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Search Results (214)

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31 pages, 10566 KiB  
Article
The Practical Impact of Price-Based Demand-Side Management for Occupants of an Office Building Connected to a Renewable Energy Microgrid
by Damilola A. Asaleye, Darren J. Murphy, Philip Shine and Michael D. Murphy
Sustainability 2024, 16(18), 8120; https://doi.org/10.3390/su16188120 - 18 Sep 2024
Viewed by 794
Abstract
This paper examined the practical impact of price-based demand-side management (DSM) for occupants of an office building connected to a renewable energy microgrid. There has been an absence of studies that have analyzed occupant reactions, in terms of perceived practicality, regarding the implementation [...] Read more.
This paper examined the practical impact of price-based demand-side management (DSM) for occupants of an office building connected to a renewable energy microgrid. There has been an absence of studies that have analyzed occupant reactions, in terms of perceived practicality, regarding the implementation of DSM in conjunction with factors including renewable energy generation, load shifting and energy costs. An increased understanding of the practicality of DSM will support future improvements in building energy efficiency and sustainability. Ten occupants of the National Build Energy Retrofit Test-bed (NBERT) were selected as a case study. The electricity consumption pattern of the NBERT occupants was derived over a period of two years. Five unique DSM schedules were developed for each of the NBERT occupants, and a survey was carried out to investigate the practicality of these DSM schedules. A real-time electricity pricing tariff, electricity CO2 intensity, three feed-in tariffs and two microgrid control methods were evaluated to assess the practicality of each DSM schedule on the ten NBERT occupants. The results showed that the incorporation of onsite renewable energy generation with price-based DSM had a positive impact (r = 0.69–0.75) on occupant practicality. Onsite renewable energy generation was able to offset the demand for expensive electricity from the grid during peak hours, which aligned with the occupants’ typical work schedules. However, the introduction of a feed-in tariff with a renewable energy microgrid made price-based DSM less practical (r = 0.15–0.64). Full article
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Figure 1

Figure 1
<p>Image illustrating the layout of the NBERT office; the blue box represents the kitchenette area, and the green box represents an occupant’s workstation.</p>
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<p>Example of work schedule for occupant A. IO = the occupant was in the office with electrical devices switched on, IO* = the occupant was in the office and made a cup of tea using the kettle, L = the occupant left the office and went to the lecture hall, and the electrical devices at his or her workstation were on standby mode, and OF = the occupant was out of the office for a lunch break or personal reasons, and the electrical devices were on standby mode.</p>
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<p>Average electricity demand (ED) (kWh) of the NBERT office occupants versus real-time electricity price (RTP) (EUR/kWh) and CO<sub>2</sub> emission factor (CO<sub>2</sub>) (kg/kWh) for the base (schedule 0) and five price-based DSM schedules. ED varies for each schedule.</p>
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<p>Hourly averaged dynamic real-time pricing (RTP) (EUR/kWh) and CO<sub>2</sub> emissions factor (kg/kWh) for the year 2013.</p>
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<p>Renewable energy microgrid (REµG) control process flow chart.</p>
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<p>Renewable microgrid practicality process overview showing the relationships and interconnections of the simulations carried out in the research.</p>
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<p>Mean hourly PV energy output (kWh), mean hourly wind turbine energy output (kWh) and mean hourly PV + wind energy output (kWh) with corresponding total hourly energy demand (kWh) of occupant A for schedule 0.</p>
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<p>Mean hourly PV energy output (kWh), mean hourly wind turbine energy output (kWh) and mean hourly PV + wind energy output (kWh) with corresponding total hourly energy demand (kWh) of occupant A for schedule 1.</p>
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<p>(<b>a</b>) Linear trend graph of correlation between practicality and monetary buying cost (EUR) when using the no renewable energy microgrid (no REµG) configuration or grid priority control for occupant A (MBCg) (feed-in tariff not applicable); (<b>b</b>) linear trend graph of correlation between practicality and carbon quantity (kg) for the no REµG configuration or grid priority control for occupant A (CQg).</p>
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<p>(<b>a</b>) Linear trend graph of correlation between practicality and aggregate cost (EUR) when using the load priority control for the PV renewable energy microgrid (REµG) configuration for occupant A with no feed-in tariff (FIT0) (AC0p); (<b>b</b>) linear trend graph of correlation between practicality and aggregate cost (EUR) when using the load priority control for the wind REµG configuration for occupant A at FIT0 (AC0w).</p>
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<p>(<b>a</b>) Linear trend graph of correlation between practicality and aggregate cost (EUR) when using the load priority control for the PV + wind renewable energy microgrid (REµG) configuration for occupant A with no feed-in tariff (FIT0) (AC0pw); (<b>b</b>) linear trend graph of correlation between practicality and carbon quantity (kg) when using the load-priority control application for the PV REµG configuration for occupant A (CQp).</p>
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<p>(<b>a</b>) Linear trend graph of correlation between practicality and carbon quantity (kg) when using the load priority for wind for occupant A (CQw); (<b>b</b>) linear trend graph of correlation between practicality and carbon quantity (kg) when using the load priority control for the PV + wind renewable energy microgrid (REµG) configuration for occupant A (CQpw).</p>
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<p>(<b>a</b>) Linear trend graph of correlation between practicality and aggregate cost (EUR) when using the load-priority microgrid control for the PV + wind renewable energy microgrid (REµG) configuration for occupant A at 0.09 EUR/kWh feed-in tariff (FIT9) (AC9pw); (<b>b</b>) linear trend graph of correlation between practicality and aggregate cost (EUR) when using the load-priority microgrid control for the PV + wind REµG configuration for occupant A at 0.19 EUR/kWh feed-in tariff (FIT19) (AC19pw).</p>
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<p>(<b>a</b>) Linear trend graph of correlation between practicality and aggregate cost (EUR) when using the load-priority microgrid control for PV renewable energy microgrid (REµG) configuration for occupant A at 0.09 EUR/kWh feed-in tariff (FIT9) (AC9p); (<b>b</b>) Linear trend graph of correlation between practicality and aggregate cost (EUR) when using the load-priority microgrid control for PV renewable energy microgrid (REµG) configuration for occupant A at 0.19 EUR/kWh feed-in tariff (FIT19) (AC19p).</p>
