Renewable Generation Systems Research Articles

Optimal Economic and Environmental Aspects in Different Types of Loads via Modified Capuchin Algorithm for Standalone Hybrid Renewable Generation Systems

Greenhouse gas emissions have become a significant concern for many countries due to their effect on the global economy and environment. This work discusses a standalone hybrid renewable generation system (HRGS) for use in isolated areas with different load demand profiles. Three load profiles were studied in this work: educational, residential, and demand-side management (DSM)-based residential load profiles. To investigate the economic and environmental aspects, a proposed modified capuchin search algorithm (MCapSA) was implemented, and the obtained results were compared with those of different conventional optimal procedures, such as the genetic algorithm (GA), particle swarm optimization (PSO), and HOMER. The Levy flight distribution method, which is based on random movement, enhances the capuchin algorithm’s search capabilities. The cost of energy (CoE), electric source deficit (ESD), greenhouse gas (GHG) emissions, and renewable factor (RF) indicators were all optimized and estimated to emphasize the robustness of the proposed optimization technique. The results reveal that the shift in the residential load profile based on individual-household DSM-scale techniques leads to significant sharing of renewable sources and a reduction in the utilization of diesel generators, consequently diminishing GHG emissions. The proposed MCapSA achieved optimal values of economic and environmental aspects that are equal to or less than those achieved through PSO. From the overall results of the three scenarios, the modified algorithm gives the best solution in terms of GHG, COE, and ESD compared to other existing algorithms. The usage of MCapSA resulted in decreases in COE and GHG in three types of loads. The robustness and effectiveness of MCapSA are demonstrated by the fact that the DSM-based optimal configuration of the renewable energy sources produces the lowest CoE and GHG emissions of 0.106 USD/kWh and 137.2 kg, respectively.

A hybrid algorithm based on Bayesian optimization and Interior Point OPTimizer for optimal operation of energy conversion systems

Optimization methods are essential to improve the operation of energy conversion systems including energy storage equipment and fluctuating renewable energy. Modern systems consist of many components, operating in a wide range of conditions and governed by nonlinear balance equations. Consequently, identifying their optimal operation (e.g. minimizing operational costs) requires solving challenging optimization problems, with the global optimum often hidden behind many local ones. In this work, we propose a hybrid method that advantageously combines Bayesian optimization (BO) and Interior Point OPTimizer (IPOPT). The BO is a global approach exploiting Gaussian process regression to build a surrogate model of the cost function to be optimized, while IPOPT is a local approach using quasi-Newton updates. The proposed BO-IPOPT combination allows leveraging the parameter space exploration of the BO with the quasi-Newton convergence of IPOPT once solution candidates are in the neighborhood of an optimum. Using a challenging constrained test function, we test BO-IPOPT in accuracy and computational efficiency. Finally, we showcase the proposed method in the optimal operation of a renewable steam generation system. The results show that BO-IPOPT combines high accuracy and computational efficiency, achieving up to 50% better objective function values at the same CPU time than other state-of-the-art methods.

Ground-breaking approach to enabling fully solar renewable energy communities

Renewable Energy Communities are an important strategy of the EU, to promote and optimize the distributed deployment of renewable generation systems. At current incentives and costs, RECs that share the energy produced by PV/battery systems are expected to take the lion's share of distributed generation. In this paper, we highlight some important critical issues in electric system management that could result from the deployment of RECs if PV/battery system generation cannot meet the entire community demand. Thus, we show how a flexible PV system (PV/battery systems able to provide firm/dispatchable generation) can be cost-effectively dimensioned to supply 24/365 96%–97 % of the demand of a residential users' community. We demonstrate that flexible PV systems of 1/10 MWp with 1.2/12 MWh of storage capacity can supply communities of 142/1420 residential users at a least production cost of 0.12/0.096 €/kWh. We further provide two business models based on power purchase agreement to demonstrate that, at these production costs, fully solar RECs are currently techno-economically feasible and provide mutual benefits to both flexible PV producers and REC members. Suitable virtual corporate PPAs can reduce the electric bill of the REC members by 11%–5% without requiring any investments and increase the producer incomes by 18%–22 %.

