Power system flexibility analysis using net-load forecasting based on deep learning considering distributed energy sources and electric vehicles

RETRACTED: Stochastic scheduling of a power grid in the presence of EVs, RESs, and risk index with a developed lightning search algorithm

A new hybrid decomposition-based multiobjective evolutionary algorithm is proposed for optimal power flow (OPF) including wind and solar generation uncertainty. This study recommends a novel constraint-handling method, which adaptively adds the penalty function and eliminates the parameter dependency on penalty function evaluation. The summation-based sorting and improved diversified selection methods are utilized to enhance the diversity of multiobjective optimization algorithms. The OPF problem is modeled as a multiobjective optimization problem with four objectives such as minimizing (i) total fuel cost (TC) including the cost of renewable energy source (RES), (ii) total emission (TE), (iii) active power loss (APL), and (iv) voltage magnitude deviation (VMD). The impact of RESs such as wind and solar energy sources on integration is considered in optimal power flow cost analysis. The costs of RESs are considered in the OPF problem to minimize the overall cost so that the impact of intermittence and uncertainty of renewable sources is studied in terms of cost and operation wise. The uncertainty of wind and solar energy sources is described using probability distribution functions (PDFs) such as Weibull and lognormal distributions. The efficiency of the algorithm is tested on IEEE 30-, IEEE 57-, and IEEE 118-bus systems for all possible conditions of renewable sources using Monte Carlo simulations.

Modern power systems which integrate renewable energy sources (RESs), such as wind or solar energy sources and plug-in electric vehicles (PEVs), need to carry out the uncertainty due to the access of demanded or injected power. Therefore, it is necessary to consider uncertainty costs in optimal power flow problems. To develop the uncertainty cost functions, log-normal probability distribution function (PDF) for solar irradiance, normal PDF for unloading and loading behavior of PEVs, and Rayleigh PDF for wind speed are considered. In this work, a dynamic optimal power flow (DOPF) model is formulated by considering the uncertainties of RESs and PEVs. A newly hybrid metaheuristic algorithm named cross entropy (CE) covariance matrix adaption evolutionary strategy (CMAES) is proposed to evaluate the DOPF problem in a modified IEEE 118-bus system. The application of adaptive step length in the CMAES method improves the local exploitation capability, while the CE method is used in the initial stage for global exploration due to its fast convergence. Simulation results indicate that the proposed technique can solve the DOPF problems with RESs and PEVs effectively and can provide high-quality solutions compared to different techniques. The conventional statistical method called ANOVA test is performed for comparative analysis of different techniques. The results of this test show the validation of CE-CMAES compared to different optimization techniques. Therefore, the proposed method could be easily and efficiently applied by power system operators in real-time optimal power flow operations.

The solar and wind renewable energy sources are gaining popularity to encourage green energy into the power system. The cost of generation of solar and wind energy sources are decreasing and competing with conventional coal-based generation. Therefore, it is very important to integrate these renewable sources into the power system. Integrating Solar and wind energy sources require to solve the uncertainty problem. Both the solar and wind energy generation is uncertain and not controllable. In this paper, sliding window optimal ARIMA forecasting algorithm is proposed to solve the uncertainty associated with solar and wind sources. The proposed forecasting method is used on the data collected from National Renewable Energy Laboratory website.

This article investigated the charge and discharge management structure of electric vehicles (EVs) in intelligent parking lots (IPLs). It seems that with the expansion of renewable energy sources (RESs) as clean energy and investigation of the effects of EVs on the operation and planning of future distribution networks around the way EVs exchange energy with each other and the upstream network operator, RESs such as solar and wind sources, along with hydrogen storage are essential. Therefore, a new stochastic multi‐scenario approach for charge/discharge management of EVs parked in IPL was proposed, in which a newly developed model for the IPL with a hydrogen storage system (HSS) consisting of a fuel cell, an electrolyzer, and a hydrogen storage tank was presented. In the proposed model, the constraints of upstream network and power balance constraints, with the operating constraints related to the IPL, including EVs, renewable energy sources, and the hydrogen storage system, were formulated as the most important objectives of the optimization problem. The optimization algorithm, Competitive Swarm Optimizer (CSO), was formulated for implementation, and its results were compared with those of the PSO and GWO algorithms. The CSO must handle a variety of practical, large‐scale optimization problems. Based on the obtained results, the excess energy purchased from the upstream network or renewable sources was used as hydrogen storage for consumption during peak hours. As expected, the technical constraints and financial goals of the system were met, and the proposed system was evaluated on a 33‐bus network. As the expected benefits will be the most beneficial with the presence of renewable sources, the final profit will be reduced by taking into account uncertainty for charge/discharge management in EVs such as the uncertainty of electric load, market price, wind turbine, and photovoltaic cell sources. Nonetheless, the profit obtained from renewable resources is preferable to losses resulting from the uncertainty of the system, and according to expectation, the performance of the system for managing the optimal charging and discharging of EVs in the IPL will be acceptable with the maximum profit for the grid.

