Electric Energy Systems Research Articles

Distributed Optimal Frequency Control under communication packet loss in multi-agent electric energy systems

This paper investigates the impact of communication packet loss on Distributed Optimal Frequency Control (DOFC) in Alternating Current (AC) electric energy systems, populated with multiple clusters of hybrid producer–consumer (prosumer) agents. The paper first establishes rigorous relationships between the communication packet delivery ratio and the convergence rate of the proposed DOFC algorithm. This provides a foundation for resource allocation on communication systems to enhance the convergence speed of distributed optimization and control algorithms, such as DOFC, under noisy and disrupted communication systems. The paper develops a systematic approach to identify the best possible convergence rate over all possible algorithms, by introducing an algorithm that can achieve asymptotically the Cramér-Rao (CR) lower bound. This fundamental result links the information contents of data to the best possible mean-square estimation error. Simulation studies on an electric energy system validate the theoretical results.

Integrated optimization in operations control and systems design for carbon emission reduction in building electrification with distributed energy resources

Building electrification with distributed energy resources (DERs) is a promising strategy to decarbonize the building sector. Considering the inter-dependencies between operations control and systems design, integrating technology operations control optimization with DERs investment optimization can cost-effectively enhance such building decarbonization opportunities. This study proposes a multi-timescale integrated optimization framework to simultaneously optimize the design and control of DERs and electrification technologies for buildings. A novel building operational performance prediction model based on deep learning is developed to approximate and replace the computationally expensive control optimization. This helps resolve the challenging, computationally intractable multi-timescale integrated design and control optimization problem. Applying the proposed framework to a residential building, our results demonstrate its effectiveness in cost-efficient carbon emissions reduction. With integrated design and control optimization for DERs and electric building energy systems, the proposed framework reduces operational carbon emissions by 80% and total costs by 2.7% compared to a base case, which uses typical conventional building energy systems without DERs and control/design optimization. Separate optimization of operations control and system design cannot achieve such performance. Further scenario analyses indicate that as power grids become cleaner, the reliance on DERs can be alleviated but remain important in building carbon emission reduction under 2050 power grid scenario. Overall, as our results demonstrate, it is possible to reduce building operational carbon emissions simultaneously with net electrical load: compared to the base case, the proposed framework helps reduce the carbon emission by 80% while driving down the net electrical load from 44.1 to 19.3 kWh/m2/year.

Agents and Multi-agent Systems in the Management of Electric Energy Systems

Agents and Multi-agent Systems in the Management of Electric Energy Systems

Review of Big Data Analytics for Smart Electrical Energy Systems

Energy systems around the world are going through tremendous transformations, mainly driven by carbon footprint reductions and related policy imperatives and low-carbon technological development. These transformations pose unprecedented technical challenges to the energy sector, but they also bring opportunities for energy systems to develop, adapt, and evolve. With rising complexity and increased digitalization, there has been significant growth in the amount of data in the power/energy sector (data ranging from power grid to household levels). Utilization of this large data (or “big data”), along with the use of proper data analytics, will allow for useful insights to be drawn that will help energy systems to deliver an increased amount of technical, operational, economic, and environmental benefits. This paper reviews various categories of data available in the current and future energy systems and the potential benefits of utilizing those data categories in energy system planning and operation. This paper also discusses the Big Data Analytics (BDA) that can be used to process/analyze the data and extract useful information that can be integrated and used in energy systems. More specifically, this paper discusses typical applications of BDA in energy systems, including how BDA can be used to resolve the critical issues faced by the current and future energy network operations and how BDA contributes to the development of smarter and more flexible energy systems. Combining data characterization and analysis methods, this review paper presents BDA as a powerful tool for making electrical energy systems smarter, more responsive, and more resilient to changes in operations.