Commercial EV fleet smart charging for cost reduction and renewables integration: A case study in Germany

The transition to electric mobility reduces transportation carbon emissions but presents challenges in integrating growing electricity demand with renewable electricity generation and power systems. Smart charging emerges as a key technology to minimize operational costs and support higher utilization of green electricity for electric vehicles (EVs). Market analysis indicates a trend towards dynamic electricity tariffs, incentivizing EV charging at times favourable to the electricity grid, yet existing literature fails to address smart charging technology in sync with times of lower electricity prices and higher renewables integration. This work examines different smart charging strategies for a commercial EV fleet in Germany, considering a Real-Time Pricing (RTP) tariff indexed to day-ahead market prices and renewable electricity integrations. HOMER Grid is used to model these smart charging strategies and generate new EV load profiles. Direct and indirect CO2 emissions and renewable electricity usage ratios are calculated based on detailed electricity grid and self consumption data. The resulting Levelized Cost of Energy (LCOE) is minimized, by adjusting solar PV and battery capacities. The adoption of an RTP tariff decreases the LCOE from €0.530 to €0.314 per kWh. A smart charging strategy designed to reduce peak demand and schedule charging at lower price times further reduces the LCOE to €0.283 per kWh. The lowest LCOE of €0.281 per kWh is achieved with 34.4 kWp of solar PV capacity and no batteries. The carbon impact is minimized with larger solar PV and battery capacities. The performed work indicates a favourable framework for EV charging integrators to maximize cost savings and renewable energy integration for an EV fleet charging use case.

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.

Robust power converters for renewable generation systems in future networks with short circuit ratio wide variations

The European Directive (EU) 2018/2001 requests the connection of a new renewable inverter-based resources (RIR) but ensuring that the connection point is strong enough to minimize and avoid possible interaction phenomena. Historically, the indicator used to determine the strength of a network has been the Short Circuit Ratio (SCR). Until now, the tendency had been to ensure that the network remained strong enough with High SCR, but another possibility is to ensure that RIR can operate stably and reliably at different SCR values, both high and low. The inverters currently deployed in European power systems use grid following inverter technology (GFI). The main objective of the present work is the redesign of the traditional current regulators of JEMA Energy PV family inverters, based on grid following technology, to guarantee the stability of electrical systems with a variable short-circuit ratio in the range [1÷20]. To achieve this, the current loops of the inverter are redesigned, introducing an additional first-order controller that improves the dynamics of the system and allows stable and efficient behaviour. For validation purposes, the system is analysed under adverse fault scenarios. The results obtained in all cases are satisfactory, but the stability with PLL (phase lock loop) slower than those originally used, is better.

Gas emission optimization and optimal energy management of a microgrid operating by renewable and sustainable generation systems

The economic operation of electric energy generating systems is one of predominant problems in energy systems. Due to the need for better reliability, high energy quality, lower losses, lower cost and clean environment, the application of renewable and sustainable energy sources such as wind energy, solar energy, fuel cells, etc. in recent years has become more widespread mainly. In this work, one of most general of all swarm intelligence algorithms inspired by collective intelligent behavior of natural or artificial systems called the Particle Swarm Optimization (PSO) is applied to solve the Gas emission Optimization (GEO) and the Optimal Energy Management (OEM) problems of a micro-grid (MG) operating by Renewable and Sustainable Generation Systems (RSGS). Our main goal is to minimize nonlinear objective function of an electrical micro-grid taking into account of equality and inequality constraints. The PSO approach was examined and tested on a standard MG system composed of different types of RSGS such as wind turbines (WT), photovoltaic systems (PV), fuel cells (FC), micro turbine (MT), diesel generator (DEG) and loads with energy storage systems (ESS). The results are promising and show the effectiveness and robustness of proposed approach to solve the GEO and the OEM problems. The results of proposed method have been compared and validated with those known references published recently.

Research on power allocation strategy and capacity configuration of hybrid energy storage system based on double-layer variational modal decomposition and energy entropy

This paper deals with the study of the power allocation and capacity configuration problems of Hybrid Energy Storage Systems (HESS) and their potential use to handle wind and solar power fluctuation. A double-layer Variable Modal Decomposition (VMD) strategy is proposed. Firstly, using the Sparrow Search Algorithm with Sine-cosine and Cauchy mutation (SCSSA) to optimize the parameters in VMD. Considering the different characteristics of batteries and supercapacitors and the richness of information that may be contained in the high frequency residuals obtained from the primary VMD, the secondary VMD is carried out to decomposition. In response to the modal aliasing phenomenon of the secondary VMD, the high and low frequency demarcation points of the secondary VMD are discriminated by combining the energy entropy (eN) to enable the more accurate power allocation strategy. Secondly, a mathematical model for cost analysis of the HESS is established by considering the daily integrated operating cost of the HESS and the relevant constraints of the renewable generation system. Finally, HESS capacity configuration and validation are based on the power allocation strategy proposed in this paper. The simulation results show that the power allocation strategy proposed in this paper is more accurate and has a better economy for the capacity configuration of HESS.