Summary. With the continuous expansion of grid-connected power generation from renewable energy sources, such as wind and light, the forms of power sources are more diversified. The coupling and uncertainty of multiple energy sources will cause significant changes in the characteristics of the power system. Problems like the complexity of the power system lack a scientific and complete explanation. This paper comprehensively considers the regional weather conditions, the uncertainty of the output of wind and solar energy sources, and the time and space complementary characteristics of multiple power sources. Based on the theory of complex adaptive systems, a systematic and holistic optimization planning method is studied. Combining the idea of complementarity because of differences, a multipoint layout planning and design method for multienergy power systems is proposed to give full play to the synergistic effects of natural resources, achieve optimal resource allocation, and provide a theoretical basis for improving the consumption of wind and renewable energy. Taking an actual provincial power system in China as an example, through the comparison of production simulation calculations and multiscenario analysis and planning methods, the proposed method can significantly increase the consumption of wind and solar renewable energy.

The global market of electric vehicles has become one of the prime growth industries of the 21st century fueled by marketing efforts, which frequently assert that electric vehicles are “very efficient” and “produce no pollution.” This article uses thermodynamic analysis to determine the primary energy needs for the propulsion of electric vehicles and applies the energy/exergy trade-offs between hydrocarbons and electricity propulsion of road vehicles. The well-to-wheels efficiency of electric vehicles is comparable to that of vehicles with internal combustion engines. Heat transfer to or from the cabin of the vehicle is calculated to determine the additional energy for heating and air-conditioning needs, which must be supplied by the battery, and the reduction of the range of the vehicle. The article also determines the advantages of using fleets of electric vehicles to offset the problems of the “duck curve” that are caused by the higher utilization of wind and solar energy sources. The effects of the substitution of internal combustion road vehicles with electric vehicles on carbon dioxide emission avoidance are also examined for several national electricity grids. It is determined that grids, which use a high fraction of coal as their primary energy source, will actually increase the carbon dioxide emissions; while grids that use a high fraction of renewables and nuclear energy will significantly decrease their carbon dioxide emissions. Globally, the carbon dioxide emissions will decrease by approximately 16% with the introduction of electric vehicles.

Attention paid to energy efficiency in datacenters has been increasing significantly in the last decade. One of the latest trends on this issue is supplying datacenters with renewable energy, and reducing the carbon footprint of datacenters is a major step towards green computing. Following this trend, more and more datacenters are built in cold climate areas for cheaper cooling and increased energy efficiency of the facilities. However, such geographical locations have a very varying availability of renewable energy (especially solar energy), depending on the annual month and fitting the datacenters completely with renewable energy is hence more difficult. This paper analyzes the feasibility of using renewable energies for a datacenter located on the 60° latitude north. We analyze the energy consumption caused by the server workload and the cost-to-energy trade-off related to available wind and solar energy sources. A wind and solar power model is built based on real weather data for the city of Turku, Finland, and a feasibility study is conducted for fitting a datacenter with renewable energy sources on such a location. The available monthly and annual renewable energy is analyzed for different scenarios and compared with the energy consumption of a simulated datacenter. We also analyze the ratio between number of wind turbines and m2 solar panel to achieve a desired fraction of renewable energy sources for a given datacenter.

This study employed the novel Dynamic Autoregressive Distributed Lag (DYARDL) model to analyze the impact of renewable energy consumption, green technological innovation and financial development on CO2 emissions in Nigeria. Furthermore, Fully Modified Ordinary Least Square (FMOLS) and Canonical Cointegrating Regression (CCR) approaches are employed for robustness checks. The DYARDL results indicate that green technological innovation, renewable energy consumption and financial development respectively, promote environmental sustainability in the longrun, whereas, in the shortrun, green technological innovation is negatively related to the environmental quality in Nigeria. However, trade openness contributes towards environmental degradation both in the shortrun and the long run. The result of the Dynamic ARDL in this study is in conformity with the findings from both the FMOLS and CCR models. The study therefore, recommends that in order to achieve the target set by Nigeria Renewable Energy Master Plan (REMP) of generating 36% of its electricity from renewable energy sources by 2030, with a primary focus on hydro, Biomass-based, wind and solar energy sources, government should provide fiscal incentives to private investors in form of tax holidays, capital incentives, moratorium on import duties and loan opportunities for renewable energy projects in the country. Also, policymakers should strengthen policies that encourage green technological investments, in order to promote economic growth while also further reducing environmental emissions.

Multi-objective optimal power flow of thermal-wind-solar power system using an adaptive geometry estimation based multi-objective differential evolution

The increase in energy demand due to the deployment of conventional sources makes shift towards Renewable Energy Sources. As Renewable energy cannot be tapped for continuous time, it is very convenient to add another source of energy to solar and wind energy sources. RES when added with other temporary sources of energy is called Hybrid Energy Source System. Hybrid System helps in meeting the peak demand during peak hours by using load generated at that point as main supply and the other source as secondary source. In this paper two renewable energy sources like Solar and Biomass energy is combined to form Hybrid Energy Source. In India solar energy is available abundantly and in villages where agriculture is the main source of income creates Biomass. For a particular region both these energy resources are combined and studied in this paper. The advantages of combining both the plants and the applications, usability of these sources depending upon the peak hours and the off peak hours are studied in this paper. These combinations helps us to reduce pollution, as garbage obtained from the agriculture plants will be utilized to generate power and also free availability of carbon free solar energy.

