Multiple Renewable Energy Sources Research Articles

Hybrid sine cosine and spotted Hyena based chimp optimization for PI controller tuning in microgrids

In this paper, a novel hybrid sine-cosine and spotted Hyena-based chimp optimization algorithm (hybrid SSC) is adopted for the precise tuning of proportional-integral (PI) controllers in a microgrid system. The microgrid integrates multiple renewable energy sources, including photovoltaic (PV) panels, wind turbines, a fuel cell, and a battery storage system, all connected to a common DC bus. This DC bus interfaces with the main grid through a voltage source converter (VSC). The microgrid comprises a total of eight PI controllers distributed across various components: the boost converter in the wind system, the fuel cell system, the battery energy storage device, and the VSC controller. The hybrid SSC optimization algorithm effectively combines the exploration capabilities of the sine-cosine algorithm (SCA) with the exploitation strengths of the spotted Hyena optimizer (SHO) and Chimp optimization algorithm (ChOA), aiming to achieve optimal tuning of the PI controllers. This hybrid approach ensures an enhanced dynamic response and overall system performance by minimizing the integral of the time-weighted squared error (ITSE) for each controller. The simulation results, directed in a MATLAB/SIMULINK environment, demonstrate the efficacy of the hybrid SSC algorithm in improving the stability, response time and efficacy of the microgrid. The proposed technique significantly outperforms traditional tuning techniques, ensuring robust operation and seamless addition of renewable energy sources with the main grid. This study contributes to the advancement of intelligent control strategies for modern microgrids, emphasizing the importance of hybrid optimization algorithms in achieving optimal performance in complex energy systems.

A stackelberg game-based programming approach for industrial steam systems incorporating renewable energy considering demand response

Fossil fuels account for 37 % of industrial combustion, primarily used for steam production. Given the significant role of steam in industrial processes, industrial steam systems (ISS) offer a critical opportunity to accelerate the transition from fossil fuels to renewable energy by integrating renewable sources. Currently, ISS optimization focuses mainly on internal improvements, often neglecting stakeholder interactions and the inherent unpredictability of renewable energy sources. This paper introduces a novel bi-level programming approach based on Stackelberg game theory to address the balance between ISS and user interests. A new ISS model is proposed that integrates multiple renewable energy sources while accounting for their intermittency. Revenue models for energy servers (ES) and consumers (EC) are developed, and demand response mechanisms and real-time pricing are incorporated into the bi-level programming model. In the designed bi-level ISS (B-L-ISS) model, the upper level optimizes energy conversion equipment and pricing, while the lower level aims to minimize users' energy costs. The Karush-Kuhn-Tucker (KKT) conditions are used to reformulate the bi-level problem into a single-level programming problem with equilibrium constraints. Validation on a real coastal industrial steam system shows that the proposed method integrates renewable energy four times more effectively than traditional methods, reducing CO2 emissions by 15,790 tons annually, while balancing the interests of both the steam system and its users.

Coordinated optimization method of renewable energy sources and energy storage devices based on synergistic capacity short circuit ratio

The traditional short circuit ratio index does not consider the impact of energy storage devices (ESDs) and cannot be used for the collaborative optimization of ESDs and renewable energy sources (RESs). Therefore, this paper proposes a novel synergistic capacity short circuit ratio (SCSCR) index, which can reflect the interaction between multiple RESs and ESDs on each bus under different capacities. Based on the proposed SCSCR, a coordinated optimization method of RESs and ESDs is proposed. It ensures system strength while determining the optimal location and capacity proportion of RESs and ESDs. Firstly, the location and capacity proportion of RESs are obtained through optimization method because the system strength is mainly determined by RESs connection points. Secondly, the optimal location and capacity proportion of the ESDs are found under the optimal configuration of the RESs. The proposed optimization method ensures the system strength while obtaining the optimal location and optimal capacity proportion of RESs and ESDs. Finally, the simulation results verify the effectiveness of this method on the IEEE 9 bus and 39 bus systems, providing reference for the site selection and efficient operation of RESs and ESDs.