Adaptive control strategy for isolated renewable energy-based generation system with intermittency

Adaptive control strategy for isolated renewable energy-based generation system with intermittency

COMPREHENSIVE CONDITION ANALYSIS AND PERSPECTIVES OF ENERGY DEVELOPMENT IN UKRAINE ACCORDING TO THE SMART GRID CONCEPT

The article examines development trends of the energy complex of Ukraine, possibility and prospects of creating a modern energy complex through the use of an innovative technical base for energy management in accordance with the Smart Grid concept. The general properties of Smart Grid technology and its main advantages are given by authors. It is shown that within the framework of the Smart Grid concept, the intelligent electric power system is considered as a single network of information and control systems. The process of electric power industry modernization in the direction of the creation of "smart" power supply networks in a number of European countries, which aims to ensure stable development, economic growth, growth of living standards and protection of the natural environment, is considered. The paper describes modernization methods of the electric power complex of Ukraine based on foreign experience. It is shown that from the point of view of energy security and sustainable development, Smart Grid is able to ensure the operation of the power grid even in case of damage or destruction of one segment, which is a key advantage during post-war reconstruction. They will also make it possible to effectively integrate renewable generation and energy storage systems into the grid, as well as provide auxiliary services for forecasting the power system operation. The paper analyzes the problematic issues of implementing the Smart Grid concept in Ukraine. They include the lack of technological solutions and methods that provide an effective algorithm for determining the state of power networks cyber security; large length of power distribution networks and insufficiently developed infrastructure. A comparative description of the existing energy systems functional properties and energy systems based on the Smart Grid concept is presented in general. An analysis of possible ways of electric power industry development was carried out, which showed the presence of serious limitations of electric power industry development within the framework of the former concept, based mainly on the improvement of certain types of equipment and technologies. It is shown that Ukraine is at the initial familiarization and formation stage of the first organizational initiatives for Smart Grid, namely the implementation of the government-approved Concept of the "smart grids" introduction in Ukraine for the period up to 2035. The purpose and expected results of this Concept implementation are considered.

A novel scenario generation method of renewable energy using improved VAEGAN with controllable interpretable features

With the high penetration of renewable generation systems in the power grid, the accurate simulation of the uncertainty in renewable energy generation is vital to the safe operation of the power system This paper proposes a novel controllable method for renewable scenario generation based on the improved VAEGAN model. The standard VAEGAN model is first improved using spectral normalization technique and the generator of GAN is trained using VAE. Then, the external and internal interpretable features in the latent space are learned as the controllable vector utilizing the principle of mutual information maximization. Finally, the renewable energy scenarios with overall features are generated using the external universal meteorological features, and renewable energy scenarios with specific features are generated by tuning along the internal interpretable feature of the controllable vector in the latent space. The proposed approach is used to produce real-time series data for renewable energy including wind and solar power. Experiments demonstrate that our method has a better performance in terms of controllable generation and enables the generation of preference patterns covering various statistical features.

Research on Grid-connected Voltage Improvement Technologies for High-penetration Photovoltaic Power Generation

The degradation in power quality due to the high penetration of grid-connected photovoltaic (PV) systems is a critical issue that urgently needs to be addressed in the field of renewable energy generation. Solar PV systems typically lack mechanical inertia, which means that they are unable to provide additional mechanical inertia to maintain voltage and frequency stability when the power system is subjected to external disturbances. Therefore, to ensure stable operation of the power system, alternative measures need to be relied on to maintain voltage stability. This article first analyzes the mechanism of voltage fluctuations when photovoltaic power generation is connected to the grid. It then delves into the challenges of power quality brought about by the grid integration of renewable distributed generation systems, as well as the current research status of mainstream voltage improvement technologies, customized power devices, and energy storage systems. Finally, the paper presents recommendations for improving grid voltage quality, enhancing the stability and reliability of the power system, and promoting the widespread application of renewable energy in power systems.

A Linked Swing Contract Market Design With High Renewable Penetration and Battery Firming

As renewable generation and energy storage systems become increasingly cost-effective, modern power systems are increasingly reliant on variable energy resources (VERs) to meet demand. However, the variability and uncertainty of VERs' generation require additional flexibility to mitigate their impacts. Unfortunately, the current power market designs rigidly define different power and reserve products, making it difficult to fully recognize and compensate for this flexibility. Furthermore, since the marginal operating costs of the system quickly approach zero, current marginal cost-based market mechanisms struggle to provide price signals that allow independent power producers to make informed decisions about constructing, operating, and retiring plants. To address these issues, this paper proposes a new linked electricity market design based on swing contracts for operating power systems with high penetration of VERs and energy storage resources. The feasibility of the proposed approach is demonstrated through study results on the modified IEEE 118-bus test system.

