Renewable Energy Focus最新文献

The ability to shift energy consumption to a time when a lot of energy is available supports the integration of renewable energy sources. Services that intend to do this automatically are currently emerging in the markets. These kinds of flexibility services are targeted as smart services to household consumers, promising them cost savings, optimal living conditions, and other benefits. The purpose of this paper is to examine the current demand and offering of these kinds of services in Finland. We performed an extensive consumer survey to untangle consumers’ potential, motivation, and engagement in flexibility services and surveyed the properties, similarities, and differences of the flexibility services in the markets. We then defined a quality dimensions framework for flexibility services, with which we evaluated the results to understand the required properties of the services from a consumer viewpoint and how the services respond to these requirements. The results show that Finnish household consumers are quite willing and ready for these kinds of services, and several services are currently available that fulfill some of the identified requirements. Most of the services are currently provided to larger customers, but it should be noted that there also exists demand among individual households. Therefore, clearer incentives, unambiguous definitions of responsibilities, and effortless service introductions are required to motivate consumer participation.

Governments worldwide have introduced diverse billing methods and regulations to promote the adoption of photovoltaic (PV) systems, aiming to reduce greenhouse gas emissions. This paper aims to develop a billing policy for installing rooftop PV systems in the Kingdom of Saudi Arabia by studying policies implemented to support installing PV systems in leading countries. Initially, the system is evaluated using the tariff predetermined by Saudi Electricity Cogeneration Regulation Authority under three different policies: a Feed-in Tariff (FIT), net metering, and net billing for different sizes of solar systems 10 kW, 15 kW, and 20 kW. The findings suggest a considerable discounted payback period ranging from 14 to 20 years under the net metering framework with the current tariff structure. In contrast, investments under FIT and net billing schemes do not appear to be viable. Thus, a tariff schedule, tailored according to the consumer-preferred payback period, is proposed. For a payback timeframe of 6–10 years, the tariff should range between $0.09–0.06 $/kWh within the FIT scheme. However, in the case of a net billing policy, the tariff should vary between $0.123–0.066 $/kWh. These findings suggest the supremacy of the FIT scheme, particularly if the government determined to use the low tariff policy.

PVs (photo Voltaics) are the leading player in distributed power systems. Despite all the developments and evolution, the affordability of the solar system is still challenging. The issue of its economy has led to the development of Maximum PowerPoint Tracking (MPPT). The role of maximum power extraction under partial shading is a crucial feature of energy harvesting. This paper has reviewed various conventional, soft computing, and hybrid techniques for MPPT under partial shading conditions. It is observed that conventional methods fail to extract maximum power under partial shading conditions due to multiple peaks. Therefore, different soft computing, hybrid techniques, algorithms and modern PV topologies are discussed to track global maxima from multiple local maxima. The strengths and weaknesses of various methods' tracking speed, transient and steady-state oscillation, cost, computational complexity, etc., are taken as independent quantities and various MPPT techniques as dependent quantity to prepare a user-friendly table. Also, three different meta-heuristic algorithms (Cuckoo Search, JAYA and PSO) performance have been compared for the PSC based on tracking time and efficiency by using a Hardware-in-the-loop (HIL) simulation platform.

This paper presents multi-objective dandelion optimization (DEO) control of an autonomous microgrid consisting of a PV array, along with a battery energy storage system (BES) and diesel generator (DG) system for rural areas that are not connected to the main grid. This configuration aims at the maximum extraction of solar energy by using maximum power point tracking (MPPT) using a boost converter with dandelion optimization (DEO)-based incremental conductance (INC) algorithm. The INC algorithm uses the dandelion algorithm to calculate the ideal step size (δ), that significantly enhances the maximum power point tracking and increases the efficiency of algorithm. The DEO optimization also gives optimized value of gains of PI controller of bidirectional converter of battery for better DC bus voltage regulation. DC link voltage has less variations during steady state and dynamic conditions. Generic sigmoid function-based- modified variable step size least mean square (GS-MVSS-LMS) adaptive VSC control provides solutions for power quality issues such as harmonic elimination, and load leveling and regulates the voltage at PCC. The suggested control method performs better in terms of steady-state error reduction, convergence rate, transition tracking effectiveness, and self-coherence. The main features of this AC microgrid are the operation of a solar array at its MPPT, less utilization of diesel generators, and reactive power compensation. The performance of the proposed microgrid topology is studied under various dynamic conditions and performs satisfactorily as per IEEE 519 standards. MATLAB/ simpower tools are used for the simulation of system.

