Electrical Energy Networks Research Articles

Optimal operation of hydrogen-based multi-energy microgrid integrating water network and transportation sector

To address the rising demand for hydrogen energy and its reliance with the water sector, this study presents an optimal scheduling framework for a multi-energy microgrid (MEMG) that integrates electric, thermal, water, and hydrogen energy networks. To this end, a mixed-integer linear programming (MILP) model is formulated to minimize both operational costs and emissions. A bi-variate piecewise McCormick envelope technique is utilized to manage the non-linear constraints associated with the water network. The model also incorporates the transportation sector, including electric and hydrogen vehicles (EVs, HVs), with vehicle-to-grid (V2G) technology, and models their associated uncertainties using Monte Carlo simulation (MCS). Additionally, the sale of oxygen as a by-product of the hydrogenation process is also considered. The case study shows significant economic and environmental benefits, with a 29.66% cost reduction and 22.26% emissions decrease from water network integration. Oxygen sales further reduce costs by 14.19%, and V2G technology contributes an additional 2.35% cost and 6.01% emissions reduction. The proposed linear approximation method achieved superior performance, with a root mean square error (RMSE) of 0.72 and a relative error of 2.132%.

A hybrid optimization for distributed generation and D-STATCOM placement in radial distribution network: a multi-faceted evaluation

The deregulation of the power system, upward growth in electrical energy demand and network expansion have resulted in an increasing integration of distributed generation (DG) and distribution static synchronous compensator (D-STATCOM) into radial distribution systems (RDS). Nonetheless, the optimal allocation of these devices is highly important to derive immense benefits. This investigation narrows down on optimizing DG and D-STATCOM placement in IEEE 33-bus RDS with a view to increase bus voltages, decrease power losses as well as maximize economic gains. The study undertakes a comprehensive analysis comparing the technical, economic and environmental performance of DG and D-STATCOM; thereby enabling power engineers to make informed choices concerning which device will be most advantageous when it comes to delivering power in RDS. A fuzzy enhanced firefly optimization (FEFO) approach is proposed for the optimization and a multifaceted evaluation in terms of technical, financial and environmental is presented for effective decision-making on distributed energy resource deployment. D-STATCOM and wind DG integrations led to notable reductions in power loss and pollutant emissions, highlighting their effectiveness in improving power quality and reducing reliance on fossil fuels. While wind DG incurred a higher installation cost ($3,100,749.2) compared to D-STATCOM ($90,566.6), it achieved greater yearly power loss cost savings ($69,198 versus $47,619). FEFO’s efficiency in optimization stands out, aiding engineers in making informed decisions for optimizing D-STATCOM and wind-DG integration in the IEEE-33 RDS, ultimately enhancing system performance and cost-effectiveness through proactive planning. The integration of D-STATCOM and wind DG led to a significant improvement in distribution system efficiency, with D-STATCOM reducing real power loss by 28.7% and reactive power loss by 27.8%, while wind DG achieved greater reductions of 41.8% in real power loss and 37.5% in reactive power loss, alongside reductions in pollutant emissions of 1.5% and 2.2%, respectively.

Sizing a Renewable-Based Microgrid to Supply an Electric Vehicle Charging Station: A Design and Modelling Approach

In this paper, an optimisation framework is presented for planning a stand-alone microgrid for supplying EV charging (EVC) stations as a design and modelling approach for the FEVER (future electric vehicle energy networks supporting renewables) project. The main problem of the microgrid capacity sizing is making a compromise between the planning cost and providing the EV charging load with a renewable generation-based system. Hence, obtaining the optimal capacity for the microgrid components in order to acquire the desired level of reliability at minimum cost can be challenging. The proposed planning scheme specifies the size of the renewable generation and battery energy storage systems not only to maintain the generation–load balance but also to minimise the capital cost (CAPEX) and operational expenditures (OPEX). To study the impact of renewable generation and EV charging uncertainties, the information gap decision theory (IGDT) is used to include risk-averse (RA) and opportunity-seeking (OS) strategies in the planning optimisation framework. The simulations indicate that the planning scheme can acquire the global optimal solution for the capacity of each element and for a certain level of reliability or obtain the global optimal level of reliability in addition to the capacities to maximise the net present value (NPV) of the system. The total planning cost changes in the range of GBP 79,773 to GBP 131,428 when the expected energy not supplied (EENS) changes in the interval of 10 to 1%. The optimiser plans PV generation systems in the interval of 50 to 63 kW and battery energy storage system in the interval of 130 to 280 kWh and with trivial capacities of wind turbine generation. The results also show that by increasing the total cost according to an uncertainty budget, the uncertainties caused by EV charging load and PV generation can be managed according to a robustness radius. Furthermore, by adopting an opportunity-seeking strategy, the total planning cost can be decreased proportional to the variations in these uncertain parameters within an opportuneness radius.

