scholarly journals INVESTIGATING COST-EFFECTIVE POLICY INCENTIVES FOR RENEWABLE ENERGY IN JAPAN: A RECURSIVE CGE APPROACH FOR AN OPTIMAL ENERGY MIX

2020 ◽  
pp. 1-22
Author(s):  
MICHAEL C. HUANG ◽  
CHUL JU KIM
Keyword(s):  
Renewable Energy ◽  
Nuclear Power ◽  
Cost Effective ◽  
Power Dependence ◽  
Energy Mix ◽  
Optimal Energy ◽  
Effective Policy ◽  
Policy Promotion ◽  
Fukushima Nuclear Disaster ◽  
Policy Incentives

The Great East Japan Earthquake (GEJE) and Fukushima nuclear disaster that occurred in 2011 gave a sharp reminder to Japan’s energy security to reconsider the reduction of nuclear power dependence with a better energy mix. We use a recursive CGE model based on Japan’s renewable energy input–output model to analyze the energy composite of power generation and consumption to investigate cost-effective policy incentives to achieve an optimal energy mix to reduce the nuclear power dependence to less than 5% within 10 years. Moreover, we create scenarios of: (1) nuclear power decommission, (2) renewable energy promotion and (3) virtual power plant (VPP) implementation with public R&D expenditure and power infrastructure investment. The simulation results show that renewable energy could gradually replace nuclear power with capital-use subsidies. Under the direction of nuclear power decommission, the VPP installation could reduce the fiscal cost of wind power by 13%, solar energy by 8% and the social cost by 36%. We provide empirical evidence that the implementation of VPP should be promoted in addition to the renewable policy promotion policy to facilitate power allocation and overcapacity problems better.

scholarly journals Optimization of Energy Mix - Nuclear Power and Renewable Energy for Low Emissions Energy Source a Benefit for Generations to Come

2017 ◽  
Vol 112 ◽  
pp. 412-417
Author(s):  
Gabriel Lazaro Pavel ◽  
Andrei Razvan Budu ◽  
Dominic Eugeniu Moraru
Keyword(s):  
Energy Source ◽  
Renewable Energy ◽  
Nuclear Power ◽  
Energy Mix ◽  
To Come

scholarly journals Transformation of the renewable energy structure in Hungary

2020 ◽  
Vol 130 (1) ◽  
pp. 103-112
Author(s):  
Remenyik Bulcsu ◽  
◽  
David Lorant ◽  
Keyword(s):  
Renewable Energy ◽  
Solar Energy ◽  
Nuclear Power ◽  
Renewable Energy Sources ◽  
Wind Farms ◽  
Energy Sources ◽  
Energy Structure ◽  
Energy Mix ◽  
The Government ◽  
The Eu

According to the Eurostat database, the percentage of renewable energy sources in the energy mix of the EU is increasing year after year (www.europa.eu/eurostat). In 2018, the values reached 73% in neighboring Austria (48% in Croatia, 42% in Romania, 32% in Slovenia and 21% in Slovakia), Hungary now stands at 8%. Wind energy is the leader in the renewable energy structure of the EU countries (35, 8% in 2018), since the construction of wind farms is prohibited in our country, one of the main objectives of research is to examine our energy structure. Our last position in renewable energy in 2020 prompted the government to develop a new climate strategy (mekh.hu/nemzetienergiastrategia2030). According to the new strategy, Hungary is estimated to be climate and carbon neutral by the end of the decade. Among the alternative sources of energy, solar energy is becoming the favorite, and our article points out why other forms of energy are being overshadowed. Analyzing the changes in the country’s future energy mix, it can be said that it will be based essentially on nuclear power and electricity from Russia’s electricity grid. However, due to the drastic drop in the cost of solar panels in the energy mix of households, solar energy has become a favorite of renewable energy sources. In the climate strategy, the government plans to create 8,000 MW of solar energy by 2050, which will be mainly used in households, municipal buildings, and tourism.

scholarly journals Demand and Electricity Energy Mix in Indonesia 2030 with Small Modular Reactor Nuclear Power Plant and Renewable Energy Scenario