A Techno-Economic Analysis of a Hybrid Microgrid System in a Residential Area of Bangladesh: Optimizing Renewable Energy

In the face of a significant power crisis, Bangladesh is turning towards renewable energy solutions, a move supported by the government’s initiatives. This article presents the findings of a study conducted in a residential area of Pabna, Bangladesh, using HOMER (Hybrid Optimization of Multiple Energy Resources) Pro software version 3.14.2. The study investigates the feasibility and efficiency of a grid-connected hybrid power system, combining photovoltaics (PV), a biomass generator, and wind energy. The simulation produced six competing solutions, each featuring a distinct combination of energy sources. Among the configurations analyzed, the grid-connected PV–biomass generator system emerged as the most cost-effective, exhibiting the lowest COE at USD 0.0232, a total net present cost (NPC) of USD 321,798.00, and an annual operating cost of USD 6060.59. The system presents a simple payback period of 9.25 years, highlighting its economic viability. Moreover, this hybrid model significantly reduces CO2 emissions to 78,721 kg/year, compared to the 257,093 kg/year emissions from a solely grid-connected system, highlighting its environmental benefits. Sensitivity analyses further reveal that the system’s performance is highly dependent on solar irradiance, indicating that slight variations in solar input can significantly impact the system’s output. This study underscores the potential of integrating multiple renewable energy sources to address the power crisis in Bangladesh, offering a sustainable and economically viable solution while also mitigating environmental impacts.

Battery energy storage with renewable energy sources integration in unbalanced distribution network considering time of use pricing

The increasing demand for sustainable energy solutions and the escalating energy demand have facilitated the emergence of renewable energy sources (RES), such as photovoltaic (PV) sources. Combining RES with battery energy storage system (BESS) technology reduces peak hour demand and allows for economical charging and discharging for time-based energy pricing. Integrating PV and BESS in an unbalanced system with multiple RES sources is a challenging task. It requires a robust algorithm to minimise power loss in the distribution system and decrease the voltage unbalance factor (VUF). This paper presents a new multi-objective Pelican optimisation algorithm (MOPOA) for the optimal allocation of PV and BESS. The MOPOA helps find the best placement of PV and BESS in an IEEE-33 bus unbalanced radial distribution system (URDS). The proposed algorithm combines multiple benefits from a lower net present cost (NPC) and a higher voltage profile enhancement index (VPEI). The case studies and simulation results show that the proposed method places PV and BESS in IEEE 33-bus URDS optimally, satisfying all of the system’s requirements. The results indicate an improvement in the minimum VUF factor of 4.4% in a winter day and 4.3% in a summer day; a reduction in active power loss of 16% in a winter day and 7.1% in a summer day; and a reduction in reactive power loss of 7.5% in a winter day and 7.2% in a summer day. Thus, the recommended strategy effortlessly accelerates to a suboptimal solution.

Resilient bus-bar protection scheme for DC microgrids connected to AC electric grids

ABSTRACT In recent years, there has been a growing interest in incorporating microgrids into existing electrical power networks to reduce reliance on conventional grids. This is further due to the numerous benefits of microgrids, most notably the ability to operate in either autonomous or grid-connected mode, making microgrids a highly versatile structure to incorporate intermittent production and energy storage. However, despite the many benefits, it is difficult to present resilience protection for DC bus bar to ensure the efficient operation of a DC microgrid, as there is a non-zero crossing phenomenon of DC fault current, while zero crossing phenomenon of an AC fault. Additionally, the DC bus bar has both an AC side with rectified DC and a DC line current, creating protection concerns for microgrids. To address these issues, this article proposes a novel fast fault detection scheme for DC microgrids with multiple renewable energy sources, energy storage, and loads. The scheme incorporates current and voltage sensing through current and voltage transformers, along with advanced digital relays, to detect and isolate faults on the DC microgrid. By analyzing rectified AC and DC line data, the scheme employs a differential current-based protection strategy to safeguard the DC bus bar. Validated through comprehensive fault simulations in PSS/SINCAL, the method proves effective in maintaining DC bus voltage stability and enhancing microgrid flexibility.

