Renewable Energy Resources Generation Research Articles

Impact of electric vehicles and wave energy systems on OPF of power networks using hybrid Osprey-PSO approach

In this article, a novel optimization algorithm named hybrid Osprey Optimization and Particle Swarm Optimization (OOPSO) is proposed to solve the Probabilistic Optimal Power Flow (POPF) problem in power systems with high penetration of renewable energy resources (RERs), such as photovoltaic, wind, and wave energy and integration of electric vehicles (EVs). The algorithm's results are compared with established optimization algorithms like Osprey Optimization Algorithm (OOA), Particle Swarm Optimization (PSO), and Heap Optimization. Applied to the 30 and 118-bus IEEE test systems, the OOPSO algorithm efficiently deals with the uncertainties produced by RERs generation. This research study includes EV charging profiles for weekdays and weekends. It considers home and work-level charging scenarios. The RERs and EVs penetration ensure system reliability while minimizing generation costs and satisfying power flow constraints. Compared to existing algorithms, the OOPSO algorithm achieves superior performance, faster convergence, and higher solution accuracy. The simulation results show the effectiveness of OOPSO. It appears to be a robust tool for POPF, integrating RERs in power systems with EVs. This work highlights the potential of OOPSO as a feasible approach for power system optimization in the context of RERs and EV integration, paving the way for further research into unconventional grid optimization techniques.

Multivariate simulation‐based forecasting for intraday power markets: Modeling cross‐product price effects

AbstractIntraday electricity markets play an increasingly important role in balancing the intermittent generation of renewable energy resources, which creates a need for accurate probabilistic price forecasts. However, research to date has focused on univariate approaches, while in many European intraday electricity markets all delivery periods are traded in parallel. Thus, the dependency structure between different traded products and the corresponding cross‐product effects cannot be ignored. We aim to fill this gap in the literature by using copulas to model the high‐dimensional intraday price return vector. We model the marginal distribution as a zero‐inflated Johnson's distribution with location, scale, and shape parameters that depend on market and fundamental data. The dependence structure is modeled using copulas, accounting for the particular market structure of the intraday electricity market, such as overlapping but independent trading sessions for different delivery days and allowing the dependence parameter to be time‐varying. We validate our approach in a simulation study for the German intraday electricity market and find that modeling the dependence structure improves the forecasting performance. Additionally, we shed light on the impact of the single intraday coupling on the trading activity and price distribution and interpret our results in light of the market efficiency hypothesis. The approach is directly applicable to other European electricity markets.

Advanced control strategies to manage electric vehicle drivetrain battery health for Vehicle-to-X applications

The demand for local power resilience is promoting the installation of renewable-based microgrids. Plug-in electric vehicles (PEVs) are rapidly increasing and can play a critical role in microgrid service provision. The capability of bi-directional charging and discharging will transition PEVs from a simple means of transportation to multi-purpose value generating assets. They can balance the inherently fluctuating generation of renewable energy resources and shape customer’s electricity demand, while still providing the core service of transportation. Value stacking through those additional services allows PEV owners to draw from a number of revenue streams to offset investment costs. However, accelerated battery degradation is often cited as concern when using PEV batteries for purposes other than driving, which presents a major barrier for broad market adoption. This study seeks to assess the economics and trade-offs of bi-directional use of PEVs when utilized as fleet vehicles. In particular, the usage patterns and benefits for PEV fleet deployment at three U.S. military bases is presented. The results show operational cost benefits for bi-directional application of up to 60.8 % annual return on investment without managing battery health, and up to 106.0 % when actively managing battery health through advanced control strategies. The one year payback period far exceeds the expected installation lifespan of at least ten years. Given the results, utilizing PEV fleets for additional services at U.S. military bases can largely offset the additional cost of bi-directional charging hardware and software compared to standard uni-directional equipment.

Reinforcing Data Integrity in Renewable Hybrid AC-DC Microgrids from Social-Economic Perspectives

The microgrid (MG) is a complicated cyber-physical system that operates based on interactions between physical processes and computational components, which make it vulnerable to varied cyber-attacks. In this article, the impact of data integrity attack (DIA) has been considered, as one of the most dangerous cyber threats to MGs, on the steady-state operation of hybrid microgrids (HMGs). Additionally, a novel method based on the sequential hypothesis testing approach is proposed to detect DIA on the renewable energy sources’ metering infrastructure and improve the data security within the HMGs. The proposed method generates a binary sample, which is used to compute a test statistic that is further used against two thresholds to decide among three alternatives. The performance of the suggested method is examined using an IEEE standard test system. The results illustrated the acceptable performance of the proposed methodology in detection of DIAs. In addition, to evaluate the effect of DIA on the operation of the HMGs, DIAs with different severities are launched on the measured power generation of renewable energy resources like wind turbines. The results showed that a successful DIA on renewable units can severely affect the operation of electric grids and cause serious damage.

