensate for the energy flow in the grid. To deal with this issue, the current manuscript proposes a new filter-less active/reactive energy management approach that can compensate for reactive powers by accurately eliminating harmonics from the grid system. In this manner, the proposed system can prevent energy loss and enables the consumers to save up on electricity bills. The performance of the proposed system is analyzed under different load conditions and the results are compared with other conventional energy management schemes to verify its superiority. The results from the case studies showed that the proposed control can effectively reduce harmonics from the system and can also keep the power factor of the grid system close to unity.