Optimal scheduling and management of grid‐connected distributed resources using improved decomposition‐based many‐objective evolutionary algorithm

AbstractThis paper emphasizes the integration of wind and photovoltaic (PV) generation with battery energy storage systems (BESS) in distribution networks (DNs) to enhance grid sustainability, reliability, and flexibility. A novel multi‐objective optimization framework is introduced in this study to minimize energy supply costs, emissions, and energy losses while improving voltage deviation (VD) and voltage stability index (VSI). The proposed framework comprising normal boundary intersection (NBI) and decomposition‐based evolutionary algorithms (DBEA) determines the optimal siting and sizing of renewable‐based distributed resources, considering load demand variations and the intermittency of wind and solar outputs. The comparative analysis establishes that the proposed strategy performs better than many contemporary algorithms, specifically when all the objective functions are optimized simultaneously. The validation of the proposed framework was carried out on the standard IEEE‐33 bus test network, which demonstrates significant percentage savings in energy supply costs (49.6%), emission rate (62.2%), and energy loss (92.3%), along with enormous improvements in VSI (91.9%) and VD (99.8953%). The obtained results categorically underline the efficiency, reliability, and robustness of the proposed approach when employed on any complex distribution network comprising multiple renewable energy sources and battery storage systems.

Application of improved Wolf pack algorithm in planning and operation of multi-microgrid systems with electric vehicles

Abstract With the rapid growth of renewable energy sources and the widespread use of electric vehicles (EVs), the planning and operation problems of multiple microgrids (MMGs) have become more complex and diverse. This paper develop an MMG model with multiple renewable energy sources and small-scale EVs, aiming to maximize the use of renewable energy sources and realize the charging demand of EVs, and highlighting the potential role of EVs in MMGs. In addition, the paper underscores the indispensable role of measurement technology in microgrids and the impetus that microgrid development provides for advancements in measurement technology. To this end, this paper proposes an improved Wolf pack algorithm (IWPA) based on the standard Wolf Pack Algorithm (WPA) with a spiral search approach and chaotic updating of individuals to improve the global search capability of the algorithm and the complexity of solving the scheduling problem. Through simulation experiments on ten standard test functions and examples, it is verified that the IWPA algorithm improves the search accuracy by 2.8%–6.8% and 13.9%–18.3% in the worst and best cases, respectively, in comparison with other algorithms, and it also has a faster convergence speed. Meanwhile, this paper proposes a load interval pricing strategy for the shortcomings of time-of-use pricing strategy and traditional real-time pricing strategy, which is simulated under grid-connected operation, isolated grid operation, and multi-microgrid cooperative operation modes, and the simulation results of the arithmetic example show that this strategy can effectively reduce carbon emissions, and IWPA can effectively coordinate renewable energy, EVs, and other energy resources to achieve efficient energy management of MMGs and supply-demand balance.

A data-driven optimization model for renewable electricity supply chain design

The rising electricity demand, driven by technological progress and population growth, has made the design of the electricity supply chain a major challenge. The transition to renewable energy sources has led to a substantial reduction in the costs and greenhouse gases emissions of electricity production. This study proposes a hybrid approach consisting of a recurrent neural network and an optimization model for designing a resilient electricity supply chain based on wind and solar energies. First, the electricity demand is forecast using the long short-term memory algorithm. Second, based on the forecast demand, a two-stage stochastic programming model is developed for designing an electricity supply chain under disruption risks. Partial and complete disruptions in power lines and facilities, including power plants, distribution and subtransmission substations, and low-power solar panel farms are considered. The objective of the proposed model is to minimize the total cost of the supply chain by making optimal decisions on facility location; technology selection for power plants; setting up transmission lines; and the quantity of power generation, power transmission, and power shortage in low- and high-voltage energy consumption systems. Three strategies are adopted to enhance the supply chain's resilience: multiple renewable energy sources, multiple power transmission lines, and lateral power lines. Lastly, the Sistan and Baluchestan Electric Power Distribution Company in Iran is used as a case study for this work to demonstrate the applicability of the proposed approach, analyze the findings, and derive relevant managerial insights.

