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The mass introduction of variable renewable energies, including wind and solar photovoltaic, leads to additional costs caused by the intermittency. Many recent studies have addressed these “integration costs,” and proposed novel metrics that replace the traditional metric known as the levelized cost of electricity (LCOE). However, the policy relevance of those metrics remains unclear. In this study, the author investigates and re-defines the concept of system LCOE, referring to prior studies, and proposes concrete methods to estimate them. Average system LCOE allocates the integration cost to each power source, dividing that by the adjusted power output. Marginal system LCOE revises the concept of system LCOE and value-adjusted LCOE proposed by prior studies, to be clearer and more policy-relevant. These metrics are also applied to Japan’s power sector in 2050, suggesting the necessity of aiming for a “well-balanced energy mix” in future power systems with decarbonised power sources.

Solar Photovoltaics (PV) is seen as one of the renewable energy technologies that could help reduce the world’s dependence on fossil fuels. However, since it is dependent on the sun, it can only generate electricity in the daytime, and this restriction is exacerbated in electricity grids with high PV penetration, where solar energy must be curtailed due to the mismatch between supply and demand. This study conducts a techno-economic analysis to present the cost-optimal storage growth trajectory that could support the dynamic integration of solar PV within a planning horizon. A methodology for cost-optimal assessment that incorporates hourly simulation, Monte Carlo random sampling, and a proposed financial assessment is presented. This approach was tested in Japan’s southernmost region since it is continuously increasing its solar capacity and is at the precipice of high PV curtailment scenario. The results show the existence of a cost-optimal storage capacity growth trajectory that balances the cost penalty from curtailment and the additional investment cost from storage. This optimal trajectory reduces the impact of curtailment on the energy generation cost to manageable levels and utilizes more solar energy potential that further reduces CO2 emissions. The results also show that the solar capacity growth rate and storage cost significantly impact the optimal trajectory. The incorporation of the Monte Carlo method significantly reduced the computational requirement of the analysis enabling the exploration of several growth trajectories, and the proposed financial assessment enabled the time-bound optimization of these trajectories. The approach could be used to calculate the optimal growth trajectories in other nations or regions, provided that historical hourly temperature, irradiance, and demand data are available.

Economic dispatch (ED) is an optimisation tool that is used to allocate active load demands to the generating units through optimising the fuel generation cost function subject to the different operational constraints. The high non-linearity of the power system imposes mathematical challenges in formulating the generation cost function models, which makes the ED problem hard to solve. This study introduces two ideas to solve issues related to the ED problem. First, a dynamic formulation technique is developed to optimally allocate the change in the total active load demand to the generating units. This technique is shown to be insensitive to the optimality of the initial active load distribution unlike the base point and participation factor method. Moreover, it guarantees an optimal distribution among the generating units due the change in the active load demand. Second, a novel approximation of the non-convex generation cost function is developed to solve non-convex ED problem with the transmission losses. This approximation enables the use of gradient and Newton techniques to solve the non-convex ED problem with valve point loading effect and transmission losses in an analytic approach. This approximation is compared with some heuristic optimisation techniques.

This paper presents application of a new effective metaheuristic optimization method namely, the Jaya algorithm to deal with different optimum power flow (OPF) problems. Unlike other population-based optimization methods, no algorithm-particular controlling parameters are required for this algorithm. In this work, three goal functions are considered for the OPF solution: generation cost minimization, real power loss reduction, and voltage stability improvement. In addition, the effect of distributed generation (DG) is incorporated into the OPF problem using a modified formulation. For best allocation of DG unit(s), a sensitivity-based procedure is introduced. Simulations are carried out on the modified IEEE 30-bus and IEEE 118-bus networks to determine the effectiveness of the Jaya algorithm. The single objective optimization cases are performed both with and without DG. For all considered cases, results demonstrate that Jaya algorithm can produce an optimum solution with rapid convergence. Statistical analysis is also carried out to check the reliability of the Jaya algorithm. The optimal solution obtained by the Jaya algorithm is compared with different stochastic algorithms, and demonstrably outperforms them in terms of solution optimality and solution feasibility, proving its effectiveness and potential. Notably, optimal placement of DGs results in even better solutions.