Performance analysis of a hybrid solar-hydrogen-retired EV batteries (REVB) energy system with thermal-electrical loops

Utilizing solar energy is an efficient method to provide hybrid renewable energy system with sufficient thermal/electrical energy. Meanwhile, the rapid development of electrical vehicles leads to an excess of retired electric vehicles. As a combination of the abovementioned two conceptions, this study proposed and examined a hybrid solar-hydrogen-retired electrical vehicle battery energy system to meet thermal and electrical loads for small-scale usage. The novelty of this research is delivered as follows: first of all, the proposed hybrid energy system supplies both thermal and electrical energy to small-scale end users; secondly, the retired electrical vehicle batteries are recycling to relieve the pressure of battery demand; thirdly, an energy management strategy to regulate the complicated hybrid energy system is designed. The results show that with assistance of fuel cell as an energy storage unit, solar energy can basically satisfy the annual thermal/electrical load with maximum monthly energy supplement of 1220.43 MJ and 1572.75 kWh, respectively. However, the solar radiation serving as single energy source is not very reliable for large-scale utilization. Although the state of charge does not fluctuate greatly, the small range charge/discharge between 59% and 63% can still guarantee the normal operation of the proposed hybrid energy system.

Hybrid Optimization Approach for Improved Efficiency in Permanent Magnet Brushless DC Motor Design: Combining Cauchy Particle Swarm & Moth Flame Methods

This article proposes a novel design strategy for a PMBLDC motor through hybrid optimization, combining Cauchy Particle Swarm Optimization and Moth Flame Optimization approaches to find the optimal set of design parameters that yield the highest efficiency. The motivation is to improve the efficiency of PMBLDC motors, which are widely used in electric cars and renewable energy systems that place a premium on minimal power usage. The proposed work implements the hybrid optimization technique performs conventional single optimization strategies in terms of efficiency improvement. This study contributes in identifying the optimal values for the various design parameters of PMBLDC motors using the hybrid CPSO-MFO approach, which converged to the optimal design parameters. The performance of the PMBLDC motor was evaluated using the optimal design parameters, with an overall power loss of 49.1 W, and an efficiency of 97.88%. The study’s results provide valuable insights for design engineers into the optimal geometrical parameters of PMBLDC motors to achieve higher efficiency and lower power loss. The proposed novel hybrid optimization approach, which is faster and more efficient than conventional single optimization approaches, making it a useful tool for designing high-performance PMBLDC motors.

A security awareness and competency evaluation in the energy sector

The energy sector is highly vulnerable to cyber-attacks due to its inherently complex ecosystem of both physical and cyber infrastructure spreading across the globe. Cyber-security breaches in this domain could have a significant impact not only on the global economy but also on citizens’ lives. This paper aims at evaluating the security awareness and competency of European Electrical Power and Energy Systems (EPES) organisations’ workforce during the COVID-19 pandemic and the Ukrainian war. A targeted assessment campaign has been designed and conducted from 11th February 2022 until 18th March 2022. During that period, 132 participants, out of the 266 invited employees, participated in the campaign. The collected results were analysed from different perspectives unveiling significant findings regarding information security readiness and resilience of individuals and, consequently, organisations in the European energy sector. Key findings are discussed in detail concluding with various cyber-security recommendations addressing both the emerged vulnerabilities and the need for security culture evolution.

Experimental study on the thermal management performance of a power battery module with a pulsating heat pipe under different thermal management strategies

Given the power battery module for electric vehicle kinetic energy system in a high-temperature environment and large rate discharge process, there are thermal safety risks such as battery easy overheating and temperature uneven, this paper takes 23Ah LFP power battery module as the research object to build the system. The system uses TiO2 nanofluid pulsating heat pipe (TiO2-CLPHP) and cooling fan as heat conduction and heat dissipation components and conducts thermal monitoring of power battery modules through a thermocouple built into the power battery. The thermal management strategy of optimal thermal efficiency and optimal thermal management strategy of energy consumption proposed in this paper is used to solve the thermal management problem of power battery modules. On this basis, the performance tests of the power battery module thermal management system with optimal thermal efficiency and optimal energy consumption were carried out under different thermal management performance testing conditions, and the energy consumption and economy of the power battery module thermal management system under the two thermal management strategies were quantitatively analyzed. The test results show that compared with the pure power battery module without any thermal management technical measures, the maximum temperature of the thermal management system of the TiO2-CLPHP power battery module under the optimal thermal management strategy is reduced by 10.3℃ and the maximum cooling efficiency is up to 75.00%. In the dynamic test condition, the TiO2-CLPHP power battery module thermal management system implemented the optimal thermal management strategy of thermal efficiency, and the maximum temperature rise of the power battery was 9.7℃, 7.3℃, 7.5℃, and the maximum temperature difference of the battery was not more than 3.5℃ and 2.5℃, respectively. And when optimal thermal management, whose goal is to have a small critical temperature rise strategy by operating energy consumption, is adopted. The maximum temperature rises are 10.5℃, 8.5℃ and 6.9℃, respectively, with the maximum battery temperature difference not exceeding 3.6℃ and 3.5℃. Compared with the thermal management strategy with optimal thermal efficiency, this not only ensures the thermal management efficiency but also reduces the energy consumption index of the system (that is, it has less thermal management energy consumption). In addition, under different ambient temperatures, compared with the optimal thermal management strategy for thermal efficiency, the TiO2-CLPHP power battery module thermal management system implements the optimal thermal management strategy for energy consumption with a smaller temperature rise critical value, and the energy consumption index of the system can be reduced by up to 60.58%. This indicates that for the TiO2-CLPHP power battery module thermal management system, the proposed energy consumption optimal thermal management strategy with a small critical value can effectively take into account both thermal management efficiency and thermal management energy consumption, which is the most ideal thermal management strategy for power battery modules.