A successful and just energy transition is subject to policies that adhere to a multi-dimensional approach to renewable energy development. To achieve success in renewable energy development, well-thought-out and efficient policies must be implemented. This paper aims to address a major shortcoming in renewable energy development in terms of understanding and recognising the policy space that promotes or hampers renewable energy growth. The paper examines the relevant literature to identify major themes and factors in renewable energy development. A thematic analysis was conducted using a hybrid inductive and deductive approach and resulted in the identification of 5 major themes namely Institutional, Environmental, Financial, Socio-cultural, Technical and 144 factors. The identified themes and factors will assist to make a policy action decisive, just, and sustainable. Recommendations including comprehensive incentivisation, regulatory support for renewable energy technologies in developing nations, environment impact assessment programs, promotion of financial transparency and opting for a just transition to benefit local and vulnerable communities.

Distribution network planning and operation are facing several problems, including asset congestion, voltage fluctuations, and system instability. The adequate planning and modeling of distributed generators and capacitor banks must quantify these problems. This article presents the optimal allocation of distributed generators in parallel with capacitor banks in distribution networks with single and multi-objectives using the Gazelle Optimization Algorithm (GOA) and Mountain Gazelle Optimization Algorithm (MGOA). The single objective framework includes technical objectives like minimization of active power losses. The multi-objective framework includes technical and non-technical objectives like simultaneously minimization of active power losses, voltage stability, and voltage deviation, and minimization of polluting greenhouse gases and total electricity purchase cost. Furthermore, these planning problems are investigated by three case studies on different nonlinear voltage-dependent models at two different loading conditions from future planning perspectives. The effectiveness and feasibility of the MGOA are evaluated on the IEEE standard 33 bus system. As a result, the MGOA demonstrates a remarkable reduction in technical and non-technical objectives in all types of distributed generator placement. Moreover, a comparative analysis of other existing research works validated the efficiency and feasibility of established algorithms at each use case with different load models by improving all the objective functions of network planning. In single-objective and multi-objective frameworks, the active power losses reduce to 94.42% and 93.57% in the voltage-independent model, respectively. Meanwhile, the non-technical objectives are also significantly improved for each load model, further validating the efficiency of the proposed algorithms.

This paper contributes with an analysis of the development of photovoltaic plus battery systems under net-metering using elements of business models, from the perspective of value proposition and value capture features. The Brazilian regulatory framework for distributed generation is taken as example. Five photovoltaic plus battery schemes are envisaged and value creation is analyzed regarding two aspects: batteries can improve photovoltaic generation and load mismatch and batteries can cooperate with the distribution system. Policy implications and barriers to the implementation of these schemes are indicated. A techno-economic feasibility analysis for residential prosumers is conducted to gain insight about the viability gap in three realistic scenarios. Behind-the-meter photovoltaic plus battery systems for arbitrage under Time-of-use energy tariffs and a scenario that includes the sale of stored energy to the grid are analyzed in relation to an only-photovoltaic system scenario. Three cities with different photovoltaic generation potential, tariffs and human development index provide good diversity for the comparisons. We find that in favorable locations in terms of high TOU tariffs and good solar radiation, Behind-the-meter photovoltaic plus battery systems could be spontaneously adopted if stacking revenues are allowed, but a case-by-case study is necessary. Moreover, in other cases net present value and payback signal economic viability, then, when specific technical conditions indicate the use of batteries integrated with photovoltaic systems to mitigate negative local effects of photovoltaic generation, in many regions of the country they are viable.