Machine learning tools for active distribution grid fault diagnosis

Faults in power distribution networks cause customer minute and economic losses. A crucial part of the protection system of such grids is effective fault diagnosis for the acceleration of the restoration process after being subjected to any faults. This article presents the fault diagnosis approach for the active distribution grid, and the method consists of machine learning tools and signal processing techniques. The Hilbert-Huang transform (HHT) and discrete wavelet transform (DWT) are considered as the signal processing tools whereas the feedforward neural networks (FFNN) are considered as the machine learning tools. The extracted features using the signal processing tools are fetched into the neural network models for the development of fault detection, fault classification, and either fault location or faulty section identification models. The proposed approach is tested on two different distribution feeders. The first one is a simplified four-node test feeder modeled in MATLAB/SIMULINK environment. In contrast, the second one is the IEEE 13-node distribution network with the incorporation of three renewable energy resources (RER) modeled in the Real-Time Digital Simulator (RTDS) machine. The uncertainty, e.g., RER generation, load demand, and fault information, associated with the test feeders are modeled using different probability density functions. Obtained results demonstrate the efficacy of the proposed models for both noise-free and noisy data. Finally, the developed models show their independence in the variation of the pre-fault loading conditions, fault inception angle, and fault resistance.

Guest Editorial: Situational awareness of integrated energy systems

Guest Editorial: Situational awareness of integrated energy systems

Hybrid‐adaptive differential evolution with decay function applied to transmission network expansion planning with renewable energy resources generation

As the country implements the reform of the electricity market and a large number of renewable energy resources (RES) are connected to the grid, the model and algorithm of traditional transmission network expansion planning (TNEP) are not suitable. A model of TNEP is built, in which the investment cost of new lines and the market-based annual congestion surplus are selected as objective functions. A probabilistic DC power flow method is used to describe uncertainties of RES generation. A new algorithm, Hybrid-Adaptive Differential Evolution with Decay Function (HyDE-DF), is applied to solve the model of TNEP for the first time. Analysis and comparison in the IEEE 18-bus system and the 52-bus system in an area of Sichuan Province prove the feasibility, effectivity, and practicability of the model and solving algorithm. Some comparisons are performed using other variants of Differential Evolution (DE) and a swarm intelligence optimization algorithm to demonstrate the performance superiority of the new algorithm and new application. It is worth mentioning that in the 52-bus system of an area of Sichuan Province, the investment cost obtained by the new algorithm is at least one or two orders of magnitude lower than other algorithms.

Performance Analysis of the Microgrid System for an Educational Institute by Using ETAP Software

Integration of renewable energy into power grid systems by the use of microgrid are becoming attractive to supply electricity to remote areas in all aspects like reliability, sustainability, and environmental concerns; especially for communities living far in areas where grid extension is difficult so generation of renewable energy resources like solar energy to provide reliable power supply with improved system efficiency and significant cost reduction is best way. This paper reports the ETAP model of a conventional grid connected to Microgrid system of an educational Institute. The purpose of this work is to study the prototype model which is been designed according to some standard electrical conditions. This paper also provides the readers the understanding about the selection appropriate protective equipments, proper cable sizing and electrical parameters which results in a energy efficient design. In this paper we have considered an educational Institute which is connected to 33/0.4 kV conventional grid supply and to this system a 50 kW microgrid is connected.

Deep Reinforcement Learning for Economic Dispatch of Virtual Power Plant in Internet of Energy

With the high penetration of large-scale distributed renewable energy generation, the power system is facing enormous challenges in terms of the inherent uncertainty of power generation of renewable energy resources. In this regard, virtual power plants (VPPs) can play a crucial role in integrating a large number of distributed generation units (DGs) more effectively to improve the stability of the power systems. Due to the uncertainty and nonlinear characteristics of DGs, reliable economic dispatch in VPPs requires timely and reliable communication between DGs, and between the generation side and the load side. The online economic dispatch optimizes the cost of VPPs. In this article, we propose a deep reinforcement learning (DRL) algorithm for the optimal online economic dispatch strategy in VPPs. By utilizing DRL, our proposed algorithm reduced the computational complexity while also incorporating large and continuous state space due to the stochastic characteristics of distributed power generation. We further design an edge computing framework to handle the stochastic and large-state space characteristics of VPPs. The DRL-based real-time economic dispatch algorithm is executed online. We utilize real meteorological and load data to analyze and validate the performance of our proposed algorithm. The experimental results show that our proposed DRL-based algorithm can successfully learn the characteristics of DGs and industrial user demands. It can learn to choose actions to minimize the cost of VPPs. Compared with the deterministic policy gradient algorithm and DDPG, our proposed method has lower time complexity.