Editorial: Internet of energy for renewable energy-based decarbonized electrical energy systems

EDITORIAL article Front. Energy Res., 10 March 2023Sec. Smart Grids Volume 11 - 2023 | https://doi.org/10.3389/fenrg.2023.1160184

Electrical energy systems resilience: A comprehensive review on definitions, challenges, enhancements and future proceedings

AbstractSecure energy providing is a critical feature of sustainable societies, where this subject requires reliable operation of generation, transmission and distribution infrastructures. In order to achieve the mentioned goal, the influence of uncertainties should be analyzed using appropriate methods, criteria and tools. The high‐impact low‐probability (HILP) events are special uncertainties that can affect the safe operation and lead to irreparable damages. This paper presents a comprehensive review of resilience in electrical energy systems consisting of fundamental concepts and definitions, challenges, enhancements and future proceedings. In this regard, a resilience evaluation framework is designed first in which all the necessary actions for a resilience study are defined. In the next step, various HILP events are introduced and then the resilience concept is defined accurately and related curves are investigated. Afterwards, the resilience assessment indices and quantitative measures are described and their details and applications are precisely explained. As well, the resilience enhancement strategies are discussed from short‐term and long‐term perspectives and eventually, resilience‐based objective functions and optimization approaches are classified in the last part. It is noteworthy that the main research gaps are also clarified according to the reviewed papers and further suggestions are expressed to cope with the declared problems.

Modeling and Analysis of Utilizing Cryptocurrency Mining for Demand Flexibility in Electric Energy Systems: A Synthetic Texas Grid Case Study

The electricity sector is facing the dual challenge of supporting increasing level of demand electrification while substantially reducing its carbon footprint. Among electricity demands, the energy consumption of cryptocurrency mining data centers has witnessed significant growth worldwide. If well-coordinated, these data centers could be tailor-designed to aggressively absorb the increasing uncertainties of energy supply and, in turn, provide valuable grid-level services in the electricity market. In this paper, we study the impact of integrating new cryptocurrency mining loads into Texas power grid and the potential profit of utilizing demand flexibility from cryptocurrency mining facilities in the electricity market. We investigate different demand response programs available for data centers and quantify the annual profit of cryptocurrency mining units participating in these programs. We perform our simulations using a synthetic 2000 bus ERCOT grid model, along with added cryptocurrency mining loads on top of the real-world demand profiles in Texas. Our preliminary results show that depending on the size and location of these new loads, we observe different impacts on the ERCOT electricity market, where they could increase the electricity prices and incur more fluctuations in a highly non-uniform manner.

Optimization scheduling of a wind–photovoltaic–gas–electric vehicles Community-Integrated Energy System considering uncertainty and carbon emissions reduction

Coupling renewable energy power generation, electric vehicles, combined cooling heating and power system, and energy storage system is a new way for Community-Integrated Energy Systems (CIES) to shift to a low-carbon, highly efficient, environmentally friendly society, which is the key task in coordinating flexible demand response with multiple uncertainties in renewable energy. To this end, an integrated demand response program is used to explore the potential interaction ability of electricity-gas-heat-cool-Electric Vehicles flexible loads in a scheduling model based on chance-constrained programming, based on which an integrated demand respond-enabled CIES with uncertain renewable generation was built, meanwhile, a ladder-type carbon trading mechanism was designed, and then the original model was transformed into a solvable mixed-integer linear programming model using Sequence Operation Theory and linearization methods. Furthermore, the objective function was constructed by using the deviation preference optimization strategy, daily comprehensive cost and daily carbon emissions were introduced to reflect the decision-makers’ different optimization preferences by using weight coefficients as double optimization objectives. Finally, the simulation shows that CIES operation can effectively balance economy and reliability by setting an appropriate confidence level for the spinning reserve constraint, meanwhile, the comparative result shows that EV charging in the CIES has a very significant environmental benefit in carbon dioxide emissions reduction, while the number of EVs exceeds 500, EV charging in the CIES starts to show a better economic benefit.