Trends in Hybrid Renewable Energy System (HRES) Applications: A Review

Microgrids and hybrid renewable energy systems play a crucial role in today’s energy transition. They enable local power generation and distribution, reducing dependence on large centralized infrastructures, can operate independently or connected to a grid, and can provide backup power, thus increasing system resilience. In addition, they combine multiple renewable energy sources, such as solar, wind, hydro, and biomass, to maximize the efficiency and reliability of the supply, and are also adaptable to location-specific conditions, taking advantage of locally available energy resources and reducing the need for energy imports. Moreover, they contribute to decarbonization goals by offering a cleaner and more sustainable alternative. In this article, a documentary review is presented on the interaction of Homer Pro software 3.16.2 (July 2023), used for the design of hybrid renewable energy systems (HRES), with other methods of optimization or sizing. Allusion is made to the type of architecture in the most prominent clean and fossil source configurations, the levelized cost, net annual cost, and maintenance and capital investment cost. A comparison is made among the works reported in the last five years regarding the use of this software tool, based on load demand, geographical area, renewable energy sources, fossil sources, and objective functions, applied to the educational, rural, and industrial sectors. It is shown that India is one of the countries that has reported the most number of HRES techno-economic environmental analysis works, and that the case studies have focused approximately 47% on rural areas, 20% on educational agencies, 14% on commerce and industry, and 29% on urban buildings.

Reshaping the energy landscape of Crete through renewable energy valleys

Renewable energy valleys (REVs) represent a transformative concept poised to reshape global energy landscapes. These comprehensive ecosystems transition regions from conventional energy sources to sustainable, self-reliant hubs for renewable energy generation, distribution, and consumption. At their core, REVs integrate advanced information and communication technology (ICT), interoperable digital solutions, social innovation processes, and economically viable business models. They offer a vision of decentralized, low-carbon landscapes accessible to all, capable of meeting local energy demands year-round by harnessing multiple renewable energy sources (RES) and leveraging energy storage technologies. This paper provides an overview of the key components and objectives of REVs, including digital integration through advanced ICT technologies and open digital solutions that enable the seamless management of RES within the REV. The social innovation aspect via the REV’s active communities is also examined, encouraging their participation in the co-design, implementation, and benefit-sharing of renewable energy solutions. In addition, business viability through sustainable business models central to the REV framework is proposed, ensuring affordability and accessibility to all stakeholders. The paper presents a case study of Crete, showcasing how the REV idea can work in real life. Crete utilizes various energy sources to become energy-independent, lower carbon emissions, and enhance system resilience. Advanced energy storage technologies are employed to ensure supply and demand balance within the REV. Situated on the picturesque island of Crete, Greece, it is pioneering the establishment of a Renewable Energy Valley ‘Living Lab’ (REV-Lab), integrating Community Energy Labs (CELs) as innovation hubs. This initiative exemplifies the REV model, striving to create a digitalized, distributed, and low-carbon landscape accessible to all residents throughout the year.

Modeling, design and optimization of integrated renewable energy systems for electrification in remote communities

Integrated renewable energy systems are becoming a promising option for electrification in remote communities. Integrating multiple renewable energy sources allows the communities to counteract the weaknesses of one renewable energy source with the strengths of another. This study aims to model, design and optimize integrated renewable energy systems consisting of solar photovoltaic (PV) panels, wind turbines, a biomass power generator, and storage batteries for applications in remote communities in Canada. Biomass is used as a fuel to produce electricity during periods when solar power and wind power are not capable of meeting the power demand. A methodology is developed to optimize the integrated renewable energy systems design, with the aim of minimizing the net present cost (NPC) and the levelized cost of electricity (LCOE) of the energy systems. Results show that the NPC is $3.61 M and the LCOE is $0.255/kWh for an optimized integrated renewable energy system in a sample remote community that has a peak power consumption of 238.7 kW and an average load demand of 2230 kWh/day. Through the present research, the integrated energy systems are evidenced to be an effective option for electrification in remote communities.

