Enhancing hybrid renewable system performance through load shifting: A multi-objective optimization and forecasting approach

SummaryDependency on alternative energy sources has increased due to the depletion of fossil fuel and their costs. Even though the usage of renewable energy sources (RESs) has significantly reduced these problems, there are quite a few challenges like reliability, lower efficiency, and high cost which can be overcome by the integration of multiple RES to meet the energy demand. In this study, a multi‐objective approach is proposed to get the best optimal sizing configuration of a standalone hybrid renewable energy system (HRES) by choosing the minimization of the total cost of the system and loss of power supply probability. In this regard, wind turbines, solar photovoltaic (PV), battery storage systems, biomass, and diesel generators are used to design HRES. The proposed approach deals with the intermittent nature of wind and solar PV power generation using Weibull distribution to model wind speed and solar irradiance. The normal distribution is used to model the load demand. The backtracking search algorithm capable of solving single‐objective optimization problems is modified to handle the multi‐objective optimization problem and to find the optimal sizes of wind turbines, solar PV, battery storage system, diesel, and biomass generators for efficient HRES performance.

Energy management strategies in hybrid renewable energy systems: A review

This paper proposes an optimum design of a diesel/PV/wind/battery hybrid renewable energy system (HRES) for rural electrification in a remote district in Tamanrasset, Algeria. In this study, a particle swarm optimization algorithm (PSO) has been proposed to solve a multi-objective optimization problem, which was created by carrying out simultaneously, the cost of energy (COE) minimization while maximizing the reliability of power supply described as the loss of power supply probability (LPSP) and a renewable fraction (RF). The simulation results show that the PV/WT/DG/BT is the best economic configuration with a reasonable annual cost of the optimal system (ACS) which is about 7798.71 $ and the COE equal to 0.79 $/kWh for an LPSP = 0.01%, where the ten households are 0.99 % satisfied by renewable energy sources.KeywordsHRESRural electrificationMulti-objective optimizationPSOCOEAlgeria

Chapter 31 - Stand-alone hybrid system of solar photovoltaics/wind energy resources: an eco-friendly sustainable approach

This study presents analysis and optimization of a standalone hybrid renewable energy system (HRES) for Adama Science and Technology University’s ICT center in Ethiopia. The proposed hybrid system combines photovoltaic panels, wind turbines, a battery bank, and a diesel generator to ensure reliable and sustainable power. The objectives are to minimize the system’s total annualized cost and loss of power supply probability, while energy reliability is maintained. To optimize the component sizing and energy management strategy of the HRES, we formulated a mathematical model that incorporates the variability of renewable energy and load demand. This optimization problem is solved using a hybrid genetic algorithm (HGA). Simulation results indicate that the HGA yielded the best solution, characterized by the levelized cost of energy of USD 0.2546/kWh, the loss of power supply probability of 0.58%, and a convergence time of 197.2889 s.

People in the Middle East are facing the problem of freshwater shortages. This problem is more intense for a remote region, which has no access to the power grid. The use of seawater desalination technology integrated with the generated energy unit by renewable energy sources could help overcome this problem. In this study, we refer a seawater reverse osmosis desalination (SWROD) plant with a capacity of 1.5 m3/h used on Larak Island, Iran. Moreover, for producing fresh water and meet the load demand of the SWROD plant, three different stand-alone hybrid renewable energy systems (SAHRES), namely wind turbine (WT)/photovoltaic (PV)/battery bank storage (BBS), PV/BBS, and WT/BBS are modeled and investigated. The optimization problem was coded in MATLAB software. Furthermore, the optimized results were obtained by the division algorithm (DA). The DA has been developed to solve the sizing problem of three SAHRES configurations by considering the object function's constraints. These results show that this improved algorithm has been simpler, more precise, faster, and more flexible than a genetic algorithm (GA) in solving problems. Moreover, the minimum total life cycle cost (TLCC = 243 763$), with minimum loss of power supply probability (LPSP = 0%) and maximum reliability, was related to the WT/PV/BBS configuration. WT/PV/BBS is also the best configuration to use less battery as a backup unit (69 units). The batteries in this configuration have a longer life cycle (maximum average of annual battery charge level) than two other configurations (93.86%). Moreover, the optimized results have shown that utilizing the configuration of WT/PV/BBS could lead to attaining a cost-effective and green (without environmental pollution) SAHRES, with high reliability for remote areas, with appropriate potential of wind and solar irradiance.

In this study, a techno-economic feasibility analysis of a stand-alone hybrid renewable energy system (HRES) for a remote rural area of Chikmagalur district of Karnataka (India) has been presented. Load shifting-based demand-side management (DSM) has been implemented for Lead-Acid and Lithium-Ion batteries based HRES for evaluating the feasibility. The performance of the proposed system has been conducted with DSM and without DSM. From the results, it is found that Lithium-Ion battery-based HRES with DSM gives the optimal feasible solution for providing reliable power supply to the proposed un-electrified villages. The optimal Net present Cost (NPC) and Cost of Energy (COE) are found to be $467,644 and 0.106 $/kWh without DSM implementation. Whereas, the operating costs are $314,564 and 0.072 $/kWh respectively with DSM implementation. Based on the analysis, it is observed that the saving of NPC and COE is found to be $153,080 and 0.034 $/kWh, respectively.KeywordsHybrid renewable energy systemDemand-side managementLead-AcidLithium-IonHOMER