Integrating Firefly and Crow Algorithms for the Resilient Sizing and Siting of Renewable Distributed Generation Systems under Faulty Scenarios

This study aimed to optimize the sizing and allocation of renewable distributed generation (RDG) systems, with a focus on renewable sources, under N-1 faulty line conditions. The IEEE 30-bus power system benchmark served as a case study for us to analyze and enhance the reliability and quality of the power system in the presence of faults. The firefly algorithm (FFA) combined with the crow search (CS) optimizer was used to achieve optimal RDG sizing and allocation through solving the optimal power flow (OPF) under the most severe N-1 faulty line. The reason for hybridization lies in leveraging the global search capabilities of the CS optimizer for the sizing and allocation of RDGs and the local search proficiency of the FFA for OPF. Two severe N-1 faulty conditions—F27-29 and F27-30—were separately applied to the IEEE 30-bus distribution system. The most severe N-1 faulty line of these two faulty lines was F27-30, based on a severity ranking index including both the voltage deviation index and the overloading index. Three candidate buses, namely 27, 29, and 30, were considered in the optimization process. Our methodology incorporated techno-economic multi-objectives, encompassing overall costs, power losses, and voltage deviation. The optimizer can eliminate the impractical buses/solutions automatically while remaining the practical one. The results revealed that optimal RDG allocation at bus 30 effectively alleviated line overloading, ensuring compliance with the line flow limit, reducing costs, and enhancing voltage profiles, thereby improving system performance under N-1 faulty conditions compared to the equivalent case without RDGs.

Wind Energy Education through Low-Power Wind Turbines and Advanced Software Tools

Wind energy has become a major source of power generation in recent years. This fact, along with the growing expectations for future decades, makes the study of renewable generation systems based on wind energy a subject of great importance for engineers from different disciplines. Although there are numerous research articles that deal with the techno-economic aspects of this type of system, there are few works focused on the development of new didactic strategies to improve the academic excellence of undergraduate engineering students. This paper describes how to boost the student understanding regarding wind power generation by combining the use of advanced software tools normally used in the design of wind farms, such as System Advisor Model (National Renewable Energy Laboratory, NREL) and Wind Farm (RESOFT) with low power wind turbines operating in self-consumption and grid connected modes. Moreover, it is also described how wind turbines constitute an interesting option for distributed generation system in micro grids.

A resilient self‐healing approach for active distribution networks considering dynamic microgrid formation

AbstractA large number of renewable distributed generation (DG) systems connect to the distribution network, affecting the structure of the traditional distribution network and forming an active distribution network (ADN). Although the accelerating grid penetration of DGs brings significant challenges to the distribution network operation, the islanded operation capability of DGs provides a flexible solution to the self‐healing of ADNs from faults. To ensure that ADN can quickly recover and reconfigure in the event of a fault and continue to maintain safe, economical, and reliable operation, this paper proposes a dynamic microgrid formation method for ADNs combined with the dynamic network reconfiguration and intentional islanding operation of DGs. An optimization model is designed to represent the proposed self‐healing method, maximizing the load restoration and minimizing the DG cost, line network loss, and voltage excursion simultaneously. A binary hybrid optimization solver is applied to pursue the optimal self‐healing schedules from the optimization model. The self‐healing method is evaluated on the Institute of Electrical and Electronics Engineers (IEEE) 33‐node system and the IEEE 123‐node system, which indicate its rationality and effectiveness fully verified. By optimizing the on and off states of the normally open switches and the on‐grid and off‐grid operation states of DGs, ADNs not only get healed with minimum load curtailment, but also achieve minimal DG generation cost, network loss, and node voltage deviation. In addition, compared with traditional solvers, the proposed solver has a slightly higher computational time than the corresponding solver.

Optimal Design and Analysis of a Hybrid Hydrogen Energy Storage System for an Island-Based Renewable Energy Community

Installations of decentralised renewable energy systems (RES) are becoming increasing popular as governments introduce ambitious energy policies to curb emissions and slow surging energy costs. This work presents a novel model for optimal sizing for a decentralised renewable generation and hybrid storage system to create a renewable energy community (REC), developed in Python. The model implements photovoltaic (PV) solar and wind turbines combined with a hybrid battery and regenerative hydrogen fuel cell (RHFC). The electrical service demand was derived using real usage data from a rural island case study location. Cost remuneration was managed with an REC virtual trading layer, ensuring fair distribution among actors in accordance with the European RED(III) policy. A multi-objective genetic algorithm (GA) stochastically determines the system capacities such that the inherent trade-off relationship between project cost and decarbonisation can be observed. The optimal design resulted in a levelized cost of electricity (LCOE) of 0.15 EUR/kWh, reducing costs by over 50% compared with typical EU grid power, with a project internal rate of return (IRR) of 10.8%, simple return of 9.6%/year, and return on investment (ROI) of 9 years. The emissions output from grid-only use was reduced by 72% to 69 gCO2e/kWh. Further research of lifetime economics and additional revenue streams in combination with this work could provide a useful tool for users to quickly design and prototype future decentralised REC systems.