Using Analytic Hierarchy Process to Assess Beekeeping Suitability in Portuguese Controlled Areas: A First Approach.

Beekeeping management is greatly influenced by spatial factors (e.g., land use/land cover, roads, or electrical energy networks), so GIS are a powerful tool to overlap and relate a variety of spatial data levels and, consequently, a very useful tool for beekeeping activity planning. This study was developed within the intervention area of three controlled zones managed by Portuguese Beekeepers Associations. The methodology, based on multi-criteria decision analysis, integrates several criteria, such as hydrographic networks, road networks, soil occupation, solar radiation, and electromagnetic radiation sources. These criteria were proposed and evaluated through online questionnaires carried out with beekeepers. Concerning the selected criteria and the respective geographical data, the most relevant were land use/land cover and water availability, with a significance of 44% and 24%, respectively. The beekeeping suitability map enabled us to evaluate the degree of compliance for the actual location of apiaries, with 60% of the apiaries being installed in high potential areas. In the context of beekeeping planning, the potential of the techniques applied seems to be an important tool for optimizing the location of apiaries and the profitability of beekeeping.

A hybrid method for the optimal reactive power dispatch and the control of voltagesin an electrical energy network

A hybrid method for the optimal reactive power dispatch and the control of voltagesin an electrical energy network

Implementing sustainable practices in the electrical energy network of Uzbekistan: a holistic approach to environmental management and engineering

Implementing sustainable practices in the electrical energy network of Uzbekistan: a holistic approach to environmental management and engineering

Investigating the impact of building local photovoltaic power plants on the national grid, an artificial intelligence approach

Today, simple electrical energy networks are becoming obsolete and are being replaced by smart networks. One of the advantages of smart grids is to facilitate energy management, so that one of the ideas of this method is to provide local energy sources (photovoltaics, wind turbines, generators, etc.) to consumers and predict the amount of energy production. In this way, not only the consumed electricity is supplied, but also it is determined the time that electricity can be exported to the national grid. For this purpose, it is necessary to predict the production and consumption of each unit in the network. In this article, an urban building complex in the Canadian state of Alberta is investigated. Its consumption is calculated using simulation in the calculation software, and then the amount of electricity produced by photovoltaic panels is also calculated and the result of production and consumption is estimated, then using a machine learning algorithm of energy balance for the following years are expected. The results of this study show that the use of photovoltaic systems and the production of electrical energy from it can have a significant impact on energy management, and not only the building's electricity is supplied in most months of the year, but also additional electricity can be used. Sending this excess energy to the main grid, can be an important step towards peak management of the power grid.

Real-time computing of power flows and node voltages in electrical energy network using decision trees

Real-time computing of power flows and node voltages in electrical energy network using decision trees

Hybrid Energy Systems for Buildings: A Techno-Economic-Enviro Systematic Review

Hybrid energy systems physically or conceptually combine various energy generation, storage, and/or conversion technologies to reduce costs and improve capability, value, efficiency, or environmental performance in comparison with independent alternatives. Hybridization is an interesting energy sector solution for plants to expand their flexibility, optimize revenues, and/or develop other useful products. Integrated hybrid energy systems’ improved flexibility can hasten the integration of more renewable energy into the grid and help become closer to the target of zero-carbon energy grids. This paper aims to provide an updated literature review of design and applications of hybrid energy systems in buildings, focusing on economic, environmental, and technical viewpoints. This current study will analyze current and future trends toward hybrid energy systems for buildings and their functions in electrical energy networks as potential research study topics for the future. This study aims to enhance sustainable building techniques and the creation of effective electrical energy networks by offering insights into the design and applications of hybrid energy systems. The methodology used in this study entails assessing present and potential trends, as well as looking at hybrid energy system uses and designs in buildings. The higher flexibility of integrated hybrid systems, which enables enhanced grid integration of renewables, is one of the key discoveries. The discussion of potential research study themes and conceivable applications resulting from this research forms the paper’s conclusion.