The primary goal of Sustainable Development Goal 7 (SDG 7) is to increase renewable energy use to reduce reliance on fossil fuels and mitigate climate change. Energy-intensive industries can benefit from in-house renewable power generation, reducing their reliance on fossil fuel-based grid power and making processes greener. However, integration among power generation/purchase, energy storage systems (ESS), and power consumption is crucial to overcome the intermittent nature of renewable power sources. ESS plays a vital role in increasing resilience and optimizing material production based on power availability and pricing. The efficacy of ESS needs critical evaluation considering cost, efficiency, and uncertainties related to renewable power generation and material product demand. The paper proposes two scenario-based optimization approaches to assess the impact of uncertainties on the integrated supply and demand side management (ISDM) system, focusing on lithium-ion batteries and cryogenic energy storage (CES). Compared to the conservative approach of the scenario-based robust optimization (SRO) method, the proposed stochastic simulation optimization (SSO) method provides a ‘risk-neutral’ solution, which is 6.45% less than the minimum expected cost solution. The analysis also suggests that lithium-ion batteries are more economically effective for the proposed integrated framework than CES, resulting in almost a 29% reduction in operating costs compared to no battery option. The proposed framework could contribute to sustainable and economically viable energy management practices in energy-intensive industries. Further research and implementation of such frameworks could accelerate the adoption of renewable energy and energy storage technologies in industrial processes.

In response to Malaysia's commitment to sustainable energy, a decisive decision for renewable energy planning investment remains crucial. Currently, a dedicated tool for assessing the benefits of renewable energy deployment in Malaysia is absent, posing challenges for stakeholders, especially prosumers, in making project investment decisions. This study introduces the development of Multi-Benefits Decision-Making (MBDM) index for solar photovoltaic (PV) investment planning. The aim is to select the most optimal renewable energy compensation schemes under multiple tariff structures and solar PV capacities. The proposed CRITIC weight, integrated with the Technique-for-Order-of-Preference-by-Similarity-to-the-Ideal-Solution (TOPSIS), or to be named as CW-TOPSIS framework, is practically demonstrated through a case study involving a commercial building with a solar PV rooftop. The Hybrid Optimization of Multiple Energy Resources (HOMER) grid software is used to develop a grid-connected solar PV model. Subsequently, a decision matrix is created using HOMER grid optimization results, incorporating indicators like environmental factors, energy usage, tariffs and policies, and economic aspects. A comparative study with Entropy-TOPSIS (EW-TOPSIS), CRITIC-Elimination and Choice Expressing Reality (CW-ELECTRE), and Entropy-ELECTRE (EW-ELECTRE) is conducted to validate the proposed technique's performance. The consistent findings from the comparative study validate the outstanding performance of Net-Energy-Metering 2.0 with Enhanced-Time-of-Use (N2ET). N2ET excels due to its efficient integration of solar PV NEM and dynamic ETOU pricing, amplifying surplus energy trading and benefiting users with improved financial metrics and bill savings.

This research envisions guiding stakeholders in solar PV investments and enhancing project feasibility assessments in Malaysia's commercial sector.

Optimizing the operation of a Solar Thermal Power plant presents significant challenges due to the variable nature of solar radiation and inherent uncertainties like cloud cover. These factors can lead to unexpected shutdowns, especially when the Solar Thermal Power plant is designed for daily startups and shutdowns aligned with sunrise and sunset. Traditional open-loop operations with constant steady-state inputs offer a degree of safety and reliability in output but may not fully leverage the plant’s operational potential. This study introduces the use of Karnaugh Map optimization to enhance the efficiency of Solar Thermal Power plant operations. Implementation of Karnaugh Map optimizer on Storage Tank with volume threshold limits, facilitates the construction of logic operations that respect operational constraints and minimize redundant inputs and its interactions, thereby provide opportunity to increase in thermal energy extraction on solar collector. This approach also ensures vital electric power generation without violating constraints, offering a simplified yet effective solution for real-time Solar Thermal Power plant operation management. This study demonstrates enhancement of electric power generation duration and thermal energy extraction by solar collector, increased by 1 h 24 min and 2078 MJ respectively for increasing from two to three varaibles threshold limits on the Storage Tank.