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<p>(<b>a</b>) Linear trend graph of correlation between practicality and aggregate cost (EUR) when using the load-priority microgrid control for the wind renewable energy microgrid (REµG) configuration for occupant A at 0.09 EUR/kWh feed-in tariff (FIT9) (AC9w); (<b>b</b>) linear trend graph of correlation between practicality and aggregate cost (EUR) when using the load-priority microgrid control for the wind REµG configuration for occupant A at 0.19 EUR/kWh feed-in tariff (FIT19) (AC9w).</p>
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<p>Base work schedule (0) and generated schedules (1–5) for occupant A, with practicality ratings and daily working hours displayed. W hours denotes daily working hours, IO denotes the occupant was in the office with electrical devices switched on IO* denotes the occupant was in the office and made a cup of tea using the hot kettle, L denotes the occupant left the office and went to the lecture hall, and the electrical devices at his or her workstation were on standby mode, and OF denotes the occupant was out of the office for lunch break or personal reasons, and the electrical devices were on standby mode.</p>
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<p>Mean hourly PV energy output (kWh), mean hourly wind turbine energy output (kWh) and mean hourly PV + wind energy output (kWh) with corresponding total hourly energy demand (kWh) of occupant A for schedule 2.</p>
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<p>Mean hourly PV energy output (kWh), mean hourly wind turbine energy output (kWh) and mean hourly PV + wind energy output (kWh) with corresponding total hourly energy demand (kWh) of occupant A for schedule 3.</p>
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<p>Mean hourly PV energy output (kWh), mean hourly wind turbine energy output (kWh) and mean hourly PV + wind energy output (kWh) with corresponding total hourly energy demand (kWh) of occupant A for schedule 4.</p>
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<p>Mean hourly PV energy output (kWh), mean hourly wind turbine energy output (kWh) and mean hourly PV + wind energy output (kWh) with corresponding total hourly energy demand (kWh) of occupant A for schedule 5.</p>
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25 pages, 8614 KiB  
Article
Techno-Economic Analysis of Combined Production of Wind Energy and Green Hydrogen on the Northern Coast of Mauritania
by Varha Maaloum, El Moustapha Bououbeid, Mohamed Mahmoud Ali, Kaan Yetilmezsoy, Shafiqur Rehman, Christophe Ménézo, Abdel Kader Mahmoud, Shahab Makoui, Mamadou Lamine Samb and Ahmed Mohamed Yahya
Sustainability 2024, 16(18), 8063; https://doi.org/10.3390/su16188063 - 14 Sep 2024
Viewed by 1241
Abstract
Green hydrogen is becoming increasingly popular, with academics, institutions, and governments concentrating on its development, efficiency improvement, and cost reduction. The objective of the Ministry of Petroleum, Mines, and Energy is to achieve a 35% proportion of renewable energy in the overall energy [...] Read more.
Green hydrogen is becoming increasingly popular, with academics, institutions, and governments concentrating on its development, efficiency improvement, and cost reduction. The objective of the Ministry of Petroleum, Mines, and Energy is to achieve a 35% proportion of renewable energy in the overall energy composition by the year 2030, followed by a 50% commitment by 2050. This goal will be achieved through the implementation of feed-in tariffs and the integration of independent power generators. The present study focused on the economic feasibility of green hydrogen and its production process utilizing renewable energy resources on the northern coast of Mauritania. The current investigation also explored the wind potential along the northern coast of Mauritania, spanning over 600 km between Nouakchott and Nouadhibou. Wind data from masts, Lidar stations, and satellites at 10 and 80 m heights from 2022 to 2023 were used to assess wind characteristics and evaluate five turbine types for local conditions. A comprehensive techno-economic analysis was carried out at five specific sites, encompassing the measures of levelized cost of electricity (LCOE) and levelized cost of green hydrogen (LCOGH), as well as sensitivity analysis and economic performance indicators. The results showed an annual average wind speed of 7.6 m/s in Nouakchott to 9.8 m/s in Nouadhibou at 80 m. The GOLDWIND 3.0 MW model showed the highest capacity factor of 50.81% due to its low cut-in speed of 2.5 m/s and its rated wind speed of 10.5 to 11 m/s. The NORDEX 4 MW model forecasted an annual production of 21.97 GWh in Nouadhibou and 19.23 GWh in Boulanoir, with the LCOE ranging from USD 5.69 to 6.51 cents/kWh, below the local electricity tariff, and an LCOGH of USD 1.85 to 2.11 US/kg H2. Multiple economic indicators confirmed the feasibility of wind energy and green hydrogen projects in assessed sites. These results boosted the confidence of the techno-economic model, highlighting the resilience of future investments in these sustainable energy infrastructures. Mauritania’s north coast has potential for wind energy, aiding green hydrogen production for energy goals. Full article
(This article belongs to the Special Issue Renewable Energy, Electric Power Systems and Sustainability)
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Graphical abstract

Graphical abstract
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<p>Map of the average annual wind speed pattern in Mauritania.</p>
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<p>Locations of meteorological measurement masts in Mauritania.</p>
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<p>Physical site photos of meteorological measurement masts in Mauritania. (<b>a</b>): Nouadhibou Measurement Mast; (<b>b</b>): Boulanoir Measurement Mast; (<b>c</b>): ZX300 Lidar with Solar Power Supply; (<b>d</b>): Nouakchott Measurement Mast with Its Equipment.</p>
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<p>Variation in wind speed on a monthly basis for the five different locations (Nouakchott, Nouamghar, Tasiast, Boulanoir, and Nouadhibou) from 2022 to 2023.</p>
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<p>Wind rose diagrams (N: north; NE: north–east; E: east; SE: south–east; S: south; SW: south–west; W: west; NW: north–west) for the five sites (Nouakchott, Nouamghar, Tasiast, Boulanoir, and Nouadhibou).</p>
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<p>Frequency distribution of wind along with the Weibull distribution curve for Nouakchott, Nouamghar, and Tasiast.</p>
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<p>Frequency distribution of wind along with the Weibull distribution curve for Boulanoir and Nouadhibou.</p>
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<p>Variations in wind speeds throughout different seasons and times of the day at 80 m for Nouakchott, Nouamghar, and Tasiast during the period 2022–2023.</p>
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<p>Variations in wind speeds throughout different seasons and times of the day at 80 m for Boulanoir and Nouadhibou during the period 2022–2023.</p>
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17 pages, 2605 KiB  
Article
The Impact of Participation Ratio and Bidding Strategies on New Energy’s Involvement in Electricity Spot Market Trading under Marketization Trends—An Empirical Analysis Based on Henan Province, China
by Liqing Zhang, Chunzheng Tian, Zhiheng Li, Shuo Yin, Anbang Xie, Peng Wang and Yihong Ding
Energies 2024, 17(17), 4463; https://doi.org/10.3390/en17174463 - 5 Sep 2024
Viewed by 701
Abstract
As new-energy electricity increasingly enters the post-subsidy era, traditional fixed feed-in tariffs and guaranteed purchase policies are not conducive to the optimal allocation of large-scale, high-proportion new-energy power due to the high pressure of subsidy funds and the fairness issues of power-generation grid [...] Read more.