Modeling and Optimization of Combined Heating, Power, and Gas Production System Based on Renewable Energies

Electrical energy and gas fuel are two types of energy needed that increase environmental pollution by burning fossil fuels in power plants to produce electrical energy and direct combustion of gas fuel. In this research, an attempt has been made to model the electrical energy network in the presence of renewable energy sources and gas production systems. The advantage of this model compared to other models of similar studies can be found in providing a mixed integer linear optimization model of distributed generation sources with gas fuel, energy storage systems, and gas power systems, along with electric vehicles in an integrated electricity and gas system. In addition to the energy consumption of buildings, an electric vehicle is also considered a base load, which is one of the limitations in optimizing the maximum charging of an electric vehicle. Among the important results of this research, it can be mentioned that the investment cost of USD 879,340 in the first scenario, in which 37,374 kW of electric energy was purchased from the network to supply the electric load, and 556,233 m3 was purchased from the gas network to supply the required gas.

State Estimation for Electric Power System with Load Uncertainty and False Data Using Cuckoo Search Algorithm

State estimate serves a crucial purpose in the control centre of a modern power system. Voltage phasor of buses in such configurations is referred to as state variables that should be determined during operation. A precise estimation is needed to define the optimal operation of all components. So many mathematical and heuristic techniques can be used to achieve the aforementioned objective. An enhanced power system state estimator built on the cuckoo search algorithm is described in this work. Several scenarios, including the influence of load uncertainty and the likelihood of false data injection as significant challenges in electrical energy networks, are proposed to analyses the operation of estimators. The ability to identify and correct false data is also assessed in this regard. Additionally, the performance of the presented estimator is compared to that of the weighted least squares and Cuckoo Search algorithm. The findings demonstrate that the Cuckoo search algorithm overcomes the primary shortcomings of the conventional approaches, including accuracy and complexity, and is also better able to identify and rectify incorrect data. On IEEE 14-bus and 30-bus test systems, simulations are run to show how well the method works.

Participation of load aggregator in grid frequency stabilization with consideration of renewable energy resources integration

Alterations in the proportion of conventional generation to that of renewable energy source could be a future challenge for electric energy networks. Conventional power plants are being replaced by wind and solar energy. Solar PV plants and wind turbines lower the system inertia constant, causing the grid frequency to shift. Renewable energy generation plants that are integrated into the energy system may cause problems with frequency stability if adequate frequency control techniques are not applied. In this study, a Load Frequency Control framework based on aggregators is developed to improve the frequency response of the power system. It was demonstrated that the aggregator is capable of forecasting available flexibility for the day ahead contributing to the frequency control process. The aggregator is contributing to the grid frequency stabilization considering the price signal for minimizing energy costs, with an effect on end user’s energy bills reduction. For assessing the aggregator’s flexibility availability, the aggregator calculates the contributed power after minimizing its energy costs. The results show that it is possible to use DR technology to stabilize the grid frequency. With the loss of generation of 50 MW, the aggregators contribute mainly for the morning event with a contribution of about 4 MW followed by the night event with the contribution of approximately 3.5 MW. The largest contribution was obtained between 06:00 and 8:00 for the examined area with three aggregators, and the smallest contribution was obtained between 22:00 and 24:00. By considering the whole power system, with the integrated renewable energy resources in different periods, the findings demonstrate that even though the frequency decreases, it quickly returns to the usual operating limit and stabilizes itself back to the normal operating limits of 50 Hz as a result of the aggregator participation in the process of grid frequency stabilization.

Grey Wolf Optimizer and Cuckoo Search Algorithm for Electric Power System State Estimation with Load Uncertainty and False Data

State estimate serves a crucial purpose in the control centre of a modern power system. Voltage phasor of buses in such configurations is referred to as state variables that should be determined during operation. A precise estimation is needed to define the optimal operation of all components. So many mathematical and heuristic techniques can be used to achieve the aforementioned objective. An enhanced power system state estimator built on the cuck search algorithm is described in this work. Several scenarios, including the influence of load uncertainty and the likelihood of false data injection as significant challenges in electrical energy networks, are proposed to analyse the operation of estimators. The ability to identify and correct false data is also assessed in this regard. Additionally, the performance of the presented estimator is compared to that of the weighted least squares, Cuckoo Search algorithm and grey wolf Optimizer. The findings demonstrate that the grey wolf Optimizer overcomes the primary shortcomings of the conventional approaches, including accuracy and complexity, and is also better able to identify and rectify incorrect data. On IEEE 14-bus and 30-bus test systems, simulations are run to show how well the method works.