Development of multi-input multi-output converter for decarbonization energy system

Decarbonization energy systems include a range of renewable energy sources, such as solar, wind, hydro, and geothermal. These sources generate electricity with minimal carbon emissions and can be used in various applications, including power generation, heating, and transportation. Therefore, the use of a multi-input multi-output (MIMO) converter is becoming increasingly important in decarbonization energy systems. This is because the integration of multiple renewable energy sources, requires a power converter that can manage multiple inputs and outputs efficiently. This paper presents the development of a MIMO converter using a single-phase matrix converter for decarbonization energy systems. The proposed converter is capable of integrating multiple renewable energy sources and can be used in a microgrid application, thus removing the need of multiple converters. The design and analysis of the converter are presented, including the selection of power switches and control strategy. The performance of the converter is evaluated through experimental results, demonstrating its effectiveness in managing multiple inputs and outputs using a single power converter circuit. The results indicate that the proposed converter is a promising solution for decarbonization energy systems, contributing to the development of sustainable energy systems.

The Role of Power Electronic Converters in Microgrid Technology: A Review of Challenges, Solutions, and Research Directions

The paper is on the role of power electronic converters in microgrid technology: A review of challenges, solutions and research directions. The objective of the paper is to perform a comprehensive overview of the role of power electronic converters in microgrid technology, focusing on challenges, solutions, and research directions. Findings revealed that major challenges of power electronic converters integration in microgrid technology are voltage and frequency regulation issues, power quality issues, creative management and coordination challenges, and Integration of renewable energy sources. The solutions to these problems are advanced control algorithms such as Model Predictive Control (MPC); deployment of active power filters or harmonic compensators to reduce harmonic distortion and improve power quality; iimplement a centralized control system with centralized monitoring controllers to coordinate the operation of several converters and ensure consistent operation; and combining multiple renewable energy sources in a hybrid energy system to diversify generation sources and reduce the gap. The future research directions include, among others, advanced control strategies, grid-forming converters, wideband semiconductor, and cyber-security and Resilience. The paper concludes that the integration of power electronic converters into microgrid technology presents both opportunities and challenges. Although these converters play an important role in the efficient conversion, distribution and utilization of energy in microgrids, they face various technical and practical challenges. To mitigate these challenges, the implementation of advanced control strategies, grid-forming converters, etc., is inevitable.

Assessing environmental impact: Micro-energy network optimization in a Chinese industrial park

Micro-energy systems contribute significantly to environmental improvement by reducing dependence on power grids through the utilization of multiple renewable energy sources. This study quantified the environmental impact of a micro-energy network system in an industrial park through a life cycle assessment using the operation of the micro-energy network over a year as the functional unit and “cradle-to-gate” as the system boundary. Based on the baseline scenario, a natural gas generator set was added to replace central heating, and the light pipes were expanded to constitute the optimized scenario. The results showed that the key impact categories for both scenarios were global warming, fine particulate matter formation, human carcinogenic toxicity, and human non-carcinogenic toxicity. The overall environmental impact of the optimized scenario was reduced by 68% compared to the baseline scenario. A sensitivity analysis of the key factors showed that electricity from the power grid was the key impact factor in both scenarios, followed by central heating and natural gas. Therefore, to reduce the environmental impact of network systems, it is necessary to further optimize the grid power structure. The research approach can be used to optimize micro-energy networks and evaluate the environmental impact of different energy systems.