As new-energy electricity increasingly enters the post-subsidy era, traditional fixed feed-in tariffs and guaranteed purchase policies are not conducive to the optimal allocation of large-scale, high-proportion new-energy power due to the high pressure of subsidy funds and the fairness issues of power-generation grid connection. Encouraging new energy to participate in electricity market transactions is considered an effective solution. However, existing studies have presupposed the adverse effects of new energy in proposing market mechanism optimization designs for new-energy participation without quantitative results to support this, which is not conducive to a true assessment of the comprehensive impact of individual instances of new-energy participation in the market. To this end, this study, based on the actual experience and data cases of China’s electricity spot market pilot provinces, considers both unit commitment and economic dispatch in the electricity distribution process, and constructs a two-stage optimization model for electricity spot market clearing. According to the differences in grid connection time and the construction costs of new-energy power, differentiated proportions of new-energy participation in the market and bidding strategies are set. By analyzing the quantitative results of new energy participating in spot market transactions under multiple scenarios, using both typical daily data for normal loads and peak loads, the study provides theoretical support and a data basis for the optimized design of market mechanisms. The research results show that there is a non-linear relationship between the scale of new energy entering the market and its bidding strategies and market-clearing electricity prices. In the transition phase of the low-carbon transformation of the power sector, the impacts of thermal power technology with a certain generation capacity and changes in the relationship between power supply and demand on electricity prices are significant. From the perspective of the individual interests of new-energy providers, the analysis of their bidding strategies in the market is important. Full article
(This article belongs to the Section C: Energy Economics and Policy)
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Figure 1
<p>Model solving process.</p>
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<p>Design of the basic mechanism for new energy to participate in market transaction.</p>
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<p>Typical daily load demand. (<b>a</b>) Typical daily load demand in spring; (<b>b</b>) typical daily load demand in summer.</p>
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<p>Prediction of new-energy output. (<b>a</b>) Wind power output; (<b>b</b>) PV power output.</p>
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<p>Real-time clearing prices in the spot market in different seasons in P-BAU scenario. (<b>a</b>) Spring; (<b>b</b>) summer.</p>
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<p>Spot market power generation dispatch results in different seasons in P-BAU scenario. (<b>a</b>) Spring; (<b>b</b>) summer.</p>
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<p>Real-time clearing prices in the spot market in different seasons in P-LSN scenario. (<b>a</b>) Spring; (<b>b</b>) summer.</p>
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<p>Power dispatch results for the AA-BAU and AA-LSN scenarios. (<b>a</b>) Spring, 10%; (<b>b</b>) spring, 50%; (<b>c</b>) summer, 10%; (<b>d</b>) summer, 50%.</p>
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<p>Changes in new-energy quotations and market-clearing price in summer.</p>
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26 pages, 6258 KiB  
Article
Comparison between Blockchain P2P Energy Trading and Conventional Incentive Mechanisms for Distributed Energy Resources—A Rural Microgrid Use Case Study
by Alain Aoun, Mehdi Adda, Adrian Ilinca, Mazen Ghandour and Hussein Ibrahim
Appl. Sci. 2024, 14(17), 7618; https://doi.org/10.3390/app14177618 - 28 Aug 2024
Viewed by 811
Abstract
Peer-to-Peer (P2P) energy trading is a new financial mechanism that can be adopted to incentivize the development of distributed energy resources (DERs), by promoting the selling of excess energy to other peers on the network at a negotiated rate. Current incentive programs, such [...] Read more.
Peer-to-Peer (P2P) energy trading is a new financial mechanism that can be adopted to incentivize the development of distributed energy resources (DERs), by promoting the selling of excess energy to other peers on the network at a negotiated rate. Current incentive programs, such as net metering (NEM) and Feed-in-Tariff (FiT), operate according to a centralized policy framework, where energy is only traded with the utility, the state-owned grid authority, the service provider, or the power generation/distribution company, who also have the upper hand in deciding on the rates for buying the excess energy. This study presents a comparative analysis of three energy trading mechanisms, P2P energy trading, NEM, and FiT, within a rural microgrid consisting of two prosumers and four consumers. The microgrid serves as a practical testbed for evaluating the economic impacts of these mechanisms, through simulations considering various factors such as energy demand, production variability, and energy rates, and using key metrics such as economic savings, annual energy bill, and wasted excess energy. Results indicate that while net metering and FiT offer stable financial returns for prosumers, P2P trading demonstrates superior flexibility and potentially higher economic benefits for both prosumers and consumers by aligning energy trading with real-time market conditions. The findings offer valuable insights for policymakers and stakeholders seeking to optimize rural energy systems through innovative trading mechanisms. Full article
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Figure 1
<p>Evolution of the electric grid.</p>
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<p>Dependent, independent, and interdependent energy markets.</p>
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<p>Blockchain-based P2P energy trading model.</p>
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<p>Simulated microgrid model.</p>
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<p>Simulation algorithm flow chart.</p>
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<p>Monthly solar radiation profile.</p>
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<p>Total yearly gains—scenario 1.</p>
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<p>Total yearly gains—scenario 2.</p>
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<p>Additional gains generated by the addition of BESS.</p>
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<p>Total P2P gains comparison between all scenarios.</p>
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<p>Consumers savings from buying energy from Prosumers A and B.</p>
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<p>Consumers savings from buying energy from Prosumers A′ and B.</p>
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<p>Scenario 1 NPV—all prosumers.</p>
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<p>Scenario 2 NPV—all prosumers.</p>
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<p>Scenario 3 NPV—all prosumers (P2P).</p>
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<p>PV size impact on total savings—Prosumer A.</p>
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<p>PV size impact on total savings—Prosumer B.</p>
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23 pages, 5050 KiB  
Article
Comparative Analysis of Reinforcement Learning Approaches for Multi-Objective Optimization in Residential Hybrid Energy Systems
by Yang Xu, Yanxue Li and Weijun Gao
Buildings 2024, 14(9), 2645; https://doi.org/10.3390/buildings14092645 - 26 Aug 2024
Viewed by 1106
Abstract
The rapid expansion of renewable energy in buildings has been expedited by technological advancements and government policies. However, including highly permeable intermittent renewables and energy storage presents significant challenges for traditional home energy management systems (HEMSs). Deep reinforcement learning (DRL) is regarded as [...] Read more.