Modern electrical power systems are becoming increasingly complex and are expanding at an accelerating pace. The power system’s transmission lines are under more strain than ever before. As a result, the power system is experiencing a wide range of issues, including rising power losses, voltage instability, line overloads, and so on. Losses can be minimized and the voltage profile can be improved when energy resources are installed on appropriate buses to optimize real and reactive power. This is especially true in densely congested networks. Optimal power flow (OPF) is a basic tool for the secure and economic operation of power systems. It is a mathematical tool used to find the instantaneous optimal operation of a power system under constraints meeting operation feasibility and security. In this study, a new application algorithm named white shark optimizer (WSO) is proposed to solve the optimal power flow (OPF) problems based on a single objective and considering the minimization of the generation cost. The WSO is used to find the optimal solution for an upgraded power system that includes both traditional thermal power units (TPG) and renewable energy units, including wind (WPG) and solar photovoltaic generators (SPG). Although renewable energy sources such as wind and solar energy represent environmentally friendly sources in line with the United Nations sustainable development goals (UN SDG), they appear as a major challenge for power flow systems due to the problems of discontinuous energy production. For overcoming this problem, probability density functions of Weibull and Lognormal (PDF) have been used to aid in forecasting uncertain output powers from WPG and SPG, respectively. Testing on modified IEEE-30 buses’ systems is used to evaluate the proposed method’s performance. The results of the suggested WSO algorithm are compared to the results of the Northern Goshawk Optimizer (NGO) and two other optimization methods to investigate its effectiveness. The simulation results reveal that WSO is more effective at finding the best solution to the OPF problem when considering total power cost minimization and solution convergence. Moreover, the results of the proposed technique are compared to the other existing method described in the literature, with the results indicating that the suggested method can find better optimal solutions, employ less generated solutions, and save computation time.

The objective of this study is to compare the cost efficiencies of nuclear power and renewable energy generation in reducing CO 2 emissions. To achieve this objective, we estimate the relationship between CO 2 emissions and both nuclear power and renewable energy generation in 16 major nuclear power–generating countries, and compare the costs of both energy generation methods in reducing CO 2 emissions by the same amount. The results show that, to reduce CO 2 emissions by 1%, nuclear power and renewable energy generation should be increased by 2.907% and 4.902%, respectively. This implies that if the current amount of electricity generation is one megawatt-hour, the cost of mitigating CO 2 emissions by 1% is $3.044 for nuclear power generation and $7.097 for renewable energy generation. That is, the total generation costs are approximately $1.70 billion for the nuclear power and $3.97 billion for renewable energy to mitigate 1% of CO 2 emissions at the average amount of electricity generation of 0.56 billion MWh in 2014 in the sample countries. Hence, we can conclude that nuclear power generation is more cost-efficient than is renewable energy generation in mitigating CO 2 emissions, even with the external costs of accidents and health impact risks associated with nuclear power generation.

The number of electric vehicles is constantly increasing in Europe and around the world. Providing a reliable charging infrastructure for the se vehicles is a major challenge for distribution grid operators. Off-grid microgrids have become a promising solution to this challenge, using renewable energy sources such as solar power to meet the demand in a sustainable way. This paper presents a practical study of a solar-powered microgrid operating at a university campus in Ilmenau, Germany, aimed at supporting electric vehicle (EV) charging at public workplaces. The system includes eight charging stations and utilizes renewable energy to reduce grid dependency. Statistical methods, including distribution functions, medians, and mean values, were applied to classify and evaluate the dataset to analyze energy generation and variable load patterns, as well as system performance. The results show that the Ilmenau microgrid can meet EV charging demand during the warm season but underperform during the cold season. An economic analysis determined costs of EUR 0.58/kWh based on pre-2020 component prices and EUR 0.46/kWh based on 2025 market prices. The calculated annual cost per employee is EUR 308.29 over a 20-year period. Increasing energy storage was found to be neither cost-effective nor operationally beneficial. The scalability of the microgrid to larger workplaces is investigated, and recommendations for system improvements are provided.