Adaptation and optimization of a photovoltaic system for a country house

This chapter provides the rationale for monitoring photovoltaic (photovoltaic) systems, its purpose, the need for proper measurement, and the frequency required to obtain meaningful results. The need for system monitoring falls into three groups: user feedback, performance review, and system evaluation. Each group is briefly summarized; other related topics include calibration, data storage, and data transfer. The text covers various monitoring modes such as performance testing, system evaluation, data collection and storage, as well as data analysis and reporting. The evaluation standards given by the International Electrotechnical Commission (IEC) are noted in the Data Analysis and Reporting section. The figures are provided to illustrate several types of displays that are an integral part of monitoring. Optimized modes of operation of the power part of a household photovoltaic system consisting of photovoltaic panels, an MRRT controller, an inverter and an electric energy storage unit, with Autonomous and network connection of the load. To build an optimal and cost-effective energy management strategy, it is necessary to take into account the energy profile of the resident, the characteristics of electricity production based on photovoltaic cells, and the electricity tariff for utilities. A photovoltaic system can be connected to the grid in order to receive energy from the grid or enter it into the network to eliminate the discrepancy between the generated, consumed and stored energy.

Defining a new index to compare the resilience of different structures of an electrical energy network

The issue of resilience in electrical distribution systems has been proposed increasingly with the frequent occurrence of natural disasters in recent years and the imposition of high costs due to widespread power outages. To date, various resilience indices have been proposed, some of which have been improved upon over time by extensive research, leading to more comprehensive indices. However, a standard index has not yet been approved and presented in this regard by international committees, despite the efforts made. The issue of resilience in electrical networks was examined by curve analysis index in more detail compared to other proposed indices, although it is not yet complete and should be investigated from various aspects and its shortcomings be eliminated. The present study aims to evaluate some fundamental obstacles in the “curve analysis” index and correct it in a new index called “Combining Investment and Reform” (CIR) index. Two issues of “costs in terms of investment and repairs imposed by the event” and “need to separate critical and non-critical loads” are considered simultaneously in the proposed index. Finally, the capabilities of the proposed index are evaluated and compared in a sample electrical network in the face of events with different intensities.

Distributed tri-layer risk-averse stochastic game approach for energy trading among multi-energy microgrids

This paper discusses a tri-layer non-cooperative energy trading approach among multiple grid-tied multi-energy microgrids (MEMGs) in the restructured integrated energy market. The heterogeneous uncertainties from renewable energy, market prices, and electric energy loads are also considered via the risk-averse stochastic programming (SP) approach. First, comprehensive operation models of individual MEMGs are presented with the consideration of practical electric energy and thermal network flows as well as battery degradation. Second, to guarantee fair multi-energy trading among MEMGs and deal with adverse effects from all uncertainty sources, a tri-layer Cournot Nash game-based energy bidding method is developed and solved by the SP approach. In the first layer, i.e., day-ahead multi-energy market, optimal energy bids, dispatches of energy storage assets, and thermal flows against uncertainty scenarios are acquired in a risk-averse manner; In the second layer, i.e., intra-day multi-energy market, optimal intra-day energy bids and dispatches of all resources against uncertainty realizations are sequentially calculated; In the third layer, i.e., the real-time multi-energy market, transactions between each MEMG and the wholesale multi-energy market are finalized. Third, for protecting the privacy of individual MEMGs and alleviating the computation burdens, the distributed alternating search procedure is employed to compute the Nash equilibriums in the day-ahead and intra-day markets. In the end, numerical case studies are conducted to verify the effectiveness of our method. From the simulation results, it can be inferred that compared with the traditional cooperative, deterministic and risk-natural methods in the literature, our proposed method is more practical and economical for real-world applications since it comprehensively considers the market competition, uncertainty handling, and energy trading risk.