The rapid expansion of renewable energy in buildings has been expedited by technological advancements and government policies. However, including highly permeable intermittent renewables and energy storage presents significant challenges for traditional home energy management systems (HEMSs). Deep reinforcement learning (DRL) is regarded as the most efficient approach for tackling these problems because of its robust nonlinear fitting capacity and capability to operate without a predefined model. This paper presents a DRL control method intended to lower energy expenses and elevate renewable energy usage by optimizing the actions of the battery and heat pump in HEMS. We propose four DRL algorithms and thoroughly assess their performance. In pursuit of this objective, we also devise a new reward function for multi-objective optimization and an interactive environment grounded in expert experience. The results demonstrate that the TD3 algorithm excels in cost savings and PV self-consumption. Compared to the baseline model, the TD3 model achieved a 13.79% reduction in operating costs and a 5.07% increase in PV self-consumption. Additionally, we explored the impact of the feed-in tariff (FiT) on TD3’s performance, revealing its resilience even when the FiT decreases. This comparison provides insights into algorithm selection for specific applications, promoting the development of DRL-driven energy management solutions. Full article
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<p>Structure of the examined building energy system.</p>
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<p>The comprehensive diagram of the HEMS optimization approach.</p>
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<p>The learning procedure of the DDPG’s interaction with the environment.</p>
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<p>The average monthly distribution of electricity demand, thermal demand, PV generation, and electricity prices.</p>
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<p>Changes in the FiT in Japan.</p>
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<p>Average episode rewards during the training process.</p>
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<p>Thermal visualization of PV self-consumption rate.</p>
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<p>The optimal battery operation strategy of different DRL models under a typical working week.</p>
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<p>The optimal heat pump operation strategy of different DRL models under a typical working week in the heating season.</p>
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<p>The optimal heat pump operation strategy of different DRL models under a typical working week in the transition season.</p>
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<p>The optimal heat pump operation strategy of different DRL models under a typical working week in the cooling season.</p>
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<p>Cost optimization effect of TD3 algorithm with different FiT.</p>
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25 pages, 14771 KiB  
Article
Model Predictive Controlled Parallel Photovoltaic-Battery Inverters Supporting Weak Grid Environment
by Fatma Selim, Mokhtar Aly, Tamer F. Megahed, Masahito Shoyama and Sobhy M. Abdelkader
Sustainability 2024, 16(17), 7261; https://doi.org/10.3390/su16177261 - 23 Aug 2024
Viewed by 715
Abstract
The hybrid photovoltaic (PV) with energy storage system (ESS) has become a highly preferred solution to replace traditional fossil-fuel sources, support weak grids, and mitigate the effects of fluctuated PV power. The control of hybrid PV-power systems as generation-storage and their injected active/reactive [...] Read more.
The hybrid photovoltaic (PV) with energy storage system (ESS) has become a highly preferred solution to replace traditional fossil-fuel sources, support weak grids, and mitigate the effects of fluctuated PV power. The control of hybrid PV-power systems as generation-storage and their injected active/reactive power for the grid side present critical challenges in optimizing their performance. Therefore, this paper introduces hybrid PV-battery parallel inverters employing a finite control set model predictive control (FCSMPC) method. The proposed FCSMPC-based controller and inverter system achieves multiple functionalities, including maximum power extraction from PV, proper charging/discharging commands for ESS, support for weak grid conditions, support during low-voltage ride-through (LVRT) by increasing reactive power injection to counteract the drop in grid voltage, and economic management based on feed-in-tariff (FiT). The controller significantly improves the performance of the PV-battery system under faulty LVRT conditions and unbalanced grid voltages, satisfying grid code requirements while continuously supplying the microgrid’s delicate local load. A real-time simulation hardware-in-the-loop (HiL) setup, utilizing the OPAL-RT platform, is employed to implement the proposed hybrid PV–ESS with its controller. The results affirm the superior ability of FCSMPC in weak-grid conditions and its capability to achieve multiple objectives simultaneously. Full article
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Figure 1
<p>The schematic diagram of the complete studied system.</p>
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<p>The integration of PV and ESS with the PCC and grid side.</p>
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<p>The space vector diagram for three-level NPC topology.</p>
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<p>Current paths (<math display="inline"><semantics> <msub> <mi>i</mi> <mi>o</mi> </msub> </semantics></math>) for three-level NPC leg. (<b>a</b>) Current path during P state (figure in left side is for positive current, and figure in right side is for negative current). (<b>b</b>) Current path during O state (figure on the left side is for positive current, and figure on the right side is for negative current). (<b>c</b>) Current path during N state (figure on the left side is for positive current, and figure on the right side is for negative current).</p>
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<p>The proposed control diagram for three-level NPC topology.</p>
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<p>The real-time simulation HiL configuration employed in the OPAL-RT 4510 platform.</p>
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<p>Response to a dramatic drop in grid voltage: (<b>a</b>) PV side response, (<b>b</b>) ESS and load current side response, and (<b>c</b>) grid side response.</p>
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<p>Investigation under conditions of imbalanced grid voltages: (<b>a</b>) PV side response, (<b>b</b>) ESS and load current side response, and (<b>c</b>) grid side response.</p>
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<p>Response to a dynamic change in PV power (reduction by <math display="inline"><semantics> <mrow> <mn>20</mn> <mo>%</mo> </mrow> </semantics></math>), (<b>a</b>) PV side response, (<b>b</b>) ESS and load current side response, and (<b>c</b>) grid side response.</p>
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<p>The system’s behavior under high FiT: (<b>a</b>) PV side response, (<b>b</b>) ESS and load current side response, and (<b>c</b>) grid side response.</p>
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<p>The system’s behavior under low FiT: (<b>a</b>) PV side response, (<b>b</b>) ESS and load current side response, and (<b>c</b>) grid side response.</p>
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<p>The system’s response during PV power uncertainty: (<b>a</b>) irradiation level and PV current response, (<b>b</b>) PV voltage and PV power response, (<b>c</b>) PV, ESS, grid, and load current response, and (<b>d</b>) PV, ESS, grid, and load power response.</p>
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<p>The system’s performance during load uncertainty: (<b>a</b>) PV, ESS, grid, and load current response; (<b>b</b>) PV, ESS, grid, and load power response.</p>
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<p>Response of the control system to parameter variations: (<b>a</b>) capacitors’ voltages when <math display="inline"><semantics> <mrow> <msub> <mi>C</mi> <mn>1</mn> </msub> <mo>=</mo> <msub> <mi>C</mi> <mn>2</mn> </msub> </mrow> </semantics></math>, and (<b>b</b>) capacitors’ voltages when <math display="inline"><semantics> <mrow> <msub> <mi>C</mi> <mn>1</mn> </msub> <mo>=</mo> <mn>0.5</mn> <msub> <mi>C</mi> <mn>2</mn> </msub> </mrow> </semantics></math>.</p>
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<p>The system’s response during the reduction in PV power with MPC control (<b>left</b>) and PI control (<b>right</b>): (<b>a</b>) Step change in the input PV power, (<b>b</b>) PV current response, (<b>c</b>) ESS current response, and (<b>d</b>) grid current response.</p>
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<p>The THD of the PV current: (<b>a</b>) PV current THD when employing MPC control, (<b>b</b>) PV current THD when employing PI control.</p>
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16 pages, 1861 KiB  
Article
Sustainable Energy Practices in Thailand and Japan: A Comparative Analysis
by Su Wutyi Hnin, Amna Javed, Jessada Karnjana, Chawalit Jeenanunta and Youji Kohda
Sustainability 2024, 16(16), 6877; https://doi.org/10.3390/su16166877 - 10 Aug 2024
Viewed by 957
Abstract
This study investigates the comparative analysis of the divergent pathways of sustainable energy development in Thailand and Japan. It offers a nuanced analysis of their policy frameworks, technological advancements, and socioeconomic contexts. This study elucidates the distinct strategies of the two nations by [...] Read more.