Two-stage stochastic optimization for cost-minimal charging of electric vehicles at public charging stations with photovoltaics

Electric vehicles are one promising technology towards an improved sustainable transportation sector, especially when charged with electricity from renewable energy sources. However, the fluctuating generation of renewable energy resources, as well as the changing driving patterns of electric vehicles, have the offset of an uncertain nature. This paper compares three approaches (heuristic, optimization, and stochastic programming) to schedule the charging process of three different electric vehicles fleets (commuters, opportunity, and commercial fleets) at a common charging infrastructure under uncertainty. In the setting of a car park case study, several technical restrictions are taken into consideration when the load shift potential of the electric vehicles fleets are evaluated in order to minimize charging costs or to maximize the utilization of generated electricity by local photovoltaic. The two-stage Stochastic Mixed Integer Optimization Problem is solved by a Latin Hypercube based Sample Average Approximation method. Uncertainties of electricity generation by the photovoltaic system are considered by three different forecasting options and the mobility characteristics of the three electric vehicles fleets are modeled with a non-parametric probability density function (Kernel Density Estimation). The differences in charging costs and utilization of electricity from photovoltaic when applying the three approaches are identified and discussed. The numerical results show the feasibility to charge different electric vehicle fleets in a car park according to different signals and taking thereby technical restrictions as well as uncertainties into consideration. An operator for facilitation of the charging control is needed to enable the load flexibilities of each electric vehicle fleet.

Optimal Operation of Micro-grids Considering the Uncertainties of Demand and Renewable Energy Resources Generation

Nowadays, due to technical and economic reasons, the distributed generation (DG) units are widely connected to the low and medium voltage network and created a new structure called micro-grid. Renewable energies (especially wind and solar) based DGs are one of the most important generations units among DG units. Because of stochastic behavior of these resources, the optimum and safe management and operation of micro-grids has become one of the research priorities for researchers. So, in this study, the optimal operation of a typical micro-grid is investigated in order to maximize the penetration of renewable energy sources with the lowest operation cost with respect to the limitations for the load supply and the distributed generation resources. The understudy micro-grid consists of diesel generator, battery, wind turbines and photovoltaic panels. The objective function comprises of fuel cost, start-up cost, spinning reserve cost, power purchasing cost from the upstream grid and the sales revenue of the power to the upstream grid. In this paper, the uncertainties of demand, wind speed and solar radiation are considered and the optimization will be made by using the GAMS software and mixed integer planning method (MIP).Article History: Received May 21, 2016; Received in revised form July 11, 2016; Accepted October 15, 2016; Available onlineHow to Cite This Article: Jasemi, M., Adabi, F., Mozafari, B., and Salahi, S. (2016) Optimal Operation of Micro-grids Considering the Uncertainties of Demand and Renewable Energy Resources Generation, Int. Journal of Renewable Energy Development, 5(3),233-248.http://dx.doi.org/10.14710/ijred.5.3.233-248

Climate Change Resilient Crops to Sustain Indian Agriculture

The agriculture sector in India is susceptible to the frequent and erratic climatic irregularities. Anticipated changes in the climate due to intensive anthropogenic activities may cause worst conditions. Models of crop production, considered together with global climate models, indicated that global warming would increase the exposure of major crops to temperature stress, leading ultimately to lower crop yields. Such decrease in yields varied significantly across different states of the country, and there remained significant uncertainties about their magnitude. Various studies also indicated that climatic variability alone had the potential to decrease yields to an extent comparable with or greater than the decrease in yields expected due to rising mean temperatures. The climatic changes led to increase in temperature and water stress, which caused heavy losses in yield of any crop. It may be a big challenge for the agriculture scientists and country leaders to feed the coming future Indian population sufficiently. Development of new climate resilient crop varieties, generation of renewable energy resources and use of green technologies might be helpful for mitigating the impact of climate change and sustained agriculture productivity in near future. To cope with intense changes as projected in climate, new research paradigm of bringing all stake holders is urgently required to establish suitable long-term strategies for addressing the Indian agriculture sustainability.

Optimal operation of a smart residential microgrid based on model predictive control by considering uncertainties and storage impacts

A model predictive control (MPC) based coordinated operation framework for a grid-connected residential microgrid with considering forecast errors is presented in this paper. This residential microgrid composes renewable energy resources (e.g., wind and solar), distributed generators (e.g., CHP), energy storages (e.g., battery bank and water tank), electrical vehicle, and smart loads (e.g. HVAC and washing machine). A novel mixed integer linear programming (MILP) problem is optimized at each decision time, on the basis of the short-term forecasts of renewable energy resources generation, load demand, and electricity price. This MILP problem is integrated into a MPC framework to reduce the negative impacts of forecast errors. Case study which considers forecast uncertainties is implemented for evaluating the performance of the proposed method and the traditional method is used. Besides, peak power price mechanism which is used to smooth the power exchanged with external grid is also considered. Moreover, a further sensitivity analysis is realized in order to discuss the impacts of energy storage units on the microgrid operation. Simulation results show that the proposed method is economic and flexible.