An Updated Review and Outlook on Electric Vehicle Aggregators in Electric Energy Networks

Electric vehicles (EVs) are predicted to be highly integrated into future smart grids considering their significant role in achieving a safe environment and sustainable transportation. The charging/discharging flexibility of EVs, which can be aggregated by an agent, provides the opportunity of participating in the demand-side management of energy networks. The individual participation of consumers at the system level would not be possible for two main reasons: (i) In general, their individual capacity is below the required minimum to participate in power system markets, and (ii) the number of market participants would be large, and thus the volume of individual transactions would be difficult to manage. In order to facilitate the interactions between consumers and the power grid, an aggregation agent would be required. The EV aggregation area and their integration challenges and impacts on electricity markets and distribution networks is investigated in much research studies from different planning and operation points of view. This paper aims to provide a comprehensive review and outlook on EV aggregation models in electrical energy systems. The authors aim to study the main objectives and contributions of recent papers and investigate the proposed models in such areas in detail. In addition, this paper discusses the primary considerations and challenging issues of EV aggregators reported by various research studies. In addition, the proposed research outlines the future trends around electric vehicle aggregators and their role in electrical energy systems.

Integrated optimization of multi-carrier energy systems: Water-energy nexus case

This paper focuses on the optimal operation of integrated electrical and water energy networks at the distribution level considering the demand response program. In this manner, the water network with various components including reservoirs, tanks, fixed and variable speed pumps, and control valves is modeled. Newton-Raphson method is adopted to solve the water flow problem. This technique, unlike existing methods, can be applied to any network with any type of topology, whether radial, circular or hybrid. The electrical network is also modeled with the relevant constraints and coupled with the water network to supply the required electrical power. The optimization problem in a practical energy system, including a standard IEEE 33-bus electrical network, and the North Marin water network has been solved by a teaching learning-based optimization algorithm as a parameter-free method. The objective is to minimize the total operation cost. With the participation of the water network in a demand response program, the optimal charging and discharging of tanks, and pumps scheduling has been carried out, which consequently has led to operation cost reduction. In addition, numerical results have proved that the utilization of variable speed pumps can reduce about 7% of the total costs.

Dispatch optimization of electricity-gas regional integrated energy network considering carbon trading

Aiming at the scheduling optimization problem of the regional electrical integrated energy network under the background of the dual carbon target strategy, on the premise of ensuring the balance and stability of the IIES energy side supply, an optimization solution method for the regional electrical-gas integrated energy network scheduling considering carbon trading was proposed. Based on the priority model of building multi-energy equipment, and taking the minimum sum of the output cost of energy production, the cost of energy conversion, and the carbon transaction cost caused by net carbon emissions as the objective function, the daily optimal scheduling of the regional electrical integrated energy network was established. The model realized the optimal solution of the output value of different energy forms. Utilizing simulation technology, the objective function values of the unit carbon tax price of 8.32$/t, 24.79$/t, and 46.94$/t under the specific scenario of IIES were discussed. The scenario with the optimal objective function (unit carbon tax price of 24.79$/t) was further compared. In addition, the real-time output value of multi-energy equipment was analyzed when the network scheduling optimization was optimized.

Blockchain-based trust management and authentication of devices in smart grid

The digitalization of the power grid and advancement in intelligent technologies have enabled the service provider to convert the existing electrical grid into a smart grid. The transformation of the grid will help in integrating cleaner energy technologies with energy management to improve power network efficiency. Internet of things (IoT) and various network components need to be deployed to harness the full potential of the smart grid. Also, integrating intermittent renewable energy sources, energy storage, intelligent control of selected power-intensive loads, etc will improve energy efficiency. But deployment of this information and communication technologies will make the grid more vulnerable to cyber attacks from hackers. In this work, blockchain-based self-sovereign identification and authentication technique is presented to avert identity theft and masquerading. The proposed approach can minimize the chances of identity-based security breaches in the smart grid. This paper provides an overview of the model of identification and authentication of IoT devices in Smart Grid based on Blockchain technology. The Blockchain based implementation of identification and authentication of devices is proposed to validate the model in the distributed electrical energy network. The model is able to authenticate the device using Blockchain in a trusted model. The system works according to plan validating the authenticity of transaction in a node in log(n) time, which justifies presented result.