This study investigates the comparative analysis of the divergent pathways of sustainable energy development in Thailand and Japan. It offers a nuanced analysis of their policy frameworks, technological advancements, and socioeconomic contexts. This study elucidates the distinct strategies of the two nations by leveraging a robust dataset from sources including the Electricity Generating Authority of Thailand (EGAT) and Japan’s Agency for Natural Resources and Energy (ANRE) toward renewable energy. The key findings indicate that Thailand has capitalized on policy instruments such as the Alternative Energy Development Plan 2018 (AEDP 2018) to augment its renewable energy capacity, particularly in the solar and biomass sectors. This policy-driven approach addresses the rural–urban energy divide and enhances energy access nationwide. Conversely, Japan’s trajectory is characterized by integrating technological innovations like smart grids and the Feed-in Tariff (FiT) system, which have catalyzed significant increases in solar energy adoption and efficiency. Japan places great emphasis on technological solutions that underscore its strategy to mitigate the legacy constraints of energy infrastructure post-Fukushima. The implications of these findings are extended beyond national borders, offering critical insights into the complex interplay between policy, technology, and social engagement in the renewable energy transition. This study highlights the potential for community-based renewable energy projects in Thailand to drive economic growth and social equity. At the same time, Japan’s experience illustrates the importance of regulatory reforms and technological leadership in overcoming structural barriers to energy innovation. These insights are particularly relevant for policymakers and stakeholders aiming to balance the imperatives of energy security, economic development, and environmental sustainability. Finally, this study emphasizes the need for tailored strategies that align renewable energy adoption with the unique contexts of each country, thereby enhancing global efforts against climate change. Full article
(This article belongs to the Section Energy Sustainability)
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<p>Energy consumption trends in Thailand and Japan (2010–2022).</p>
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<p>Renewable energy capacity growth in Thailand and Japan (2010–2022).</p>
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<p>Renewable energy capacity growth in Thailand.</p>
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<p>Renewable energy capacity growth in Japan.</p>
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30 pages, 2718 KiB  
Article
Costs and Benefits of Citizen Participation in the Energy Transition: Investigating the Economic Viability of Prosumers on Islands—The Case of Mayotte
by Lukas Otte, Nikolas Schöne, Anna Flessa, Panagiotis Fragkos and Boris Heinz
Energies 2024, 17(12), 2904; https://doi.org/10.3390/en17122904 - 13 Jun 2024
Viewed by 625
Abstract
Citizen-driven approaches are promising to overcome the challenges in the energy transition of geographical islands. However, the economic profitability of related activities must be ensured to achieve the intrinsic and sustainable uptake of related solutions in an island’s communities. Here, we investigate the [...] Read more.
Citizen-driven approaches are promising to overcome the challenges in the energy transition of geographical islands. However, the economic profitability of related activities must be ensured to achieve the intrinsic and sustainable uptake of related solutions in an island’s communities. Here, we investigate the long-term (2020–2054) economic profitability of solar-based prosumption on islands belonging to the European Union (EU), soft-linking energy system modelling and actor-related cash-flow analysis. This combination considerably extends common assessments of the profitability of renewable energy technology and long-term projections of island energy systems. We base our case study on the French overseas territory of Mayotte, discussing household affordability and the socio-economic impact of prosumerism. These topics are relevant to transferability on non-EU islands. The profitability of investments in PV depends on (i) the size of the PV system, with larger systems (>9 kWp) profiting from lower specific investment costs compared to smaller systems; (ii) the time of investment, with more profitable investments to be expected in early periods; (iii) the level of decarbonization of the entire energy sector, with an ongoing decarbonization reducing the compensation or energy-saving possibilities; and (iv) the market behavior, with the practice of feeding in all electricity produced rather than self-consuming energy offering a higher expected return on investment under current feed-in-tariff (FiT) compensation schemes. We introduce various policy measures to improve solar rooftop PV profitability and discuss their trade-offs and effectiveness. While indirect subsidies via FiT are generally effective in improving PV profitability, they undermine efforts to incentivize decentralized self-consumption. From the perspective of harmonizing efforts in the energy transition of African and European islands, we recommend a careful evaluation of the trade-offs in relevant regulations required for the economic incentivization of prosumers to achieve compatibility with the principles of a citizen-driven and just energy transition. Particular attention must be paid to context-specific socio-economic characteristics, including low access to financial resources and non-financial access barriers, including legal status. Full article
(This article belongs to the Section B: Energy and Environment)
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<p>Overview of soft link of energy system modelling and cash-flow analysis.</p>
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<p>Overview of assumptions, price projections, cash flows, and financial indicators used in the assessment.</p>
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<p>Projected feed-in tariff on Mayotte for producers with 100 kWp PV systems (<b>left</b>), and prosumers with different PV sizes (<b>right</b>) and electricity price, as resulting from the energy system modeling.</p>
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<p>(<b>a</b>–<b>d</b>). FiT and LCOE for different PV sizes and prosumers. (<b>e</b>) FiT, LCOE, and pre-tax electricity price for producers with 36 to 100 kWp.</p>
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<p>NPV (EUR thousand) of rooftop solar PV for different prosumer classes over economic lifetime, with 1 MW installed capacity at 30% maximum self-consumption (prosumers).</p>
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<p>Sensitivity analysis for the investment in a 3 kWp system under the assumption of 30% potential self-consumption for the baseline and decarbonization scenario. The values are set in relation to the reference parameters assumed in the reference case.</p>
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<p>NPV [EUR 1000] for the investment in a 3 kWp PV asset (1 MW installed capacity) under different levels of decarbonization and self-consumption rates.</p>
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16 pages, 1956 KiB  
Article
Satisfaction with Rooftop Photovoltaic Systems and Feed-in-Tariffs Effects on Energy and Environmental Goals in Jordan
by Abbas Al-Refaie and Natalija Lepkova
Processes 2024, 12(6), 1175; https://doi.org/10.3390/pr12061175 - 7 Jun 2024
Cited by 1 | Viewed by 598
Abstract
Rooftop photovoltaic (RPV) systems are valuable clean-energy-efficient technology that facilitates the transition toward energy sustainability in residential buildings. Hence, the government in Jordan implemented the feed-in-tariffs (FiT) policy to motivate residents’ willingness to install RPV systems. However, the quality of RPV products and [...] Read more.