2021 ◽  
Vol 927 (1) ◽  
pp. 012025
Author(s):  
Lazuwardi Imani ◽  
Ahmad Agus Setiawan ◽  
Mohammad Kholid Ridwan
Keyword(s):  
Renewable Energy ◽  
Power Plant ◽  
Nuclear Power Plant ◽  
Nuclear Power ◽  
Power Plants ◽  
Combined Cycle ◽  
Electricity Sector ◽  
Energy Mix ◽  
General Plan ◽  
Small Modular Reactors

Abstract The interest in small modular reactors worldwide has been increasing due to flexibility in the power generation for more comprehensive users and applications. Small Modular Reactors or SMRs can be the primary choice for Indonesia provided with the geographical condition, which consists of many islands and is more flexible in construction compared to the conventional nuclear power plant. The main objective of this paper is to provide an overview projection of demand and energy mix of electrical in Indonesia 2030 with SMRs NPP in the energy mix referring to RUPTL or General Plan of Electricity Supply Indonesia. Using the end-use model, which is total electricity consumption for each electricity sector, it can be calculated how much electricity demand is from these sectors. The scenario uses RUPTL, roadmap from Energy and Mineral Resources Ministry references, and policy of no coal power plant added from 2020 onwards. The results show in 2030, Indonesia needs 577,016.2 GWh of electricity, where the household and industry sectors have the highest electricity needs, which is 44% for the household sector and 31% for the industry. The transformation projection in PLTGU or Combined Cycle Power Plants scenario also shows that without replacing the power plant, renewable along without SMRs only had ±7.49% of the total capacity mix, and the second scenario with SMRs shows that renewable energy share had 16.07%.

scholarly journals Optimal Energy Management of a Grid-Tied Solar PV-Battery Microgrid: A Reinforcement Learning Approach

Energies ◽  
2021 ◽  
Vol 14 (9) ◽  
pp. 2700
Author(s):  
Grace Muriithi ◽  
Sunetra Chowdhury
Keyword(s):  
Renewable Energy ◽  
Reinforcement Learning ◽  
Energy Management ◽  
Renewable Energy Sources ◽  
Critical Role ◽  
Winter Season ◽  
Learning Approach ◽  
Solar Pv ◽  
Optimal Energy ◽  
Optimal Energy Management

In the near future, microgrids will become more prevalent as they play a critical role in integrating distributed renewable energy resources into the main grid. Nevertheless, renewable energy sources, such as solar and wind energy can be extremely volatile as they are weather dependent. These resources coupled with demand can lead to random variations on both the generation and load sides, thus complicating optimal energy management. In this article, a reinforcement learning approach has been proposed to deal with this non-stationary scenario, in which the energy management system (EMS) is modelled as a Markov decision process (MDP). A novel modification of the control problem has been presented that improves the use of energy stored in the battery such that the dynamic demand is not subjected to future high grid tariffs. A comprehensive reward function has also been developed which decreases infeasible action explorations thus improving the performance of the data-driven technique. A Q-learning algorithm is then proposed to minimize the operational cost of the microgrid under unknown future information. To assess the performance of the proposed EMS, a comparison study between a trading EMS model and a non-trading case is performed using a typical commercial load curve and PV profile over a 24-h horizon. Numerical simulation results indicate that the agent learns to select an optimized energy schedule that minimizes energy cost (cost of power purchased from the utility and battery wear cost) in all the studied cases. However, comparing the non-trading EMS to the trading EMS model operational costs, the latter one was found to decrease costs by 4.033% in summer season and 2.199% in winter season.