Abstract: This article introduces hybrid Arithmetic Optimization Algorithm (AOA) and Local Unimodal Sampling (hAOA-LUS)-based fractional order (FO) proportional derivative (PD) cascaded with one plus proportional integral (1+PI) controller for automatic generation control of power system with renewable energy sources (RES) and electric vehicle (EV). The control-area 1 has thermal, hydro, gas, and wind power generators and the same true for control area 2, which uses thermal, hydro, gas, and solar energy sources. Initially, Proportional-Integral-Derivative (PID) controllers are taken into consideration and it has been demonstrated that hAOA-LUS outperforms as compared to the AOA, Particle Swarm Optimization, and Generic Algorithm (GA). The assessing of overshoots, undershoots, and different integral errors of frequency and tie-line power deviations after step load perturbations in each area allows for performance comparison of the proposed power system. In the next stage, EVs are considered in each area and the controller parameters are optimized by hAOA-LUS techniques in the presence of RES and EV. A comparative analysis is carried out by hAOA-LUS-tuned FO PD(1+PI) controller with PID as well integer ordered PD(1+PI) for various cases so as to validate the superiority of the anticipated controller. The results from MATLAB and OPAL-RT are compared in order to verify the authenticate feasibility of method. Cite this article as: A. Kumar Baliarsingh, S. “Keshori Mohapatra and P. Mohan Dash, Fractional order PD(1+ PI) controller for frequency control of power system with renewable sources and electric vehicle,” Electrica, 24(2), 406-424, 2024.

The access to electricity is one of the major problems faced by rural areas and isolated regions. Over the years, the fossil-fueled power stations used to generate electricity had a detrimental effects and it has necessitated the need for alternative energy sources such as solar, wind, biomass and other renewable energy sources. Hybrid energy system (HES) based on distributed energy resources (DERs) integration into the DC microgrid is considered as the viable means to carry out the electrification of rural as well as isolated area. However, DC microgrids are limited by the occurrence of faults and this affect their performance. This paper presents the modelling and simulation of power flow and the fault analysis in a hybrid DC microgrid. This hybrid DC microgrid is powered by PV and wind energy system, the formulation, modeling and simulations was implemented in the MATLAB/SIMULINK. The faults analyzed in the system are the DC line-to-ground faults and DC line-to-line faults. Results for a hybrid DC microgrid revealed that high quality of power is experienced in load distribution. Also based on the results, when DC faults occurs there is disturbance to output.

Preserving perishable foods, particularly fish, demands meticulous attention throughout the entire supply chain, from capture to consumption. The critical role of cold storage facilities in maintaining optimal conditions, encompassing temperature and humidity regulation, is indispensable. Nevertheless, these facilities pose a considerable challenge due to their substantial electrical energy consumption, which significantly contributes to operational costs. In addressing this challenge, the implementation of energy-efficient strategies, including the utilization of advanced equipment and real-time monitoring facilitated by automation technologies, becomes imperative. This research investigates an innovative integrated cold storage system situated in Tarrafal, Santiago, Cape Verde, which harmoniously incorporates wind, solar, and tidal energy sources. The primary objectives are to evaluate the energy efficiency, financial viability, and environmental impact across four distinct hypothesis. Hypothesis 1 entails the deployment of two R134a refrigeration units powered by the conventional public grid, while Hypothesis 2 adopts a transcritical CO2 system reliant on grid electricity, Hypothesis 3 integrates CO2 technology with autonomous renewable energy sources and Hypothesis 4 leverages CO2 for refrigeration coupled with seawater heat exchange and autonomous renewable energy generation. The research findings strongly favor Hypothesis 4, demonstrating emissions of 15880 kg CO2eq with an impressive 5-year return on investment. The autonomous electricity production associated with this hypothesis markedly reduces emissions by more than 90%. Despite an initial higher investment, Hypothesis 3 showcases financial viability, contributing significantly to long-term energy sustainability. The autonomous energy production in this scenario results in a noteworthy reduction of more than 600000 kgCO2 compared to conventional systems, underscoring the positive impact of integrating local renewable energy sources.

Available Transfer Capability (ATC) determination, accounting for the stochastic effects of uncertain sources and loads, is of key significance for the safe and economic operation of a power system, especially in a competitive market environment. In this paper, a two-stage risk-adjusted stochastic optimal power dispatch problem is proposed. The reduction in ATC due to active power uncertainties in renewable energy sources and also the load is taken as the risk. The problem considers a combined transmission-distribution system (TDS) with solar, wind and other conventional energy sources. The ATC is estimated using Continuation power flow (CPF) and power transfer sensitivity factor (PTSF) methods. The joint probability distribution function (PDF) of ATC is derived using individual discrete PDFs of random variables (renewable power generation and loads). The risk is minimized using stochastic weight trade-off particle swarm optimization (SWT-PSO). The proposed approach helps to hedge the financial risk of the network/transmission system owner/operator.