Rooftop photovoltaic (RPV) systems are valuable clean-energy-efficient technology that facilitates the transition toward energy sustainability in residential buildings. Hence, the government in Jordan implemented the feed-in-tariffs (FiT) policy to motivate residents’ willingness to install RPV systems. However, the quality of RPV products and services is a key determinant of social acceptance to install RPV systems. Hence, manufacturers and suppliers are working closely with adopters to design and manufacture RPV systems that meet or exceed their expectations. Still, there is a need to develop a quantitative assessment to examine the effects of this FiT policy and the quality of RPV systems on energy security. This study, therefore, develops a system dynamics model to examine the effects of the FiT policy and the quality of RPV products and services on social acceptance to install RPV systems. To achieve this objective, several hypotheses were established related to the main model factors, including the quality of services, complaint reduction, performance ratio, payback period and warranty, and FiT price, with a willingness to install RPV systems. Then, a system dynamics model was constructed. The simulation results reveal the significant factor that impacts energy goals. Moreover, from the end of the year 2030 to the end of 2050, RPV installations, generated power, and CO2 emission reductions are expected to increase from 0.681 GW to 72.83 GW, from 1.07 to 125.74 TWh, and from 0.680 to 79.59 million tons of CO2, respectively. Optimization was performed to maximize the three objectives under the uncertainty of key model variables. The optimal factor values can significantly increase the current energy goals by about 20%. In conclusion, collecting, analyzing, and evaluating adopter input and feedback on RPV systems regarding their design and technology and manufacturing and the post-services of RPV systems significantly influence energy sustainability in residential buildings. In addition, government support through investing in the FiT policy can boost RPV installations in residential buildings. Full article
(This article belongs to the Special Issue Optimal Design for Renewable Power Systems)
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<p>Model hypotheses.</p>
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<p>Satisfaction model.</p>
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<p>FiT policy model.</p>
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<p>Adopter satisfaction and FiT policy relationships with energy objectives.</p>
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<p>Simulation results.</p>
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<p>Simulation results.</p>
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<p>The cost of RPV systems (USD) versus time (Year).</p>
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<p>The cumulative FiT cost (USD million) versus time (Year).</p>
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12 pages, 1119 KiB  
Article
Techno-Economic Assessment of Anaerobic Digestion Technology for Small- and Medium-Sized Animal Husbandry Enterprises
by Alexandros Eftaxias, Iliana Kolokotroni, Christos Michailidis, Panagiotis Charitidis and Vasileios Diamantis
Appl. Sci. 2024, 14(11), 4957; https://doi.org/10.3390/app14114957 - 6 Jun 2024
Viewed by 1016
Abstract
Investments in small and medium-sized anaerobic digestion facilities have the potential to boost biogas production in Greece and other EU countries. This study aimed to evaluate the economic feasibility of anaerobic digestion facilities equipped with combined heat and power (CHP) units ranging from [...] Read more.
Investments in small and medium-sized anaerobic digestion facilities have the potential to boost biogas production in Greece and other EU countries. This study aimed to evaluate the economic feasibility of anaerobic digestion facilities equipped with combined heat and power (CHP) units ranging from 50 to 400 kW, while treating livestock waste. For this purpose, data were gathered from various livestock operations (dairy cattle, poultry, swine, dairy sheep and goats) regarding their annual production, revenues, electricity and fuel usage, and waste generation. Waste samples were then collected and analyzed to assess their biochemical methane production potential. The capital and operational costs of anaerobic digestion facilities, from 50 and 400 kW, were calculated using the equations developed within the “eMT cluster” project. Findings indicate that current feed-in tariffs (FITs) of 0.21 € kWh−1 are insufficient to incentivize investment in anaerobic digestion facilities with capacities below 250 kW, highlighting the need for increased FIT rates or capital expenditure subsidies. Recommendations include shifting towards simplified technology and business models with reduced farmer involvement, coupled with supportive legislative framework and long-term electricity price guarantees. These measures are expected to foster the implementation of anaerobic digestion projects in the animal husbandry sector. Full article
(This article belongs to the Section Environmental Sciences)
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<p>Cumulative methane production curves from the batch anaerobic digestion of different livestock wastes samples.</p>
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<p>(<b>a</b>) Capital expenses (CAPEX) in € per kW installed CHP electric power, and (<b>b</b>) operational expenses (OPEX) per kW installed CHP electric power, for small-size anaerobic digestion facilities, as a function of the CHP installed electric power.</p>
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<p>(<b>a</b>) Capital expenses (CAPEX), operational expenses (OPEX) and total cost (TOTAL) per m<sup>3</sup> of methane produced and kWh<sup>−1</sup> electricity generated, and (<b>b</b>) energy sustainability index (ESI) and electricity consumption as a percentage of produced electrical energy, for small-size anaerobic digestion facilities, as a function of the CHP installed electric power.</p>
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<p>Investment payback period for anaerobic digestion facilities as a function of the CHP installed electric power, for different feed-in tariffs (FIT) and construction subsidies. The FIT was considered as 210 € MWh<sup>−1</sup> (FIT210) or 260 € MWh<sup>−1</sup> (FIT260) and the construction subsidy equal to 70% of the CAPEX (CAPEX70).</p>
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17 pages, 331 KiB  
Article
The Relationship between Transparency Obligations and Foreign Investment in Renewable Energies: Realising the Potential Role of IIAs
by Xuming Qian and Mohammad Akefi Ghaziani
Energies 2024, 17(11), 2721; https://doi.org/10.3390/en17112721 - 3 Jun 2024
Viewed by 613
Abstract
The global deployment of renewable energies has taken off and calls for a continuous increase in foreign investments and cooperation, particularly because many states cannot cover the costs and technological requirements of the energy transition on their own. Therefore, there should be policies [...] Read more.