As global energy demand and warming increase, there is a need to transition to sustainable and renewable energy sources. Integrating different systems to create a hybrid renewable system enhances the overall adoption and deployment of renewable energy resources. Given the intermittent nature of solar and wind, energy storage systems are combined with these renewable energy sources, to optimize the quantity of clean energy used. Thus, various optimization strategies have been developed for the integration and operation of these hybrid renewable energy systems. Existing studies have either reviewed hybrid renewable energy systems or energy storage systems, however, these studies ignored energy storage systems integrated with hybrid renewable energy systems. This study offers a comprehensive analysis of the optimization methods used in hybrid renewable energy systems (HRES) integrated with energy storage systems (ESS). We examined the optimization models used in the integration of HRES and ESS, their objectives, and the common constraints. Based on our review, capacity and CO2 emissions constraints were frequently used in hybrid optimization techniques that are effective approaches for integrating HRES and ESS. This research supports the move towards sustainable, clean energy solutions by combining an analysis of energy storage techniques with the optimization of hybrid renewable energy systems.

A combined multi-objective intelligent optimization approach considering techno-economic and reliability factors for hybrid-renewable microgrid systems

This study investigates the asymmetric trade-off between cost and reliability in the optimal sizing of stand-alone Hybrid Renewable Energy Systems (HRESs) composed of photovoltaic panels (PV), wind turbines (WT), battery storage, a diesel generator (DG), and an inverter. The optimization is formulated as a multi-objective problem with Cost of Energy (CoE) and Loss of Power Supply Probability (LPSP) as conflicting objectives, highlighting that those small gains in reliability often require disproportionately higher costs. To ensure practical feasibility, the installation roof area limits both the number of PV panels, wind turbines, and batteries. Two metaheuristic algorithms—NSGA-II and MOPSO—are implemented in a Python-based framework with an Energy Management Strategy (EMS) to simulate operation under real-world load and resource profiles. Results show that MOPSO achieves the lowest CoE (0.159 USD/kWh) with moderate reliability (LPSP = 0.06), while NSGA-II attains a near-perfect reliability (LPSP = 0.0008) at a slightly higher cost (0.179 USD/kWh). Hypervolume (HV) analysis reveals that NSGA-II offers a more diverse Pareto front (HV = 0.04350 vs. 0.04336), demonstrating that explicitly accounting for asymmetric sensitivity between cost and reliability enhances the HRES design and that advanced optimization methods—particularly NSGA-II—can improve decision-making by revealing a wider range of viable trade-offs in complex energy systems.

This paper presents a methodology to size Standalone Hybrid Renewable Energy System (SHRES) which combines solar PV, wind turbine (WT) and battery energy storage (BES) for application in rural areas. These sources are integrated via an AC bus to support the load demand. SHRES is simulated under varying load demand, solar radiation, temperature and wind speed obtained from the Malaysian Meteorological Department. A Multi-objective Optimization using Non-dominate Sorting Genetic Algorithm (NSGA-II) was utilized to determine the best sizing of PV / wind turbine / battery, and minimize Cost of Energy (COE) and Loss of Power Supply Probability (LPSP). The results show that the NSGAII optimization of the model is able to determine the best techno-economic sizing for the suggested location. For the case study, the optimum COE was 0.1099 (USD/kWh) and LPSP was 0.0865. The proposed tool can be used to size the SHRES for rural electrification and enhance energy access within remote locations.

Renewable energy sources (RES) are an inevitable environmental option in near future. These sources compete with conventional power generation, where good wind and solar resources are available. Hybrid renewable energy systems improve the economic and environmental aspects of renewable resources to meet energy demand. This paper aims to propose a multi-objective model to size a hybrid renewable system optimally. The system consists of wind turbines, photovoltaic panels, batteries, and a diesel generator as support for the system. This multi-objective optimization problem is solved using non-dominated sorting genetic algorithm (NSGA-II) method, resulting in the number of system components that will maximize the renewable energy efficiency while minimizing net present cost and CO2 emission. Results are compared to another multi-objective optimization algorithm, Epsilon-constraint. The comparison shows the feasibility of our suggested method for the problem. A residential building complex is then chosen in Khansar, Iran, to apply the proposed model and optimally size the hybrid renewable energy system. Results show that under the chosen climate and the building parameters, the renewable energy efficiency of nearly 80% is achievable which is satisfactory. Furthermore, the results show the undeniable impact of using renewable resources on reducing the pollutants’ emission and their related external costs.

Design of reliable standalone utility-scale pumped hydroelectric storage powered by PV/Wind hybrid renewable system

This paper presents the Hybrid Renewable Energy System (HYRES), a powerful tool to contribute to the viability analysis of energy systems involving renewable generators. HYRES considers various input parameters related to climatic conditions, statistical reliability, and economic views; in addition to offering multi-objective optimizations using Genetic Algorithms (GAs) that have a better cost-benefit ratio than mono-objective optimization, which is the technique used in several commercial systems like HOMER, a worldwide leader in microgrid modeling. The use of intelligent techniques in HYRES allows optimal sizing of hybrid renewable systems with wind and solar energy generators adapted to different conditions and case studies. The elements that affect the system design like buying and selling energy from/to the grid and the use of storage units can be included in system configuration according to the need. Optimization approaches are selectable and include Initial Cost, Life Cycle Cost, Loss of Power Probability, and Loss of Power Supply Probability.

Feasibility evaluation of a hybrid renewable power generation system for sustainable electricity supply in a Moroccan remote site