Faulty Branch Identification Method for DC Generation and Storage Systems With Multiple Series-Parallel Cells

Solar photovoltaic energy and electrochemical energy storage are key elements for the development of more sustainable and efficient electrical energy systems such as electric vehicles or microgrids. These systems require of multiple series-parallel cell connections that complicate enormously the location of possible ground faults. In this context, this article proposes a new method based on a low frequency periodical commutation of a grounding resistor placed in the midpoint of the DC source, that allows to discern the faulty branch by measuring the current at strategical positions of each string. The highest relative current variation when the system is grounded or ungrounded determines the faulty branch. The proposed method has been analyzed for three types of grounding connections by means of simulations and experimental tests using batteries, achieving successful results.

A Statistical Signal Processing Approach to Islanding Detection

The integration of distributed generation (DG) sources in the electric energy systems may bring new problems that need attention, one of these problems is the occurrence of unintentional islanding. Islanding is a condition in which part of the distribution network is disconnected from the system, and consumer units are still powered by one or more DGs, which can cause damage to equipment and pose risks to the safety of technicians. This paper shows an islanding detection method (IDM) in Power Systems with DG based on statistical signal processing. We used a MathWorks Simulink model of a grid-connected 250 kW photovoltaic (PV) array to simulate the behavior of the three-phase voltage signal in the point of common coupling (PCC) under the nominal operation, islanding condition, and fault condition using different load compositions. Principal Component Analysis (PCA) was used to extract the transitory events from the voltage signals, and then we used second-, third-, and fourth-order cumulants to generate features and the best ones were selected using the Fisher’s Discriminant Ratio (FDR). A Radial Basis Function Network (RBFN) makes the classification of the events. We found that, for this setup, we can achieve detection rates of 99% for both islanding condition detection and fault occurrence classification, no matter the power mismatch between the load and the DG.

복합에너지 프로슈머를 위한 동시냉난방활용 열에너지공급 시스템 적용성 분석

In this study, decentralized prosumers configuring a renewable thermal and electric convergence energy system for energy sharing community were proposed. This paper investigated energy performance and operating energy savings of integrating the decentralized convergence energy system with centralized thermal network. For the case study, a smart village located in Busan Eco Delta Smart City was selected for this analysis via detailed simulation. Major thermal energy for cooling, heating, and domestic hot water was produced by ground source heat pumps in centralized system. Decentralized prosumer had water-to-water heat pump for simultaneous production of cooling and heating water. As a result of this analysis, it was found that the proposed system could save 13.5% of operating energy to serve cooling and hot water energy compared with a ground-source heat pump system.

Experimental Performance and Cost-Effectiveness of a Combined Heating System under Saharan Climate

The solar water heater can be integrated into future residential buildings as the main energy source, which will subsequently reduce the energy cost of water heating. An original configuration for an efficient Domestic Hot Water (DHW) storage tank is developed and experimentally evaluated under Saharan climate. This novel DHW configuration includes a hybrid (solar and electric) energy system with a flat plate solar collector coupled with an electric heater. Additionally, a phase change material (PCM) mixture that is composed of paraffin wax and animal fat with a melting temperature between 35.58 °C and 62.58 °C and latent heat between 180 and 210 kJ/kg is integrated into this novel tank configuration. The experimental results indicated that hot water production by using latent heat storage could be economically attractive. By evaluating the cost of water heating expressed in Algerian dinar per liter (DZD/L), it was found that one liter of hot water may cost around 0.1362 DZD/L (i.e., 0.00096 USD/L) compared to 0.4431 DZD/L for the conventional water heater, an average energy cost savings of 69.26%. On a yearly basis, the average energy cost savings may reach up to 80.25% if optimal tilt for the solar collector is adopted on a monthly basis. The flat plate collector may be vulnerable to convective heat transfer; therefore, other solar collectors, such as vacuum tube collectors, may provide enhanced energy performance.