The global deployment of renewable energies has taken off and calls for a continuous increase in foreign investments and cooperation, particularly because many states cannot cover the costs and technological requirements of the energy transition on their own. Therefore, there should be policies and legal frameworks in place to protect and thereby promote foreign investments. International Investment Agreements (IIAs) can, ceteris paribus, contribute to this goal. These agreements contain a set of obligations that protect foreign investments against possible discriminatory or arbitrary conduct of the host states. This includes transparency obligations that can help to create a level playing field for national and foreign renewable energy investors. Unfortunately, the concept of transparency, and its inherent implications, has not been clearly defined to date, and its relationship with renewable energy investments is still under investigation. Therefore, it is important to realise the prevailing transparency obligations under IIAs, and the best practices that can better meet the particular requirements of renewable energy investments. Using a qualitative approach, this article intends to pursue this goal by providing an overview of the concept of transparency, exploring its status in the context of fair and equitable treatment (FET), and analysing favourable transparency clauses in the light of renewable energy investment considerations. Full article
17 pages, 2531 KiB  
Article
Prediction and Feed-In Tariffs of Municipal Solid Waste Generation in Beijing: Based on a GRA-BiLSTM Model
by Xia Zhang and Bingchun Liu
Sustainability 2024, 16(9), 3579; https://doi.org/10.3390/su16093579 - 24 Apr 2024
Viewed by 998
Abstract
To cope with the increasing energy demand of people and solve the problem of a “Garbage Siege”, most cities have begun to adopt waste power generation (WTE). Compared to other WTE technologies, incineration has proven to be the most efficient technology for municipal [...] Read more.
To cope with the increasing energy demand of people and solve the problem of a “Garbage Siege”, most cities have begun to adopt waste power generation (WTE). Compared to other WTE technologies, incineration has proven to be the most efficient technology for municipal solid waste (MSW) treatment. Therefore, to further explore the economic feasibility of MSW incineration plant construction, this study established a multi-factor prediction of MSW generation based on the GRA-BiLSTM model. By fully considering the relationship between the change in feed-in tariff (FIT) and the building of an incineration plant in Beijing, the economic feasibility of building an incineration plant is discussed based on the three scenarios set. The experimental results showed that (1) the combined model based on the GRA-BiLSTM showed good applicability for predicting MSW generation in Beijing, with MAE, MAPE, RMSE, and R2 values of 12.47, 5.97%, 18.5580, and 0.8950, respectively. (2) Based on the three scenarios set, the incineration power generation of Beijing MSW will show varying degrees of growth in 2022–2035. In order to meet future development, Beijing needs to build seven new incinerators, and the incineration rate should reach 100%. (3) According to setting different feed-in tariffs, based on the economic feasibility analysis, it is found that the feed-in tariff of MSW incineration for power generation in Beijing should be no less than $0.522/kWh. The government should encourage the construction of incineration plants and give policy support to enterprises that build incineration plants. Full article
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<p>Comparison of the number of municipal domestic waste incineration, sanitary daily treatment capacity, and treatment plants in 31 provinces in 2021.</p>
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<p>The change in per capita municipal waste generation in China and Beijing (2011–2021).</p>
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<p>Bidirectional long short-term memory model structure.</p>
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<p>Generation of Municipal Solid Waste in Beijing (2021–2035).</p>
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<p>The cost, sale price, profit, and cumulative profit of municipal solid waste incineration in Beijing (2021–2035).</p>
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<p>Impact of electricity sales tariff on three scenarios: (<b>a</b>) Scenario 1, (<b>b</b>) Scenario 2, (<b>c</b>) Scenario 3.</p>
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15 pages, 2456 KiB  
Article
Renewable Energy Stocks’ Performance and Climate Risk: An Empirical Analysis
by Lingyu Li, Xianrong Zheng and Shuxi Wang
J. Risk Financial Manag. 2024, 17(3), 121; https://doi.org/10.3390/jrfm17030121 - 18 Mar 2024
Viewed by 1846
Abstract
This article studies the relationship between renewable energy stocks’ performance and climate risk. It shows that publicly held renewable energy stocks underperform as a reaction to climate policy information releases, modeled by feed-in tariff (FIT) legislation announcements. The study examined stock price behaviors [...] Read more.
This article studies the relationship between renewable energy stocks’ performance and climate risk. It shows that publicly held renewable energy stocks underperform as a reaction to climate policy information releases, modeled by feed-in tariff (FIT) legislation announcements. The study examined stock price behaviors 2 days before and 30 days after FIT policy announcements. The stock sample used in the study has 3702 firm-day combinations, which included 180 cleantech firms and 32 events from 2007 to 2017. Based on the residual analysis of the sample’s abnormal return, it indicated that the FIT announcements are associated with significant declines in returns. The cumulative abnormal return until Day 18 was a significant −0.83%, while the average abnormal return on the day was −0.16% at normal levels. The study partially excluded the likelihood of a transitory result by varying the measurement horizon. It also adopted both the market model and the Fama–French three-factor models to rule out model misspecification when estimating abnormal returns and thus increased the robustness. In fact, the results were stable to changes in estimating the model’s specifications. In addition, the study compared the portfolio’s performance with mimicking portfolios in terms of size, book-to-market equity (BE/ME), and the firms’ geographic location. It demonstrated that the documented anomaly of the portfolio of renewable energy companies is robust. Full article
(This article belongs to the Special Issue Finance, Risk and Sustainable Development)
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<p>Total FIT legislation events.</p>
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<p>Yearly FIT legislation events.</p>
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<p>Daily trading volumes.</p>
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<p>Policy announcements and trading volumes.</p>
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<p>Cumulative abnormal returns for different models.</p>
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<p>Cumulative abnormal returns for different event windows.</p>
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<p>Cumulative abnormal returns for different portfolios.</p>
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<p>Cumulative abnormal returns adjusted for different benchmarks.</p>
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34 pages, 3342 KiB  
Article
The Economic Potential of Agrivoltaic Systems in Apple Cultivation—A Hungarian Case Study
by Aidana Chalgynbayeva, Péter Balogh, László Szőllősi, Zoltán Gabnai, Ferenc Apáti, Marianna Sipos and Attila Bai
Sustainability 2024, 16(6), 2325; https://doi.org/10.3390/su16062325 - 12 Mar 2024
Cited by 5 | Viewed by 2829
Abstract
Agrivoltaic systems (AVS) allow the simultaneous use of land—as a limited resource—for crop production and electricity generation. This paper introduces the development prospects of AVS in Hungary with insights into international trends. The most important part is a complex economic analysis and a [...] Read more.