Simulation of the optimal plant size to produce renewable hydrogen based on the available electricity

Renewable hydrogen is emerging as a valid energy alternative to the fossil fuels currently in use. Governments are developing hydrogen-based energy strategies as a tool to decarbonise the transport, industrial, energy, and residential sectors. It is estimated that by 2050, hydrogen could cover 34% of the energy demand in these sectors in Europe according to the European Commission. However, for this to happen, green hydrogen must be produced on a large scale and in an economically feasible way.The aim of this project is to model any given renewable electricity generation system so that resources are optimized to achieve the highest and most cost-effective hydrogen production possible. Based on the green energy available data available and the characteristics of the electrolysers considered, the model minimises the cost per kilogram of hydrogen produced by optimising the size of the electrolyser.Once boundary conditions were set for two renewable energy generation cases and hydrogen production was determined for an average year, the profitability was calculated as a function of two variables: electrolysis plant size and hydrogen selling price. In the case of offshore wind power, it becomes profitable to produce hydrogen starting at a selling price of $5.5/kg H2, with a minimum plant size of 55 MW.With solar photovoltaic electricity as the input of the electrolysis plant in the second scenario, for plant size range of 1 MW–300 MW, the economic balance becomes positive selling at $3.5 to $7 per kg of hydrogen produced. The results are discussed more in depth throughout the article based on the simplified calculus model presented.

A Model-Aware Comprehensive Tool for Battery Energy Storage System Sizing

This paper presents a parametric procedure to size a hybrid system consisting of renewable generation (wind turbines and photovoltaic panels) and Battery Energy Storage Systems (BESS). To cope with the increasing installation of grid-scale BESS, an innovative, fast and flexible procedure for evaluating an efficient size for this asset has been developed. The tool exploits a high-fidelity empirical model to assess stand-alone BESS or hybrid power plants under different service stacking configurations. The economic performance has been evaluated considering the revenue stacking that occurs when participating in up to four distinct energy markets and the degradation of the BESS performances due to both cycle- and calendar-aging. The parametric nature of the tool enables the investigation of a wide range of system parameters, including novel BESS control logic, market prices, and energy production. The presented outcomes detail the techno-economic performances of a hybrid system over a 20-year scenario, proposing a sensitivity analysis of both technical and economic parameters. The case study results highlight the necessity of steering BESS investment towards the coupling of RES and accurate planning of the service stacking. Indeed, the implementation of a storage system in an energy district improves the internal rate of return of the project by up to 10% in the best-case scenario. Moreover, accurate service stacking has shown a boost in revenues by up to 44% with the same degradation.

An effective solution to boost generation from waves: Benefits of a hybrid energy storage system integration to wave energy converter in grid-connected systems.

Background: Wave energy represents one of the most promising renewable energies due to its great theoretical potential. Nevertheless, the electrical compliance of grid-connected systems is a great issue nowadays, due to the highly stochastic nature of wave energy. Methods: In this paper, a Hybrid Energy Storage System (HESS) consisting of a Li-ion battery and a flywheel is coupled to a Wave Energy Converter (WEC) that operates in grid connected mode. The study is performed using real yearly wave power profiles relating to three different sites located along the European coasts. The Simultaneous Perturbation Stochastic Approximation (SPSA) principle is implemented as real-time power management strategy for HESS in wave energy conversion systems. Results: Obtained results demonstrate how the proposed HESS and the implementation of the SPSA power management coupled to a WEC allow a reduction of more than 80% of power oscillations at the Point of Common Coupling (PCC), while proving the robustness of the developed management strategy over the investigated sites. Moreover, the average energy penalty due to the HESS integration results slightly higher than 5% and battery solicitation is reduced by more than 64% with respect to the flywheel solicitation, contributing to extend its lifetime. Conclusions: HESS integration in renewable generation systems maximizes the WEC production while smoothing the power at the PCC. Specifically, flywheel-battery HESS together with the implemented power management strategy could provide a great flexibility in the view of increasing power production from waves, strongly mitigating the variability of this source while enhancing grid safety and stability.