Scheduling Optimization of IEHS with Uncertainty of Wind Power and Operation Mode of CCP

With the gradual depletion of fossil energy sources and the improvement in environmental protection attention, efficient use of energy and reduction in carbon emissions have become urgent issues. The integrated electricity and heating energy system (IEHS) is a significant solution to reduce the proportion of fossil fuel and carbon emissions. In this paper, a stochastic optimization model of the IEHS considering the uncertainty of wind power (WP) output and carbon capture power plants (CCPs) is proposed. The WP output in the IEHS is represented by stochastic scenarios, and the scenarios are reduced by fast scenario reduction to obtain typical scenarios. Then, the conventional thermal power plants are modified with CCPs, and the CCPs are equipped with flue gas bypass systems and solution storage to form the integrated and flexible operation mode of CCPs. Furthermore, based on the different load demand responses (DRs) in the IEHS, the optimization model of the IEHS with a CCP is constructed. Finally, the results show that with the proposed optimization model and shunt-type CCP, the integrated operation approach allows for a better reduction in carbon capture costs and carbon emissions.

Methodology Presentation for the Configuration Optimization of Hybrid Electrical Energy Systems

The paper presents the methodology that should be used to optimize hybrid power system configurations, i.e., the chosen microgrid scenario. The methodology was developed with the aim of evaluating the different production capacities of a system and comparing them with alternative connections to the main grid. It has a step-by-step structure and includes part of the steps performed by simulations in a computer program. It provides us with not only a basic but also a representative idea of an actual microgrid configuration, which is further used as support in the system’s design. An algorithm has been developed to check the energy flows and correct the selling price. The program HOMER Pro is used as the program support in performing the hybrid optimization simulation. In the analysis, it was found that the maximum share of generation from renewable energy sources in relation to the power grid is 143%, while the share of renewable energy sources in the generated energy is 65.3%. Greenhouse gas emissions are reduced by 40.75% compared to energy generated only from the power grid. In the optimization process, four configurations of the microgrid model with 990 solutions were taken, all of which are feasible.

Reverse Power Protection of an Alternator

Power plant generators are important components of an electrical energy system. They should be constantly monitored and protected in order to maintain the quality and reliability of the power supply. Otherwise generator may operate incorrectly resulting in poor power quality and instability during events like faults, equipment failure, reverse power flow(RPF) etc., the presence of large distributed energy resources is the major cause to the reverse power flow during low loads/excess generation at distribution level. If reverse power flow (RPF) is not resolved, it leads to incorrect operation of generators or affect connected devices. Therefore it is necessary to monitor and prevent reverse powerflow condition.This paper dealswith reversepower flow protection, here hardware model representing generator connected to the grid is developed using low rating permanent magnet motors, sensors, PIC16F877A microcontroller, regulated power supply, relay driver, Liquid Crystal display (LCD) and relay, to monitor and protect generator from RPF. Key Words: Reverse power flow, Permanent magnet motors, PIC16F877A Microcontroller, Changeover & protection relay.

Cooley-Tukey FFT Algorithm based on GDFT for Phasor Estimation by PMU under Power Quality Disturbances

Due to the high penetration of renewable energy sources, such as photovoltaic panels and wind turbines, in addition to the use of different electric power supplies in the power grid, there are major disturbances in the forms of electric waves. These variations and disturbances must be monitored and controlled for the efficient management of transmission and distribution of electrical energy, safety, and electrical protection systems. Nowadays, Phasor Measurement Unit (PMU) technology is an essential tool to develop the supervision, protection, and control of the electrical power grid. PMUs measure the amplitude and angle of current and voltage waveforms on a Coordinated Universal Time (UTC) time scale and speedily measure the fundamental frequencies and their rates of change using fast and accurate estimation algorithms. This paper presents a phasor estimation using a Modified Cooley-Tukey Fast Fourier Transform algorithm based on the Generalized Discrete Fourier Transform (DFT) used in PMUs, using simulations in MATLAB. This algorithm was utilized to accelerate and simplify the computation of DFTs. To validate the performance under waveform disturbances, several tests with different waveforms and disturbances were simulated and interpreted according to the standard and compared with DFT.