Agrivoltaic systems (AVS) allow the simultaneous use of land—as a limited resource—for crop production and electricity generation. This paper introduces the development prospects of AVS in Hungary with insights into international trends. The most important part is a complex economic analysis and a unit cost analysis of a 38 MWp capacity AVS, considering the most typical basic data in electricity and apple production. The applied risk analysis is based on a Monte Carlo simulation, the distribution function, and probabilities. To introduce the economic facet of the competitiveness of AVS, a comparative analysis was carried out between AVS, ground-mounted photovoltaic (GM-PV) systems, and conventional apple production systems (ConAPS). In the most probable scenario, the AVS was financially attractive (NPV = 70 million EUR under 30 years). Our correlation analysis shows that feed-in tariff (FIT) price and the role of financing are considered the dominant economic factors. A favorable FIT price enhances the profitability of AVS; however, it makes GM-PV systems more profitable compared to AVS, so it negatively affects the competitiveness of AVS systems. AVS operations result in a more balanced unit cost of apples and of electricity compared to the independent operation of GM-PV systems and of ConAPS; in addition, it allows for land saving and more intensive land use. Full article
(This article belongs to the Section Energy Sustainability)
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<p>Basic layout of a single solar PV shed on an agrivoltaic farm. Source: The result of the authors’ calculations using SOLIDWORKS<sup>®</sup> 2022 and <a href="#sustainability-16-02325-t001" class="html-table">Table 1</a> (2024) data.</p>
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<p>Solar PV sheds on an agrivoltaic apple farm. Source: The result of the authors’ calculations using SOLIDWORKS<sup>®</sup> 2022 and <a href="#sustainability-16-02325-t001" class="html-table">Table 1</a> (2024) data.</p>
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<p>Change in output means across range of input values, regarding NPV. Source: own calculation (2024).</p>
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<p>Correlation coefficients (Spearman rank) of the influence of analyzed input and output data on the NPV. Source: own calculation (2024).</p>
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<p>Regression coefficients of the influence of analyzed input and output data on the NPV. Source: own calculation (2024).</p>
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<p>The summary of the Monte Carlo simulations regarding the value of NPV. Source: own calculation (2024).</p>
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<p>Change in outputs means across a range of input values, regarding the unit cost of apple production. Source: own calculation (2024).</p>
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<p>Correlation coefficients (Spearman rank) of the effects of the analyzed input and output data on the unit cost of apple production. Source: own calculation (2024).</p>
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<p>Regression coefficients of the effects of the analyzed input and output data on the unit cost of apple production. Source: own calculation (2024).</p>
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<p>The summary of the Monte Carlo simulations regarding the value of unit cost. Source: own calculation (2024).</p>
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<p>Mean change in outputs across a range of input values regarding unit cost (EUR/Nm<sup>3</sup>). Source: own calculations (2024).</p>
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<p>Correlation coefficients (Spearman rank) of the effects of analyzed input and output data on the unit cost. Source: own calculations (2024).</p>
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<p>Regression coefficients of the effects of analyzed input and output data on the unit cost. Source: own calculations (2023).</p>
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<p>The summary of the Monte Carlo simulations regarding the value of unit cost. Source: own calculations (2023).</p>
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45 pages, 3245 KiB  
Review
Green Certificates Research: Bibliometric Assessment of Current State and Future Directions
by Stamatios K. Chrysikopoulos, Panos T. Chountalas, Dimitrios A. Georgakellos and Athanasios G. Lagodimos
Sustainability 2024, 16(3), 1129; https://doi.org/10.3390/su16031129 - 29 Jan 2024
Cited by 6 | Viewed by 2013
Abstract
In recent years, sustainability initiatives and the prominence of renewables have emerged as pivotal priorities in addressing environmental, ecological, and socioeconomic challenges. Within this context, green certificates—representing proof of electricity generation from renewable sources—have gained substantial recognition, enabling organizations to demonstrate their commitment [...] Read more.
In recent years, sustainability initiatives and the prominence of renewables have emerged as pivotal priorities in addressing environmental, ecological, and socioeconomic challenges. Within this context, green certificates—representing proof of electricity generation from renewable sources—have gained substantial recognition, enabling organizations to demonstrate their commitment to clean energy. This study employs a bibliometric analysis to chart the evolution and current state of green certificates research. Drawing from the Scopus database, we sourced bibliographic data, resulting in a refined dataset of 940 documents spanning from 2000 to 2022. Through performance analysis, we systematically evaluated the landscape of green certificates research, assessing publication trends, identifying influential works, spotlighting prolific authors, highlighting leading academic institutions, mapping regional research hotspots, and pinpointing the top publishing journals in the domain. Employing science mapping techniques—such as co-authorship networks, keyword co-occurrence analysis, and bibliographic coupling—we delineated the collaborative patterns and the conceptual and intellectual structure of the field. This was further augmented by content analysis, revealing four salient research themes, emphasizing the consistent and central focus on support mechanisms and policies for renewable energy sources, sustainable renewable technologies and market dynamics, technological innovations and green certificate trading, and renewable energy sources investment strategies. Building on these findings, the paper concludes by outlining practical implications and prospective research avenues. These encompass a detailed understanding of renewable energy support mechanisms, the pivotal role of electricity disclosure in enhancing transparency, and the transformative potential of emergent technologies, such as artificial intelligence and blockchain, in the green certificate trading landscape. The research also emphasizes the fundamental role of guarantees of origin in advancing sustainability goals, the dynamic discourse on green hydrogen certification standards, and the intricate dynamics of trading mechanisms in shaping investment strategies. Full article
(This article belongs to the Special Issue Governing Green Energy Trade: Challenges and Opportunities)
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<p>Research design.</p>
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<p>Publication and citation trends between 2000 and 2022.</p>
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<p>Author-level co-authorship network.</p>
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<p>Country-level co-authorship network.</p>
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<p>Keyword co-occurrence network.</p>
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<p>Bibliographic coupling network.</p>
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