The rising energy demand, substantial transmission and distribution losses, and inconsistent power quality in remote regions highlight the urgent need for innovative solutions to ensure a stable electricity supply. Microgrids (MGs), integrated with distributed generation (DG), offer a promising approach to address these challenges by enabling localized power generation, improved grid flexibility, and enhanced reliability. This paper introduces the Improved Lyrebird Optimization Algorithm (ILOA) for optimal sectionalizing and scheduling of multi-microgrid systems, aiming to minimize generation costs and active power losses while ensuring system reliability. To enhance search efficiency, ILOA incorporates the Levy Flight technique for local search, which introduces adaptive step sizes with long-distance jumps, improving the exploration-exploitation balance. Unlike conventional local search strategies that rely on fixed step sizes, Levy Flight prevents premature convergence by allowing the algorithm to escape local optima and explore the solution space more effectively. Additionally, a chaotic sine map is integrated to enhance global search capability, ensuring better diversity and superior optimization performance compared to traditional algorithms. Simulation studies are conducted on a modified 33-bus distribution system segmented into three independent microgrids. The algorithm is evaluated under single-objective scenarios (cost and loss minimization) and a multi-objective optimization framework combining both objectives. In single-objective optimization, ILOA achieves a generation cost of $19,254.64/hr with 0.7118 kW of power loss, demonstrating marginal improvements over the standard Lyrebird Optimization Algorithm and significant gains over Genetic Algorithm (GA) and Jaya Algorithm (JAYA). In multi-objective optimization, ILOA surpasses competing methods by achieving a generation cost of $89,792.18/hr and 10.26 kW of power loss. The optimization results indicate that, for the IEEE-33 bus system without considering EIR, the proposed ILOA algorithm achieves savings of approximately 0.0014%, 0.0041%, and 0.657% in operation costs compared to LOA, JAYA, and GA, respectively, when MG-1, MG-2, and MG-3 are operational. The analysis of real power loss reduction demonstrates that, in the IEEE-33 bus system without considering EIR, the proposed ILOA algorithm effectively minimizes power loss by approximately 0.692%, 1.696%, and 1.962% in comparison to LOA, JAYA, and GA, respectively, under the operational conditions of MG-1, MG-2, and MG-3. Additionally, reliability constraints based on the Energy Index of Reliability (EIR) are effectively incorporated, further validating the robustness of the proposed approach. Considering EIR, the real power loss analysis for the IEEE-33 bus system highlights that the proposed ILOA algorithm achieves a reduction of approximately 1.319%, 2.069%, and 2.134% in comparison to LOA, JAYA, and GA, respectively, under the operational scenario where MG-1, MG-2, and MG-3 are active. The results confirm that ILOA is a highly efficient and reliable solution for distributed generation scheduling and multi-microgrid sectionalizing, showcasing its potential for real-world applications such as dynamic economic dispatch and demand response integration in smart grid systems.

During the past few years, due to the growth of electric power consumption, generation costs as well as rises in the level of greenhouse gases efficiency bring special focus on distributed generation. Developing distributed generation resources, especially renewable energy resources, is one of the safest ways to solve such problem. These resources have been decentralised by being installed close to the houses producing few kilowatts. Therefore, there are no losses in transmission lines and provide response for demand. Based on their benefits, the use of such energy resources should be developed in the future, but its management and optimal use is a major challenge. This has become one of the main concerns ofenergy systems researchers. In the current study, an innovative model is provided as a strategic management. It is intended to optimise the operation in smart homes consisting of generation units such as a wind turbine, solar panels, storages, and un/controllable loads. The main objective of this optimisation management is to maximise microgrid profitability for 24 h. The overall results of the model proved that the profit of microgrid increased significantly.

This study investigates the feasibility to repurpose wells from gas production for geothermal closed-loop application in the North German Basin (NGB). The objective for this research topic is to extend the value-added chain of idle wells by re-completion as coaxial deep borehole heat exchangers as an efficient way to produce green energy without drilling new wells by saving the carbon emission and costs of building a new geothermal well. With numerical models of two typical geological settings of the NGB and two different completion schemes, it is possible to simulate the thermal performance over a lifetime of 30 years. The calculated heat extraction rates range from 200 to 400 kW, with maximum values of up to 600 kW. Sensitivity analyses demonstrate that re-completion depth and injection temperature are the most sensitive parameters of thermal output determination. The heat demand around the boreholes is mapped, and heat generation costs are calculated with heating network simulations. The initial production costs for heat are comparable to other renewable energy resources like biomass and competitive against gas prices in 2022. This study highlights available geothermal resources’ environmental and economic potential in already installed wells. The application has almost no geological and no drilling risks and may be installed at any idle well location.