Deep Reinforcement Learning-Based Security-Constrained Battery Scheduling in Home Energy System

The home energy system today involves multiple renewable energy sources and battery energy storage systems, which can be considered as a microgrid. The battery energy storage system is a key component in the home energy system for the sake of filling the gap between the user demand and volatile energy supplies to maximize the techno and economic performances. However, the battery scheduling must suffer the stochastic nature of renewable energy resources and loads, which results in an intractable multi-period stochastic optimization problem with security constraints. An improved actor-critic-based reinforcement learning is proposed for this issue, where a distributional critic net is applied for faster and more accurate reward estimation under uncertainties, and a policy net incorporating protective secondary control is adopted to satisfy security constraints, preventing the unsafe state of batteries during the trial-and-error process. Numerical tests show that the proposed approach outperforms conventional reinforcement learning algorithms, as well as the rule-based battery scheduling approach while guaranteeing safe operation. The robustness and adaptability of the proposed method are also verified in case studies with different optimization tasks.

Design and control of LSTM-ANN controllers for an efficient energy management system in a smart grid based on hybrid renewable energy sources

Many isolated locations, including hilly areas, remote sites, and military camps, lack feasible access to the main power grid. In these conditions, locally established Microgrids can provide the necessary power supply. However, to meet the demands of these isolated areas, numerous individual Microgrids are required. Although some of these Microgrids might be integratable, geographical constraints may preclude full integration. Under these circumstances, the establishment of a smart grid—integrating multiple Microgrids with a battery management system—can potentially solve many power supply issues. A smart grid control system paired with a centralized battery management system is proposed in this paper. The system considers the use of multiple renewable energy sources at various locations for stable and reliable power generation. The proposed method incorporates a long short-term memory (LSTM)-based artificial neural network (ANN) to ensure a stable, high-quality power supply at different load buses. Additionally, this work introduces an artificial intelligence-based operating system designed to maintain energy management under various conditions. To enhance voltage quality, a 7-level aligned multilevel inverter is incorporated into the system. As compared with PI and Fuzzy controllers, the proposed method with LSTM-ANN controller is improved the power quality under sudden changes in the system which are also presented in results, Voltage variation of PI, Fuzzy, and LSTM-ANN is 370V,180V,and 70V . The effectiveness of the proposed energy management system (EMS) is verified by using a hardware-in the-loop approach with OPAL-RT modules, yielding realistic results.

Integrated Control and Energy Flow Management for Hybrid Grid-Connected Photovoltaic/Wind Systems with Battery Storage Using Fuzzy Logic Controllers

This paper focuses on the control and management of an energy conversion system within a microgrid setup comprising multiple renewable energy sources and a storage system, all connected to the AC-grid through DC/DC, AC/DC, and DC/AC converters. Various control objectives are defined, which depend on factors such as energy availability, load demand, storage system state of charge, and grid availability. To address these scenarios, an energy flow management system is developed in this work. The primary roles of this management algorithm include ensuring the safety of all microgrid components, enhancing the storage system’s lifespan, and improving energy efficiency. To achieve these objectives, multiple control loops are designed based on fuzzy logic controllers ensuring appropriate system operation modes. The effectiveness of this control and energy management strategy is evaluated using the MATLAB/Simulink/Simpower environment, demonstrating its capability to meet the specified objectives.

Intermittent control switch characteristics of triboelectric electric hybrid energy harvesting devices and power management circuits

Renewable energy sources such as wind, vibration, and water wave energy are widely available in everyday life, where there are also scenarios where multiple renewable energy sources coexist. If multiple...

Optimal Energy Sharing in Hybrid Microgrid System Using Battery Energy Storage

A smart micro grid technology application facilitates the integration of renewable energy and increase its penetration. A smart grid is an electrical network which is built on advanced technology that uses dual-way digital communication to transmit electricity to buyers. The smart grid was created with the aim of using smart meters to overcome the problems faced by traditional grids. Microgrids allow for the integration of multiple renewable energy sources at different levels, improving the power system’s reliability, sustainability and efficiency. Remote places, spacecraft and maritime applications all use DC microgrids. Solar photovoltaic (PV) systems, wind energy, fuel cells, battery management systems, supercapacitors, and loads make up a DC microgrid. In this paper, some of the interesting approaches for optimal energy sharing in the hybrid microgrid are discussed.