Assessing policies to mitigate abandonment of shade-grown coffee production in forest systems amid low and uncertain prices

2020 ◽  
pp. 1-26
Author(s):  
Heidi J. Albers ◽  
Stephanie Brockmann ◽  
Beatriz Ávalos-Sartorio
Keyword(s):  
Ecosystem Services ◽  
Low Cost ◽  
Cost Effective ◽  
Biodiversity Hotspot ◽  
Payments For Ecosystem Services ◽  
Natural Forests ◽  
Coffee Production ◽  
Certification Programs ◽  
Coffee Farmers ◽  
Effective Policy

Abstract Low and highly variable prices plague the coffee market, generating concerns that coffee farmers producing in shade systems under natural forests, as in biodiversity hotspot Oaxaca, Mexico, will abandon production and contribute to deforestation and reduced ecosystem services. Using stakeholder information, we build a setting-informed model to analyze farmers' decisions to abandon shade-grown coffee production and their reactions to policy to reduce abandonment. Exploring price premiums for bird-friendly certified coffee, payments for ecosystem services, and price floors as policies, we find that once a farmer is on the path toward abandonment, it is difficult to reverse. However, implementing policies early that are low cost to farmers – price floors and no-cost certification programs – can stem abandonment. Considering the abandonment that policy avoids per dollar spent, price floors are the most cost-effective policy, yet governments prefer certification programs that push costs onto international coffee consumers who pay the price premium.

scholarly journals Cyber-Physical Systems Improving Building Energy Management: Digital Twin and Artificial Intelligence

Energies ◽  
2021 ◽  
Vol 14 (8) ◽  
pp. 2338
Author(s):  
Sofia Agostinelli ◽  
Fabrizio Cumo ◽  
Giambattista Guidi ◽  
Claudio Tomazzoli
Keyword(s):  
Artificial Intelligence ◽  
Renewable Energy ◽  
Energy Management ◽  
Three Dimensional ◽  
Cost Effective ◽  
Automation System ◽  
Climate Conditions ◽  
Digital Twin ◽  
Building Energy Management ◽  
Computing Paradigm

The research explores the potential of digital-twin-based methods and approaches aimed at achieving an intelligent optimization and automation system for energy management of a residential district through the use of three-dimensional data model integrated with Internet of Things, artificial intelligence and machine learning. The case study is focused on Rinascimento III in Rome, an area consisting of 16 eight-floor buildings with 216 apartment units powered by 70% of self-renewable energy. The combined use of integrated dynamic analysis algorithms has allowed the evaluation of different scenarios of energy efficiency intervention aimed at achieving a virtuous energy management of the complex, keeping the actual internal comfort and climate conditions. Meanwhile, the objective is also to plan and deploy a cost-effective IT (information technology) infrastructure able to provide reliable data using edge-computing paradigm. Therefore, the developed methodology led to the evaluation of the effectiveness and efficiency of integrative systems for renewable energy production from solar energy necessary to raise the threshold of self-produced energy, meeting the nZEB (near zero energy buildings) requirements.

The path to a 2025 nuclear-free Taiwan: An analysis of dynamic competition among emissions, energy, and economy

2020 ◽  
pp. 0958305X2094998
Author(s):  
Chun Chih Chen
Keyword(s):  
Renewable Energy ◽  
Energy Consumption ◽  
Nuclear Energy ◽  
Fossil Fuel ◽  
Percentage Error ◽  
Kuznets Curve ◽  
Energy Mix ◽  
Improve Energy Efficiency ◽  
Relationship Analysis ◽  
The Government

Taiwan intends to be nuclear free by 2025. This study employs the Lotka–Volterra competition model for sustainable development to analyze the emissions–energy–economy (3Es) issue to make appropriate policy suggestions for a nuclear-free transition. It also offers a new approach to naming the 3E relationship. The literature review shows that the environmental Kuznets curve accompanies the feedback and conservation hypotheses. In the 3E dynamics relationship analysis, the model shows a good mean absolute percentage error (<15%) for the model estimation. The key findings are as follows: 1) the fossil fuel-led economy exists; 2) CO2 emissions are reduced with nuclear energy consumption; 3) renewable energy is far from scale; 4) a complementary effect exists between fossil fuel and nuclear energy consumption; and 5) gas retrofitting and phasing out of nuclear seem imminent. In the energy transition, Taiwan drastically cuts nuclear energy without considering energy diversity due to which troubles might ensue. The priority issue for Taiwan’s energy mix is energy security. To deal with these concerns, this study suggests the government could improve energy efficiency, build a smart grid, develop carbon capture and storage, and reconsider putting nuclear energy back into the energy mix before renewable energy is scaled.

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